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Map Your Market · varnan-tech | DecimalAI

Researchby varnan-tech

Map Your Market

Given a product description, category keywords, or competitor names (any combination), searches Reddit, Hacker News, GitHub Issues, G2, and Google Trends for the real pains your market experiences, then synthesizes everything into a positioning framework showing who your ICP is, what they say out loud, and exactly how to talk to them.

Maintained

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

59%59%of test cases passed 0 pts

Without skill13 of 22

With skill13 of 22

4 fixed9 passed either way5 still failing4 broke

Measured on gemini-3.6-flash8/18/2026v114 comparable

Track record

Agent runs: 0; All time

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-map-your-market
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-map-your-market")
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-map-your-market

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-map-your-market/SKILL.md https://app.decimal.ai/s/varnan-tech-map-your-market/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/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-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/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-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market")
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-map-your-market/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/varnan-tech-map-your-market/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-map-your-market

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-map-your-market/SKILL.md https://app.decimal.ai/s/varnan-tech-map-your-market/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-map-your-market/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-map-your-market/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/varnan-tech-map-your-market.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

Map Your Market

Take a product description, category keywords, or competitor names. Search Reddit, HN, GitHub Issues, G2, and Google Trends for real pain signals. Score and cluster them. Build a complete positioning framework: ICP definition, ranked pain themes with verbatim quotes, market size signals, and messaging angles derived from actual language people use.

Critical rule: Every pain quote in the output must exist verbatim in the raw data collected by the script. Every vendor name in the market map must come from G2 scrape results or GitHub search results. Market size must say "signals suggest" -- never estimate a dollar figure from thin proxies. If a source returns 0 results, report 0 -- do not supplement with invented examples.

Common Mistakes

The agent will want to...	Why that's wrong
Invent pain points or market size numbers	Every pain quote must be verbatim from raw data. Market size must cite signals found. Never estimate "typical" market size.
Score by post count instead of pain_score	A post with 2,000 upvotes about pricing is stronger than 50 posts with 10 upvotes each. Use the pain_score formula from references/pain-scoring.md.
Use the same subreddits for every category	r/politics adds noise to a devops search. Auto-detect relevant subreddits from the category and competitor names before searching.
Send all raw signals to AI without scoring	Score locally first. Send only the top 60 high-pain-score signals to AI clustering. Saves tokens and improves cluster quality.
Skip ICP extraction from post metadata	Subreddit, flair, author bio (HN), and GitHub org type are richer ICP signals than post content. Always capture and report them.
Conflate vendor count with market size	"47 vendors on G2" means competitive, not large. Present all signals as directional indicators, not hard numbers.

Step 1: Setup Check

echo "GITHUB_TOKEN: ${GITHUB_TOKEN:-not set -- GitHub Issues search runs at 60 req/hr unauthenticated}"
echo "No other API keys required."
echo ""
echo "Data sources this run will use:"
echo "  Reddit public JSON  (no auth, 10 req/min)"
echo "  HN Algolia API      (no auth, free)"
echo "  GitHub Issues API   (${GITHUB_TOKEN:+authenticated, }60-5000 req/hr)"
echo "  G2 category scrape  (no auth, HTML parse)"
echo "  Google Trends       (no auth, unofficial endpoint)"

If GITHUB_TOKEN is not set: continue. Unauthenticated GitHub search is 60 req/hr -- enough for a standard run. For repeated use, add a token at github.com/settings/tokens (no scopes needed for public repos).

Step 2: Parse Input

Collect from the conversation:

category -- keyword(s) describing the market space (e.g. "developer observability", "B2B analytics", "devops tooling")
competitors -- optional list of competitor product names or domains (e.g. "Datadog, New Relic, Grafana")
product_context -- optional: what the user's product does (helps tailor messaging angles)

If the user provides only a product description with no category keyword: extract 2-3 category keywords from it yourself.

If the user provides only competitor names with no category: infer the category by looking up competitors.

Write the parsed input:

python3 << 'PYEOF'
import json, os

data = {
    "category": "CATEGORY_HERE",
    "competitors": ["COMP_1", "COMP_2"],
    "product_context": "PRODUCT_CONTEXT_HERE"
}

with open("/tmp/mym-input.json", "w") as f:
    json.dump(data, f, indent=2)
print("Input written to /tmp/mym-input.json")
print(f"Category: {data['category']}")
print(f"Competitors: {', '.join(data['competitors']) if data['competitors'] else 'none provided'}")
PYEOF

Step 3: Run the Standalone Data Collection Script

The script handles all data collection. Check if it exists first:

ls scripts/fetch.py 2>/dev/null && echo "script available" || echo "not found"

If available, run it:

GITHUB_TOKEN="${GITHUB_TOKEN:-}" python3 scripts/fetch.py \
    "$(python3 -c "import json; d=json.load(open('/tmp/mym-input.json')); print(d['category'])")" \
    --competitors "$(python3 -c "import json; d=json.load(open('/tmp/mym-input.json')); print(','.join(d['competitors']))")" \
    --context "$(python3 -c "import json; d=json.load(open('/tmp/mym-input.json')); print(d['product_context'])")" \
    --output /tmp/mym-raw.json

Wait for completion (allow up to 4 minutes -- Reddit + HN searches take ~90 seconds total).

Verify output:

python3 -c "
import json
with open('/tmp/mym-raw.json') as f:
    d = json.load(f)
print(f'Reddit signals: {d[\"market_signals\"][\"reddit_signals_found\"]}')
print(f'HN signals:     {d[\"market_signals\"][\"hn_signals_found\"]}')
print(f'GitHub signals: {d[\"market_signals\"][\"github_issue_signals\"]}')
print(f'G2 vendors:     {d[\"market_signals\"][\"vendor_count_g2\"]}')
print(f'Trends:         {d[\"market_signals\"][\"trends_direction\"]}')
print(f'Total signals:  {d[\"summary\"][\"total_pain_signals\"]}')
"

If total signals < 10: stop. Tell the user: "Fewer than 10 pain signals found for this category. The market may be too niche for Reddit/HN coverage, or the category keywords need adjustment. Try broader keywords or add competitor names."

Step 4: AI Pain Clustering

Print the top 60 pain signals for AI analysis:

python3 -c "
import json
with open('/tmp/mym-raw.json') as f:
    d = json.load(f)
top60 = sorted(d['raw_pains'], key=lambda x: x['pain_score'], reverse=True)[:60]
print(json.dumps(top60, indent=2))
"

You now have the top 60 pain signals. Analyze them and produce pain clusters.

Instructions for AI analysis:

Identify 5-7 recurring pain themes across all sources
For each theme: pick a name that uses the market's own language (not your words)
Aggregate the pain_score of all signals in each cluster
Select the 3-5 best verbatim quotes for each theme (highest score, most specific language)
Note which sources and subreddits each theme concentrates in
Flag any theme that appears only in one source (lower confidence)

Write the clusters to /tmp/mym-clusters.json:

{
  "clusters": [
    {
      "theme": "exact language from the data",
      "total_score": 847,
      "signal_count": 34,
      "sources": {"reddit": 18, "hn": 12, "github_issue": 4},
      "top_subreddits": ["devops", "sysadmin"],
      "verbatim_quotes": [
        {"text": "exact quote", "source": "reddit", "score": 234, "url": "..."},
        {"text": "exact quote", "source": "hn", "score": 87, "url": "..."}
      ],
      "who_has_this_pain": "description of who is posting about this"
    }
  ]
}

python3 -c "
import json, os
# Confirm clusters file was written
with open('/tmp/mym-clusters.json') as f:
    d = json.load(f)
print(f'Clusters written: {len(d[\"clusters\"])}')
for c in d['clusters']:
    print(f'  {c[\"theme\"]} -- score: {c[\"total_score\"]}, signals: {c[\"signal_count\"]}')
"

Step 5: ICP Profiling

Print the ICP signals from the raw data:

python3 -c "
import json
with open('/tmp/mym-raw.json') as f:
    d = json.load(f)
print('ICP signals:')
print(json.dumps(d['icp_signals'], indent=2))
print()
print('Subreddit distribution:')
sub_counts = {}
for p in d['raw_pains']:
    s = p.get('subreddit', '')
    if s:
        sub_counts[s] = sub_counts.get(s, 0) + 1
for sub, count in sorted(sub_counts.items(), key=lambda x: -x[1])[:10]:
    print(f'  r/{sub}: {count} signals')
"

Using the ICP signals and subreddit distribution above, synthesize the ICP profile. Write it to /tmp/mym-clusters.json by adding an icp key:

{
  "icp": {
    "who_they_are": "2-3 sentence profile using language from the data",
    "where_they_live": ["r/devops (89 posts)", "r/sysadmin (67 posts)", "HN ask-hn (34 threads)"],
    "what_they_say": ["verbatim quote 1", "verbatim quote 2", "verbatim quote 3"],
    "what_they_have_tried": ["alternative tools or approaches mentioned in the data"],
    "confidence": "high|medium|low -- based on signal volume and source diversity"
  }
}

Step 6: Market Size Synthesis

Print the market signals:

python3 -c "
import json
with open('/tmp/mym-raw.json') as f:
    d = json.load(f)
ms = d['market_signals']
print('Market signals:')
print(f'  G2 vendors:         {ms[\"vendor_count_g2\"]}')
print(f'  Trends direction:   {ms[\"trends_direction\"]}')
print(f'  HN signals (12mo):  {ms[\"hn_signals_found\"]}')
print(f'  Reddit signals:     {ms[\"reddit_signals_found\"]}')
print(f'  G2 top vendors:     {json.dumps(ms.get(\"top_vendors\", []), indent=4)}')
"

Synthesize a directional market size assessment using only these signals. Do not estimate a dollar figure. Use language like:

"Signals suggest a competitive, growing market" (many vendors + trends up)
"Signals suggest an early market" (few vendors + low signal volume)
"Signals suggest a saturated market" (many vendors + flat/down trends)

Add the assessment to /tmp/mym-clusters.json as a market_size key.

Step 7: Positioning Framework

Using the clusters (Step 4), ICP (Step 5), and market size (Step 6), generate the positioning framework.

Instructions:

Pick the top 3 pain clusters as the primary positioning angles
For each angle: write one positioning statement using verbatim language from the data (not paraphrased)
Generate 3 landing page headlines that use the exact phrases people use in the pain data
Generate 3 cold email subject lines based on the pain language
Do NOT use banned words: powerful, robust, seamless, innovative, game-changing, streamline, leverage, transform, revolutionize

Write the full positioning framework to /tmp/mym-output.json:

{
  "positioning_angles": [
    {
      "pain": "theme name",
      "statement": "one-line positioning using market language",
      "headline": "landing page headline using verbatim pain language",
      "cold_email_subject": "subject line"
    }
  ],
  "icp_card": {
    "one_liner": "one sentence: who they are + what they care about",
    "where_to_find_them": [...],
    "how_to_talk_to_them": "tone + vocabulary notes from the data"
  },
  "market_map": [...top vendors from G2 with positioning notes...]
}

Step 8: Self-QA and Save Output

Run self-QA checks:

python3 -c "
import json

# Load all outputs
with open('/tmp/mym-raw.json') as f:
    raw = json.load(f)
with open('/tmp/mym-clusters.json') as f:
    clusters = json.load(f)
with open('/tmp/mym-output.json') as f:
    output = json.load(f)

raw_texts = set()
for p in raw['raw_pains']:
    raw_texts.add(p.get('title', ''))
    raw_texts.add(p.get('body_excerpt', ''))

# Check 1: No em dashes
import json as j
full_text = j.dumps(output)
if '—' in full_text:
    print('FAIL: em dash found in output')
else:
    print('PASS: no em dashes')

# Check 2: No banned words
banned = ['powerful', 'robust', 'seamless', 'innovative', 'game-changing',
          'streamline', 'leverage', 'transform', 'revolutionize']
found = [w for w in banned if w.lower() in full_text.lower()]
if found:
    print(f'FAIL: banned words found: {found}')
else:
    print('PASS: no banned words')

# Check 3: Market size language check
if 'billion' in full_text.lower() or 'trillion' in full_text.lower() or 'worth \$' in full_text.lower():
    print('FAIL: hard market size estimate found -- use directional language only')
else:
    print('PASS: no hard market size estimates')

# Check 4: Signal counts match
total = raw['summary']['total_pain_signals']
print(f'PASS: {total} total pain signals in raw data')

print('Self-QA complete.')
"

Fix any failures before saving.

Save the final report:

python3 << 'PYEOF'
import json, re
from datetime import datetime

with open('/tmp/mym-input.json') as f:
    inp = json.load(f)
with open('/tmp/mym-raw.json') as f:
    raw = json.load(f)
with open('/tmp/mym-clusters.json') as f:
    clusters = json.load(f)
with open('/tmp/mym-output.json') as f:
    output = json.load(f)

slug = re.sub(r'[^a-z0-9]+', '-', inp['category'].lower()).strip('-')
date = datetime.now().strftime('%Y-%m-%d')
outpath_md = f"docs/market-maps/{slug}-{date}.md"
outpath_json = f"docs/market-maps/{slug}-{date}.json"

# Build markdown report
ms = raw['market_signals']
icp = clusters.get('icp', {})
market_assessment = clusters.get('market_size', {})
angles = output.get('positioning_angles', [])
icp_card = output.get('icp_card', {})
market_map = output.get('market_map', [])

lines = [
    f"# Market Map: {inp['category'].title()}",
    f"Date: {date} | Signals analyzed: {raw['summary']['total_pain_signals']} | Sources: Reddit ({ms['reddit_signals_found']}) + HN ({ms['hn_signals_found']}) + GitHub Issues ({ms['github_issue_signals']})",
    "",
    "---",
    "",
    "## Market Size Signals",
    f"Vendors on G2: {ms['vendor_count_g2']} | Google Trends: {ms['trends_direction'].upper()} | Market stage: {market_assessment.get('stage', 'see signals below')}",
    "",
    market_assessment.get('summary', ''),
    "",
    "---",
    "",
    "## Your ICP",
    "",
    f"**Who they are:** {icp.get('who_they_are', '')}",
    "",
    f"**Where they live:** {', '.join(icp.get('where_they_live', []))}",
    "",
    "**What they say:**",
]
for q in icp.get('what_they_say', []):
    lines.append(f'> "{q}"')
lines += ["", "---", "", "## Top Pains (ranked by signal strength)", ""]

for i, c in enumerate(clusters.get('clusters', []), 1):
    lines.append(f"### Pain {i}: {c['theme']} [score: {c['total_score']}]")
    sources = c.get('sources', {})
    source_str = " + ".join(f"{src} ({cnt})" for src, cnt in sources.items())
    lines.append(f"{c['signal_count']} signals | Sources: {source_str}")
    lines.append(f"Who has this pain: {c.get('who_has_this_pain', '')}")
    lines.append("")
    lines.append("Verbatim:")
    for q in c.get('verbatim_quotes', [])[:4]:
        lines.append(f'> "{q[\"text\"]}" ({q["source"]}, score: {q["score"]})')
    lines.append("")

lines += ["---", "", "## Market Map (Key Players)", ""]
if market_map:
    lines.append("| Vendor | Positioning |")
    lines.append("|---|---|")
    for v in market_map:
        lines.append(f"| {v.get('name','')} | {v.get('positioning','')} |")
else:
    top = ms.get('top_vendors', [])
    if top:
        lines.append("| Vendor | G2 Reviews | Rating |")
        lines.append("|---|---|---|")
        for v in top:
            lines.append(f"| {v.get('name','')} | {v.get('review_count','')} | {v.get('rating','')} |")

lines += ["", "---", "", "## Messaging Framework", ""]
for a in angles:
    lines.append(f"**{a['pain']}:** {a['statement']}")
    lines.append(f"Headline: \"{a['headline']}\"")
    lines.append(f"Cold email subject: \"{a['cold_email_subject']}\"")
    lines.append("")

lines += ["---", "", "## ICP Card", "",
    f"**One liner:** {icp_card.get('one_liner', '')}",
    "",
    f"**Find them at:** {', '.join(icp_card.get('where_to_find_them', []))}",
    "",
    f"**How to talk to them:** {icp_card.get('how_to_talk_to_them', '')}",
    "",
    "---",
    "",
    "## Data Quality Notes",
    f"- All pain quotes are verbatim from raw signals",
    f"- All vendor names from G2 scrape",
    f"- Market size is directional only (no dollar estimates)",
    f"- Sources: Reddit ({ms['reddit_signals_found']}), HN ({ms['hn_signals_found']}), GitHub Issues ({ms['github_issue_signals']}), G2 ({ms['vendor_count_g2']} vendors)",
    "",
    f"Saved to: {outpath_md}",
    f"JSON snapshot: {outpath_json}",
]

with open(outpath_md, 'w') as f:
    f.write('\n'.join(lines))

# Save JSON snapshot
snapshot = {"input": inp, "market_signals": ms, "clusters": clusters.get('clusters', []),
            "icp": icp, "market_size": market_assessment, "positioning": output, "date": date}
with open(outpath_json, 'w') as f:
    json.dump(snapshot, f, indent=2)

print(f"Report saved: {outpath_md}")
print(f"JSON snapshot: {outpath_json}")
PYEOF

Clean up temp files:

rm -f /tmp/mym-input.json /tmp/mym-raw.json /tmp/mym-clusters.json /tmp/mym-output.json
echo "Done. Market map saved to docs/market-maps/"

Present the full contents of the saved .md file to the user.

When it activates

Automatically, when a task matches. Use when asked to understand a market, find ICP pain points, map competitors, build a positioning doc, find messaging angles, or answer who is my customer and what

About this skill

Automatic

Sales & GTM pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

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

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

varnan-tech / opendirectory

skills/map-your-market/SKILL.md

View on GitHub ↗

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

Illustrative Want to see what varnan-tech/map-your-market 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

Map Your Market

Common Mistakes

Step 1: Setup Check

Step 2: Parse Input

Step 3: Run the Standalone Data Collection Script

Step 4: AI Pain Clustering

Step 5: ICP Profiling

Step 6: Market Size Synthesis

Step 7: Positioning Framework

Step 8: Self-QA and Save Output

When it activates

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