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Competitor PR Finder · varnan-tech | DecimalAI

Marketingby varnan-tech

Competitor PR Finder

Give it your product URL or description. It finds your top 5 competitors, runs three-track PR research across all of them (editorial, podcasts, communities), identifies which channels appear most frequently, looks up the journalist or host for each, and returns a tiered outreach list with story angles and ready-to-send cold pitch drafts tailored to your product.

Maintained

SkillScore

SkillScore36/100

Test results 36

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

27%36%of test cases passed +9 pts

Without skill6 of 22

With skill8 of 22

4 fixed4 passed either way12 still failing2 broke

Measured on gemini-3.6-flash8/18/2026v117 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskDuring the targeted search for reporters covering a Tier 1 channel, search snippets confirm coverage of competitors but omit any specific writer or editor names. What string should be recorded for the journalist name?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add varnan-tech-competitor-pr-finder
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install varnan-tech-competitor-pr-finder

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/varnan-tech-competitor-pr-finder/SKILL.md https://app.decimal.ai/s/varnan-tech-competitor-pr-finder/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/varnan-tech-competitor-pr-finder/SKILL.md

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add varnan-tech-competitor-pr-finder

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("varnan-tech-competitor-pr-finder")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/varnan-tech-competitor-pr-finder/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add varnan-tech-competitor-pr-finder

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/varnan-tech-competitor-pr-finder/SKILL.md https://app.decimal.ai/s/varnan-tech-competitor-pr-finder/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-competitor-pr-finder/SKILL.md

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/varnan-tech-competitor-pr-finder/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/varnan-tech-competitor-pr-finder.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/18/2026
Source: varnan-tech/opendirectory ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Competitor PR Finder

Give it your product URL. It finds your competitors, researches every PR channel they used (news, podcasts, communities), surfaces the channels that appear across multiple competitors (your proven targets), finds the journalist or host for each, and drafts a personalized cold pitch for your product at every tier-1 channel.

Zero-hallucination policy: Every channel, journalist name, story angle, and pitch detail in the output must trace to a specific Tavily search result or the fetched product page. This applies to:

Competitor names: must appear in Tavily search results, not AI training knowledge
Channel names: must have a URL in the search results
Journalist/host names: must appear verbatim in a Tavily snippet
Story angles: extracted from article/episode titles in search results only
Pitch drafts: reference specific evidence from search data + product analysis

Common Mistakes

The agent will want to...	Why that's wrong
Name a journalist from training knowledge	Every journalist name must trace to a search result snippet. Writing "Sarah Perez covers startups at TechCrunch" from memory is hallucination.
List channels without evidence URLs	Every channel in the output must have at least one URL from the PR search results proving a competitor was featured there.
Skip the competitor confirmation step	Always show discovered competitors and wait for the user to confirm. Wrong competitors = wasted searches and a useless output.
Generate generic pitches ("We'd love to be featured")	Every pitch must reference a specific angle from the evidence AND a specific differentiator from the product analysis.
Mark a channel as Tier 1 with only 1 competitor occurrence	Tier 1 = 3+ competitors. Tier 2 = exactly 2. Tier 3 = 1. Do not promote channels that haven't proven themselves.
Use em dashes in output	Replace all em dashes (--) with hyphens.

Read Reference Files Before Each Run

cat references/pr-channel-types.md
cat references/pitch-guide.md
cat references/tier-scoring.md

Step 1: Setup Check

echo "TAVILY_API_KEY:    ${TAVILY_API_KEY:+set}${TAVILY_API_KEY:-NOT SET -- required}"
echo "FIRECRAWL_API_KEY: ${FIRECRAWL_API_KEY:+set}${FIRECRAWL_API_KEY:-not set, Tavily extract will be used as fallback}"

If TAVILY_API_KEY is missing: Stop immediately. Tell the user: "TAVILY_API_KEY is required to research competitors and find PR coverage. There is no fallback. Get it at app.tavily.com -- free tier: 1000 credits/month (about 43 full runs at ~23 searches/run). Add it to your .env file."

If only FIRECRAWL_API_KEY is missing: Continue. Tavily extract will be used for the URL fetch.

Step 2: Parse Input

Collect from the conversation:

product_url: the URL to fetch (required, unless user pastes a description directly)
product_name: optional, derived from page if not provided
geography: optional -- US / Europe / global. Default: US

If the user provides only a pasted description (no URL): Skip Steps 3 and 4. Go directly to Step 4 (product analysis) using the pasted text as product_content. Set page_source to user_description and note in data_quality_flags.

If neither URL nor description: Ask: "What is the URL of your product or startup? Or paste a short description: what it does, who it is for, and what makes it different from competitors."

Derive product slug:

PRODUCT_SLUG=$(python3 -c "
from urllib.parse import urlparse
import sys
url = 'URL_HERE'
if url.startswith('http'):
    host = urlparse(url).netloc.replace('www.', '')
    print(host.split('.')[0])
else:
    import re
    print(re.sub(r'[^a-z0-9]', '-', url[:30].lower()).strip('-'))
")
echo "Product slug: $PRODUCT_SLUG"

Step 3: Fetch Product Page

Primary: Firecrawl (if FIRECRAWL_API_KEY is set)

curl -s -X POST https://api.firecrawl.dev/v1/scrape \
  -H "Authorization: Bearer $FIRECRAWL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "URL_HERE", "formats": ["markdown"], "onlyMainContent": true}' \
  | python3 -c "
import sys, json
d = json.load(sys.stdin)
content = d.get('data', {}).get('markdown', '') or d.get('markdown', '')
print(f'Fetched via Firecrawl: {len(content)} characters')
open('/tmp/cprf-product-raw.md', 'w').write(content)
"

Fallback: Tavily extract (if FIRECRAWL_API_KEY is not set)

curl -s -X POST https://api.tavily.com/extract \
  -H "Content-Type: application/json" \
  -d "{\"api_key\": \"$TAVILY_API_KEY\", \"urls\": [\"URL_HERE\"]}" \
  | python3 -c "
import sys, json
d = json.load(sys.stdin)
content = d.get('results', [{}])[0].get('raw_content', '')
print(f'Fetched via Tavily extract: {len(content)} characters')
open('/tmp/cprf-product-raw.md', 'w').write(content)
"

Checkpoint:

python3 -c "
content = open('/tmp/cprf-product-raw.md').read()
if len(content) < 200:
    print('ERROR: fewer than 200 characters fetched')
else:
    print(f'Content OK: {len(content)} characters')
"

If content < 200 characters: Stop fetching. Tell the user: "The product page returned no readable content -- the site is likely JavaScript-rendered and blocked the fetch. Please paste a short description directly: what it does, who it is for, and what makes it different."

Step 4: Product Analysis (AI)

Print page content:

python3 -c "
content = open('/tmp/cprf-product-raw.md').read()[:5000]
print('=== PRODUCT PAGE (first 5000 chars) ===')
print(content)
"

AI instructions: Analyze the product page above and extract:

product_name: the product or company name
one_line_description: what it does, for whom, core value prop. Under 20 words. No marketing language. Example: "CI/CD automation for developer teams that self-host their pipelines."
industry_taxonomy: l1 (top-level: e.g. developer tools / fintech / healthtech / consumer), l2 (sector: e.g. devops / payments / telemedicine), l3 (specific niche: e.g. CI/CD automation / embedded payments / async video consultation). Vague labels like "technology" alone are not acceptable.
differentiators: exactly 2-3 specific things that distinguish this product from generic competitors. These feed directly into the pitch drafts -- be specific. Example: ["Self-hosted pipeline runner -- no data leaves your infra", "Native support for monorepos with dynamic step generation"]
icp: buyer_persona (job title), company_type, company_size
geography_bias: US / Europe / global / unclear
page_source: "live_page" or "user_description"

Write to /tmp/cprf-product-analysis.json:

python3 << 'PYEOF'
import json

analysis = {
    # FILL from your analysis above
    "product_name": "",
    "one_line_description": "",
    "industry_taxonomy": {"l1": "", "l2": "", "l3": ""},
    "differentiators": [],
    "icp": {"buyer_persona": "", "company_type": "", "company_size": ""},
    "geography_bias": "US",
    "page_source": "live_page"
}

json.dump(analysis, open('/tmp/cprf-product-analysis.json', 'w'), indent=2)
print('Product analysis written.')
PYEOF

Verify:

python3 -c "
import json
a = json.load(open('/tmp/cprf-product-analysis.json'))
print('Product:', a['product_name'])
print('Industry:', a['industry_taxonomy']['l1'], '>', a['industry_taxonomy']['l2'], '>', a['industry_taxonomy']['l3'])
print('Differentiators:')
for d in a['differentiators']:
    print(f'  - {d}')
"

Step 4b: Phase 1 -- Competitor Discovery

ls scripts/research.py 2>/dev/null && echo "script found" || echo "ERROR: scripts/research.py not found -- cannot continue"

python3 scripts/research.py \
  --phase discover \
  --product-analysis /tmp/cprf-product-analysis.json \
  --tavily-key "$TAVILY_API_KEY" \
  --output /tmp/cprf-competitors-raw.json

Print results for AI review:

python3 -c "
import json
data = json.load(open('/tmp/cprf-competitors-raw.json'))
print(f'Searches run: {len(data[\"competitor_searches\"])}')
for s in data['competitor_searches']:
    print(f'\nQuery: {s[\"query\"]}')
    print(f'Answer: {s.get(\"answer\",\"\")[:400]}')
    for r in s.get('results', [])[:5]:
        print(f'  - {r[\"title\"]} | {r[\"url\"]}')
        print(f'    {r.get(\"content\",\"\")[:200]}')
"

AI instructions: Read the search results above. Pick exactly 5 competitor companies that:

Are named in the search result titles, answers, or snippets
Are in the same L3 niche as the product being analyzed
Are actual competing products (not agencies, consultancies, or list articles)
Are distinct from each other (not the same company under different names)

For each competitor write: name, url (from the search result where they appeared), description (one sentence from snippet), source_url (the search result URL where they were found).

Step 5: Competitor Confirmation

Show the discovered competitors to the user:

python3 << 'PYEOF'
import json

analysis = json.load(open('/tmp/cprf-product-analysis.json'))

# FILL: 5 competitors from the search results above
candidates = [
    # {"name": str, "url": str, "description": str, "source_url": str}
]

print(f"\nFound 5 competitors for {analysis['product_name']} in {analysis['industry_taxonomy']['l3']}:\n")
for i, c in enumerate(candidates, 1):
    print(f"  {i}. {c['name']} -- {c['description']}")
    print(f"     {c['url']}")

data = json.load(open('/tmp/cprf-competitors-raw.json'))
data['competitor_candidates'] = candidates
json.dump(data, open('/tmp/cprf-competitors-raw.json', 'w'), indent=2)
PYEOF

Tell the user: "These are the 5 competitors I'll research for PR coverage. Add, remove, or swap any -- or say 'looks good' to continue."

Wait for confirmation. If the user edits the list (adds/removes/swaps), update the candidates accordingly. Then write the confirmed list:

python3 << 'PYEOF'
import json

# FILL: confirmed competitor list (after user review)
confirmed = [
    # {"name": str, "url": str}
]

json.dump({"confirmed_competitors": confirmed}, open('/tmp/cprf-competitors-confirmed.json', 'w'), indent=2)
print(f"Confirmed {len(confirmed)} competitors for PR research.")
for c in confirmed:
    print(f"  - {c['name']} ({c['url']})")
PYEOF

Step 6: Three-Track PR Research (Phase 2)

python3 scripts/research.py \
  --phase pr-research \
  --competitors /tmp/cprf-competitors-confirmed.json \
  --product-analysis /tmp/cprf-product-analysis.json \
  --tavily-key "$TAVILY_API_KEY" \
  --output /tmp/cprf-pr-raw.json

This runs 3 searches per competitor (15 total):

Track A (Editorial): "[competitor]" featured press coverage TechCrunch Forbes Wired article interview
Track B (Podcasts): "[competitor]" founder CEO podcast interview appeared on episode
Track C (Communities): "[competitor]" site:reddit.com OR site:news.ycombinator.com OR site:producthunt.com

Print coverage summary:

python3 -c "
import json
data = json.load(open('/tmp/cprf-pr-raw.json'))
print(f'Competitors researched: {data[\"competitors_researched\"]}')
print()
for r in data['results']:
    print(f'{r[\"competitor\"]}:')
    for track, tdata in r['tracks'].items():
        n = len(tdata.get('results', []))
        print(f'  {track:12}: {n} results')
"

If all 3 tracks for a competitor return 0 results: This competitor has very low press coverage. Note in data_quality_flags and proceed -- the cross-competitor pattern will still work with the remaining 4.

Step 7: Pattern Analysis (AI)

Print all raw PR results:

python3 -c "
import json
data = json.load(open('/tmp/cprf-pr-raw.json'))
for r in data['results']:
    print(f'\n=== {r[\"competitor\"]} ===')
    for track, tdata in r['tracks'].items():
        print(f'\n--- Track {track.upper()} ---')
        print(f'Query: {tdata[\"query\"]}')
        print(f'Answer: {tdata.get(\"answer\",\"\")[:400]}')
        for item in tdata.get('results', [])[:5]:
            print(f'  Title: {item[\"title\"]}')
            print(f'  URL:   {item[\"url\"]}')
            print(f'  Snippet: {item.get(\"content\",\"\")[:200]}')
"

AI instructions: Read ALL search results above. Build a channel frequency map.

Step 1 -- Normalize URLs to root domain: https://techcrunch.com/2023/06/article-title → techcrunch.com. https://open.spotify.com/episode/... → identify as podcast (spotify episode). https://www.reddit.com/r/devops/ → reddit.com/r/devops.

Step 2 -- Count occurrences: How many different competitors appeared in results from each channel root? A channel that shows up in Competitor A's Track A AND Competitor B's Track A counts as frequency 2.

Step 3 -- Tier channels (follow references/tier-scoring.md):

Tier 1: appeared in 3+ competitors
Tier 2: appeared in exactly 2 competitors
Tier 3: appeared in 1 competitor

Step 4 -- Extract story angles from article/episode titles in the results. Classify each as: funding-announcement / product-launch / founder-story / trend-piece / category-creation / how-to / comparison / award. Do not infer -- only classify angles visible in the titles.

Step 5 -- Classify channel type for each: editorial / podcast / community / newsletter.

Write to /tmp/cprf-pr-patterns.json:

python3 << 'PYEOF'
import json

patterns = {
    "tier_1_channels": [
        # FILL -- channels appearing in 3+ competitors
        # Each: {"channel_name": str, "channel_url": str, "channel_type": str,
        #        "frequency": int, "found_in_competitors": [str],
        #        "evidence_urls": [str], "story_angles_used": [str],
        #        "journalist_name": "", "journalist_beat": ""}
    ],
    "tier_2_channels": [
        # FILL -- channels appearing in exactly 2 competitors
        # Each: {"channel_name": str, "channel_url": str, "channel_type": str,
        #        "frequency": 2, "found_in_competitors": [str], "evidence_urls": [str],
        #        "story_angles_used": [str]}
    ],
    "tier_3_channels": [
        # FILL -- channels appearing in only 1 competitor (name + URL only)
        # Each: {"channel_name": str, "channel_url": str, "found_in_competitor": str}
    ],
    "data_quality_flags": []
}

json.dump(patterns, open('/tmp/cprf-pr-patterns.json', 'w'), indent=2)
PYEOF

Verify:

python3 -c "
import json
p = json.load(open('/tmp/cprf-pr-patterns.json'))
print(f'Tier 1 channels: {len(p[\"tier_1_channels\"])}')
for ch in p['tier_1_channels']:
    print(f'  {ch[\"frequency\"]}x {ch[\"channel_name\"]} ({ch[\"channel_type\"]}) -- {ch[\"found_in_competitors\"]}')
print(f'Tier 2 channels: {len(p[\"tier_2_channels\"])}')
print(f'Tier 3 channels: {len(p[\"tier_3_channels\"])}')
"

If fewer than 3 Tier 1 channels: This is normal for niche markets. Promote the top Tier 2 channels (highest frequency) to get to at least 3 total channels with deep dives. Note the promotion in data_quality_flags.

Step 8: Journalist / Host Lookup

For each Tier 1 channel (up to 7), run one targeted Tavily search:

python3 << 'PYEOF'
import json, os, urllib.request

patterns = json.load(open('/tmp/cprf-pr-patterns.json'))
analysis = json.load(open('/tmp/cprf-product-analysis.json'))
l2 = analysis['industry_taxonomy']['l2']
l3 = analysis['industry_taxonomy']['l3']
tavily_key = os.environ.get('TAVILY_API_KEY', '')

lookup_results = []

for channel in patterns.get('tier_1_channels', [])[:7]:
    name = channel['channel_name']
    ctype = channel['channel_type']

    if ctype == 'editorial':
        query = f'"{name}" journalist reporter writer covers {l2} {l3} startups technology'
    elif ctype == 'podcast':
        query = f'"{name}" podcast host interviewer {l2} {l3} founders'
    else:
        query = f'"{name}" moderator community manager {l2} {l3}'

    payload = json.dumps({
        "api_key": tavily_key,
        "query": query,
        "search_depth": "basic",
        "max_results": 5
    }).encode()

    req = urllib.request.Request(
        'https://api.tavily.com/search',
        data=payload,
        headers={'Content-Type': 'application/json'},
        method='POST'
    )
    try:
        with urllib.request.urlopen(req, timeout=20) as resp:
            data = json.loads(resp.read())
            lookup_results.append({
                'channel': name,
                'channel_type': ctype,
                'query': query,
                'answer': data.get('answer', ''),
                'results': [
                    {'title': r['title'], 'url': r['url'], 'content': r.get('content', '')[:400]}
                    for r in data.get('results', [])[:3]
                ]
            })
            print(f'Journalist lookup -- {name}: {len(data.get("results", []))} results')
    except Exception as e:
        lookup_results.append({
            'channel': name, 'channel_type': ctype,
            'query': query, 'answer': '', 'results': [], 'error': str(e)
        })
        print(f'Journalist lookup -- {name}: FAILED ({e})')

json.dump(lookup_results, open('/tmp/cprf-journalist-results.json', 'w'), indent=2)
print(f'Journalist lookups complete: {len(lookup_results)} channels')
PYEOF

Print results for AI extraction:

python3 -c "
import json
results = json.load(open('/tmp/cprf-journalist-results.json'))
for r in results:
    print(f'\n=== {r[\"channel\"]} ({r[\"channel_type\"]}) ===')
    print(f'Answer: {r.get(\"answer\",\"\")[:400]}')
    for item in r.get('results', []):
        print(f'  {item[\"title\"]}')
        print(f'  {item.get(\"content\",\"\")[:300]}')
"

AI instructions: For each Tier 1 channel, extract from the search results above:

journalist_name: the person's name verbatim from a snippet. Write "not found in search data" if absent -- do NOT fill from training knowledge.
journalist_beat: what topics they cover, extracted from snippet text. Write "not found in search data" if absent.

Update /tmp/cprf-pr-patterns.json with journalist_name and journalist_beat populated for each Tier 1 channel:

python3 << 'PYEOF'
import json

patterns = json.load(open('/tmp/cprf-pr-patterns.json'))

# FILL: update journalist_name and journalist_beat for each tier_1 channel
# journalist_name and journalist_beat come from search snippet text only
# Write "not found in search data" if the snippets don't name a person

# Example:
# patterns['tier_1_channels'][0]['journalist_name'] = 'Ingrid Lunden'
# patterns['tier_1_channels'][0]['journalist_beat'] = 'enterprise software and developer tools'

json.dump(patterns, open('/tmp/cprf-pr-patterns.json', 'w'), indent=2)
print('Journalist data updated.')
for ch in patterns['tier_1_channels']:
    print(f"  {ch['channel_name']}: {ch.get('journalist_name','--')} | {ch.get('journalist_beat','--')}")
PYEOF

Step 9: Synthesis -- Generate Outreach Packages (AI)

Print consolidated data:

python3 -c "
import json

analysis = json.load(open('/tmp/cprf-product-analysis.json'))
patterns = json.load(open('/tmp/cprf-pr-patterns.json'))

print('=== PRODUCT ===')
print(f'Name: {analysis[\"product_name\"]}')
print(f'What it does: {analysis[\"one_line_description\"]}')
print(f'Differentiators:')
for d in analysis['differentiators']:
    print(f'  - {d}')
print(f'ICP: {analysis[\"icp\"]}')
print(f'Geography: {analysis[\"geography_bias\"]}')
print()
print('=== TIER 1 CHANNELS ===')
for ch in patterns['tier_1_channels']:
    print(f'\n{ch[\"channel_name\"]} ({ch[\"channel_type\"]}, freq={ch[\"frequency\"]})')
    print(f'  Found in: {ch[\"found_in_competitors\"]}')
    print(f'  Evidence URLs: {ch[\"evidence_urls\"][:3]}')
    print(f'  Story angles: {ch[\"story_angles_used\"]}')
    print(f'  Journalist: {ch.get(\"journalist_name\",\"not found\")} | {ch.get(\"journalist_beat\",\"\")}')
print()
print('=== TIER 2 CHANNELS ===')
for ch in patterns['tier_2_channels']:
    print(f'  {ch[\"channel_name\"]} ({ch[\"channel_type\"]}) -- found in {ch[\"found_in_competitors\"]}')
"

AI instructions -- zero-hallucination rules:

Channel names: Only include channels from /tmp/cprf-pr-patterns.json. No invented channels.
Journalist/host names: Use only what was populated in Step 8. Write "not found in search data" if blank. Do NOT substitute from training knowledge.
Story angles: Use only angles extracted from article/episode titles in the search results. Do not infer from training knowledge.
Cold pitch drafts: Must reference (a) a specific story angle from the evidence, (b) at least one specific differentiator from the product analysis, (c) the journalist's beat if found. No generic "we'd love to be featured" or "our product is revolutionary" language.
Channel overview: 1-2 sentences from search snippets only. Write "not found in search data" if the snippets don't describe the channel's coverage focus.
Bonus hooks: 3 angles that your competitors did NOT use in their coverage. These must be grounded in the product's actual differentiators from Step 4 -- not generic advice.
No em dashes. No banned words (powerful, seamless, game-changing, revolutionary, cutting-edge, leverage, transform).

Per Tier 1 channel generate:

channel_overview: 1-2 sentences about coverage focus (from snippets)
why_they_covered_competitors: specific angle extracted from evidence titles
journalist_name + journalist_beat
approach_method: cold email / podcast pitch form / community post / LinkedIn DM (based on channel type)
cold_pitch_draft:
- subject: "[Journalist name]: [their beat] + [your specific angle]"
- body: 3-4 sentences. Structure: hook (reference their past coverage of a competitor) + what you do (one sentence) + why it fits their beat (tie to a specific differentiator) + ask (clear, low-friction CTA)

Also generate bonus_hooks: 3 pitch angles not used by any competitor in the search results. Base each on a specific product differentiator.

Write to /tmp/cprf-final.json:

python3 << 'PYEOF'
import json

result = {
    "product_summary": {
        # FILL from analysis
    },
    "competitors_researched": [],  # FILL: names of confirmed competitors
    "tier_1_deep_dives": [
        # FILL per tier 1 channel:
        # {
        #   "channel_name": str,
        #   "channel_type": str,  # editorial / podcast / community
        #   "frequency": int,
        #   "found_in_competitors": [str],
        #   "evidence_urls": [str],
        #   "channel_overview": str,
        #   "why_they_covered_competitors": str,
        #   "story_angles_used": [str],
        #   "journalist_name": str,
        #   "journalist_beat": str,
        #   "approach_method": str,
        #   "cold_pitch_draft": {"subject": str, "body": str}
        # }
    ],
    "tier_2_channels": [
        # FILL: {channel_name, channel_type, frequency, found_in_competitors, evidence_urls}
    ],
    "tier_3_channels": [
        # FILL: {channel_name, found_in_competitor}
    ],
    "bonus_hooks": [
        # FILL: 3 strings -- pitch angles not used by competitors
    ],
    "data_quality_flags": []
}

json.dump(result, open('/tmp/cprf-final.json', 'w'), indent=2)
print(f'Synthesis written.')
print(f'Tier 1 deep dives: {len(result.get("tier_1_deep_dives", []))}')
print(f'Bonus hooks: {len(result.get("bonus_hooks", []))}')
PYEOF

Step 10: Self-QA, Present, and Save

Self-QA:

python3 << 'PYEOF'
import json

result = json.load(open('/tmp/cprf-final.json'))
failures = []

# Check 1: em dashes
full_text = json.dumps(result)
if '—' in full_text:
    result = json.loads(full_text.replace('—', '-'))
    failures.append('Fixed: em dashes replaced with hyphens')

# Check 2: banned words
banned = ['powerful', 'seamless', 'innovative', 'game-changing', 'revolutionize',
          'excited to announce', 'cutting-edge', 'best-in-class', 'world-class',
          'leverage', 'transform', 'disrupt']
for word in banned:
    if word.lower() in json.dumps(result).lower():
        failures.append(f'Warning: banned word "{word}" found in output -- review before presenting')

# Check 3: cold pitch subjects exist
for dd in result.get('tier_1_deep_dives', []):
    pitch = dd.get('cold_pitch_draft', {})
    if not pitch.get('subject') or len(pitch.get('subject', '')) < 10:
        dd['cold_pitch_draft']['subject'] = 'not generated'
        failures.append(f'Fixed: missing subject line for {dd.get("channel_name")}')
    if not pitch.get('body') or len(pitch.get('body', '')) < 50:
        failures.append(f'Warning: very short pitch body for {dd.get("channel_name")}')

# Check 4: bonus hooks count
if len(result.get('bonus_hooks', [])) != 3:
    failures.append(f'Expected 3 bonus hooks, got {len(result.get("bonus_hooks", []))}')

# Check 5: "not found in search data" count
nf_count = json.dumps(result).count('not found in search data')
if nf_count > 0:
    failures.append(f'INFO: {nf_count} field(s) marked "not found in search data" -- verify before outreach')

# Check 6: tier 1 channels have evidence URLs
for ch in result.get('tier_1_deep_dives', []):
    if not ch.get('evidence_urls'):
        failures.append(f'Warning: {ch["channel_name"]} has no evidence_urls')

if 'data_quality_flags' not in result:
    result['data_quality_flags'] = []
result['data_quality_flags'].extend(failures)

json.dump(result, open('/tmp/cprf-final.json', 'w'), indent=2)
print(f'QA complete. {len(failures)} issues addressed.')
for f in failures:
    print(f'  - {f}')
if not failures:
    print('All QA checks passed.')
PYEOF

Present the output:

## PR Intel: [product_name]
Date: [today] | Competitors researched: [N] | Tier 1 channels: [N] | Tier 2 channels: [N]

---

### Your Product
[one_line_description]
Differentiators: [list]
Competitors researched: [names]

---

### Tier 1 Channels (Proven Beats -- Found in 3+ Competitors)

*These channels have already covered multiple companies in your space.*

| Channel | Type | Found in | Journalist/Host | Approach |
|---|---|---|---|---|
[one row per tier 1 channel]

---

### Deep Dives + Cold Pitches

#### 1. [Channel Name] (Tier 1 -- [Type], found in [N] competitors)

Covers: [channel_overview]
Covered competitors: [found_in_competitors with evidence URLs]
Story angle they used: [why_they_covered_competitors]
Journalist/Host: [journalist_name] | Beat: [journalist_beat]
How to reach: [approach_method]

**Cold pitch:**
Subject: [subject]

[body -- 3-4 sentences]

---

[repeat for each tier 1 channel]

---

### Tier 2 Channels (Warm -- Found in 2 Competitors)

| Channel | Type | Found in | URL |
|---|---|---|---|
[one row per tier 2 channel]

---

### Tier 3 Channels (Discovery -- Found in 1 Competitor)

[comma-separated list of channel names with URLs]

---

### 3 Bonus Hooks (Angles Your Competitors Didn't Use)

1. [hook_text]
2. [hook_text]
3. [hook_text]

---
Data notes: [data_quality_flags, or "None"]
Saved to: docs/pr-intel/[PRODUCT_SLUG]-[DATE].md

Save to file and clean up:

DATE=$(date +%Y-%m-%d)
OUTPUT_FILE="docs/pr-intel/${PRODUCT_SLUG}-${DATE}.md"
mkdir -p docs/pr-intel
echo "Saved to: $OUTPUT_FILE"

rm -f /tmp/cprf-product-raw.md /tmp/cprf-product-analysis.json \
      /tmp/cprf-competitors-raw.json /tmp/cprf-competitors-confirmed.json \
      /tmp/cprf-pr-raw.json /tmp/cprf-pr-patterns.json \
      /tmp/cprf-journalist-results.json /tmp/cprf-final.json
echo "Temp files cleaned up."

When it activates

Automatically, when a task matches. Use when asked to find PR opportunities, discover where competitors got featured, build a media outreach list, find which journalists c

About this skill

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✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

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Source

Imported

varnan-tech / opendirectory

skills/competitor-pr-finder/SKILL.md

View on GitHub ↗

License MITImported 10/17/2026Version v1Bundled 4 files · scripts

Illustrative Want to see what varnan-tech/competitor-pr-finder would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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Use this skill

Competitor PR Finder

Common Mistakes

Read Reference Files Before Each Run

Step 1: Setup Check

Step 2: Parse Input

Step 3: Fetch Product Page

Step 4: Product Analysis (AI)

Step 4b: Phase 1 -- Competitor Discovery

Step 5: Competitor Confirmation

Step 6: Three-Track PR Research (Phase 2)

Step 7: Pattern Analysis (AI)

Step 8: Journalist / Host Lookup

Step 9: Synthesis -- Generate Outreach Packages (AI)

Step 10: Self-QA, Present, and Save

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