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GitHub Issue Demand Signal · varnan-tech | DecimalAI

Researchby varnan-tech

GitHub Issue Demand Signal

Takes a competitor's public GitHub repo URL, fetches their open issues via the GitHub REST API, filters noise locally, clusters issues into 6 demand categories, computes a demand score per issue and per cluster, and outputs a ranked demand gap report with a GTM messaging brief.

Maintained

SkillScore

SkillScore68/100

Test results 68

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%68%of test cases passed +32 pts

Without skill8 of 22

With skill15 of 22

7 fixed8 passed either way7 still failing0 broke

Measured on gemini-3.6-flash8/18/2026v120 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskOut of 8 total cluster themes generated during demand analysis, how many top clusters are selected to construct the GTM messaging brief, and how are they chosen?

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-gh-issue-to-demand-signal
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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal/SKILL.md https://app.decimal.ai/s/varnan-tech-gh-issue-to-demand-signal/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/varnan-tech-gh-issue-to-demand-signal/SKILL.md

This gets SKILL.md only. The skill's 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/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-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/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-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal")
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-gh-issue-to-demand-signal/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/varnan-tech-gh-issue-to-demand-signal/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 2 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-gh-issue-to-demand-signal

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-gh-issue-to-demand-signal/SKILL.md https://app.decimal.ai/s/varnan-tech-gh-issue-to-demand-signal/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-gh-issue-to-demand-signal/SKILL.md

This gets SKILL.md only. The skill's 2 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-gh-issue-to-demand-signal/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/varnan-tech-gh-issue-to-demand-signal.json

This gets SKILL.md only. The skill's 2 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

GitHub Issue Demand Signal

Take a competitor's public GitHub repo. Fetch their open issues. Filter noise locally. Cluster into 6 demand categories. Score by real engagement. Output a ranked demand gap report and GTM messaging brief.

Critical rule: Every issue title in the output must be verbatim from the GitHub API response. Every cluster theme name must be derived from actual issue titles in that cluster. If fewer than 10 issues remain after noise filtering, stop and tell the user -- the repo is too small for reliable clustering. No invented issue content anywhere.

Common Mistakes

The agent will want to...	Why that's wrong
Send all 200 raw issues to the AI without filtering	Bot issues, PRs, and zero-engagement noise inflate cluster counts and waste context. Filter locally first.
Use comment count as the primary demand signal	Comments include maintainer responses, off-topic discussion, and spam. reactions["+1"] is the cleanest buyer signal.
Paraphrase issue titles when summarizing clusters	Paraphrasing loses the buyer's exact language, which is the entire point. Use verbatim issue titles.
Continue past Step 4 if fewer than 10 issues remain after filtering	Under 10 issues means the repo is too small or the wrong URL was given. Clustering on sparse data produces meaningless categories.
Include pull requests in the analysis	The GitHub Issues endpoint returns PRs too. Filter by checking that the pull_request key is absent on the issue object.
Mark an issue as ignored demand without checking all 3 criteria	All three must be true: reactions >= 10, age >= 180 days, no planned/in-progress/roadmap label. Missing one criterion disqualifies the issue.

Step 1: Setup Check

echo "GITHUB_TOKEN: ${GITHUB_TOKEN:-not set, unauthenticated rate limit applies (60 req/hr)}"

If GITHUB_TOKEN is not set: Continue. Tell the user: "GITHUB_TOKEN is not set. Unauthenticated rate limit is 60 requests/hour -- enough for 2 fetches before hitting the limit. For repeated use, add a token at github.com/settings/tokens (no scopes needed for public repos)."

Step 2: Gather Input

You need:

GitHub repo URL (e.g. https://github.com/owner/repo) or owner/repo slug (e.g. facebook/react)

Parse owner and repo from input:

python3 << 'PYEOF'
import re, sys, os

raw = "REPO_INPUT_HERE"

# Normalize to owner/repo
if raw.startswith("http"):
    m = re.search(r"github\.com/([^/]+)/([^/?\s]+)", raw)
    if not m:
        print("ERROR: Could not parse GitHub URL. Expected format: https://github.com/owner/repo")
        sys.exit(1)
    owner, repo = m.group(1), m.group(2).rstrip("/")
elif "/" in raw:
    parts = raw.strip().split("/")
    owner, repo = parts[0], parts[1]
else:
    print("ERROR: Input must be a GitHub URL or owner/repo slug (e.g. vercel/next.js)")
    sys.exit(1)

print(f"Owner: {owner}")
print(f"Repo: {repo}")

with open("/tmp/ghd-target.txt", "w") as f:
    f.write(f"{owner}/{repo}")
PYEOF

If parsing fails: Stop. Ask: "Please provide the GitHub repo as a URL (https://github.com/owner/repo) or an owner/repo slug (e.g. vercel/next.js)."

Step 3: Fetch Issues from GitHub REST API

Fetch up to 200 issues (2 pages of 100). Check rate limit after the first fetch.

python3 << 'PYEOF'
import json, urllib.request, os, sys
from datetime import datetime, timezone

target = open("/tmp/ghd-target.txt").read().strip()
owner_repo = target
token = os.environ.get("GITHUB_TOKEN", "")

headers = {"Accept": "application/vnd.github+json", "User-Agent": "gh-issue-demand-signal/1.0"}
if token:
    headers["Authorization"] = f"Bearer {token}"

all_issues = []
rate_limit_hit = False

for page in [1, 2]:
    url = f"https://api.github.com/repos/{owner_repo}/issues?state=open&per_page=100&page={page}"
    req = urllib.request.Request(url, headers=headers)

    try:
        with urllib.request.urlopen(req, timeout=30) as resp:
            # Check rate limit after first page
            if page == 1:
                remaining = int(resp.headers.get("X-RateLimit-Remaining", 999))
                reset_ts = resp.headers.get("X-RateLimit-Reset", "")
                if remaining == 0:
                    reset_str = datetime.fromtimestamp(int(reset_ts), tz=timezone.utc).strftime("%H:%M UTC") if reset_ts else "unknown"
                    print(f"ERROR: GitHub rate limit exhausted. Resets at {reset_str}.")
                    print("Add GITHUB_TOKEN to your .env file to get 5000 req/hr. See github.com/settings/tokens (no scopes needed).")
                    sys.exit(1)
                print(f"Rate limit remaining: {remaining}")

                # Check for 404/403
                status = resp.status
                if status == 404:
                    print(f"ERROR: Repo '{owner_repo}' not found. Check the URL or slug.")
                    sys.exit(1)

            page_data = json.loads(resp.read())
            if not page_data:
                print(f"Page {page}: empty, stopping.")
                break
            all_issues.extend(page_data)
            print(f"Page {page}: {len(page_data)} issues fetched")
    except urllib.error.HTTPError as e:
        if e.code == 404:
            print(f"ERROR: Repo '{owner_repo}' not found (404). Check the URL or slug.")
        elif e.code == 403:
            print(f"ERROR: Access denied (403). Repo may be private or rate limit hit.")
        else:
            print(f"ERROR: GitHub API returned HTTP {e.code}")
        sys.exit(1)
    except Exception as e:
        print(f"ERROR: Failed to fetch page {page}: {e}")
        sys.exit(1)

print(f"Total raw issues fetched: {len(all_issues)}")
json.dump(all_issues, open("/tmp/ghd-raw-issues.json", "w"), indent=2)
PYEOF

If GitHub returns 404: Stop. Tell the user: "Repo not found. Check the URL or slug and try again. Private repos are not accessible without authentication and explicit repo scope."

If GitHub returns 403 with rate limit header: Stop. Show the reset time and tell the user to add GITHUB_TOKEN.

Step 4: Pre-Process Locally -- Filter, Score, Detect Ignored Demand

No API call. Pure Python. Run before anything goes to the AI.

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

raw = json.load(open("/tmp/ghd-raw-issues.json"))
target = open("/tmp/ghd-target.txt").read().strip()
now = datetime.now(tz=timezone.utc)

noise_patterns = re.compile(
    r"^(chore|deps|bump|renovate|dependabot|release|ci|build|revert)[\s:\[]",
    re.IGNORECASE
)
pr_title_patterns = re.compile(
    r"^(feat|fix|refactor|docs|test|style|perf|chore)(\(.+\))?:",
    re.IGNORECASE
)

filtered = []
noise_count = 0
noise_reasons = {}

for issue in raw:
    # Skip pull requests (GitHub Issues endpoint returns PRs too)
    if "pull_request" in issue:
        noise_count += 1
        noise_reasons["pull_request"] = noise_reasons.get("pull_request", 0) + 1
        continue

    title = issue.get("title", "")
    reactions = issue.get("reactions", {}).get("+1", 0)
    comments = issue.get("comments", 0)
    user_type = (issue.get("user") or {}).get("type", "User")

    # Skip bot-authored issues
    if user_type == "Bot":
        noise_count += 1
        noise_reasons["bot_author"] = noise_reasons.get("bot_author", 0) + 1
        continue

    # Skip bot-pattern titles
    if noise_patterns.match(title):
        noise_count += 1
        noise_reasons["bot_title"] = noise_reasons.get("bot_title", 0) + 1
        continue

    # Skip PR-as-issue titles
    if pr_title_patterns.match(title):
        noise_count += 1
        noise_reasons["pr_as_issue"] = noise_reasons.get("pr_as_issue", 0) + 1
        continue

    # Skip zero-signal issues
    if reactions == 0 and comments == 0:
        noise_count += 1
        noise_reasons["zero_signal"] = noise_reasons.get("zero_signal", 0) + 1
        continue

    # Compute demand score
    demand_score = (reactions * 2) + (comments * 0.5)

    # Detect ignored demand
    created_at = issue.get("created_at", "")
    if created_at:
        created = datetime.fromisoformat(created_at.replace("Z", "+00:00"))
        age_days = (now - created).days
    else:
        age_days = 0

    labels = [l.get("name", "").lower() for l in issue.get("labels", [])]
    has_planned_label = any(
        kw in label for label in labels
        for kw in ["in-progress", "planned", "roadmap", "wip", "in progress"]
    )

    ignored_demand = (
        reactions >= 10 and
        age_days >= 180 and
        not has_planned_label
    )

    filtered.append({
        "number": issue["number"],
        "title": title,
        "url": issue.get("html_url", f"https://github.com/{target}/issues/{issue['number']}"),
        "reactions_plus1": reactions,
        "comments": comments,
        "demand_score": demand_score,
        "age_days": age_days,
        "labels": labels,
        "ignored_demand": ignored_demand,
        "body_snippet": (issue.get("body") or "")[:300]
    })

# Sort by demand score descending
filtered.sort(key=lambda x: x["demand_score"], reverse=True)

print(f"Raw issues: {len(raw)}")
print(f"Noise filtered: {noise_count} ({', '.join(f'{k}: {v}' for k, v in noise_reasons.items())})")
print(f"Issues for analysis: {len(filtered)}")

if len(filtered) < 10:
    print(f"ERROR: Only {len(filtered)} issues remain after filtering. This repo has too few engaged issues for reliable clustering.")
    print("Try a larger repo or a repo with more community engagement.")
    import sys; sys.exit(1)

# Ignored demand summary
ignored = [i for i in filtered if i["ignored_demand"]]
print(f"Ignored demand issues: {len(ignored)}")

json.dump(filtered, open("/tmp/ghd-filtered-issues.json", "w"), indent=2)
print("Pre-processing complete.")
PYEOF

If fewer than 10 issues remain after filtering: Stop. Tell the user exactly how many issues were found and filtered, and why the repo is too small for reliable demand clustering.

Step 5: Cluster Issues

Print the filtered issues for analysis:

python3 << 'PYEOF'
import json
filtered = json.load(open("/tmp/ghd-filtered-issues.json"))
target = open("/tmp/ghd-target.txt").read().strip()

issue_list = filtered[:150]
print(f"Repo: {target}")
print(f"Issues to cluster: {len(issue_list)}")
print()
for i in issue_list:
    labels_str = f" [{', '.join(i['labels'][:3])}]" if i['labels'] else ""
    print(f"#{i['number']} [score:{round(i['demand_score'],1)} reactions:{i['reactions_plus1']}] {i['title']}{labels_str}")
PYEOF

Classify each issue printed above into one of these 6 categories: feature_gap, bug_pattern, ux_complaint, performance, integration_missing, docs_missing

Rules:

Classify each issue into exactly one category
Extract a 1-sentence pain statement using the user's exact language from the title -- do not paraphrase
Identify 5-8 cluster themes: short phrases (3-6 words) capturing dominant complaint patterns across all issues
No em dashes. No marketing language.

Write your analysis to /tmp/ghd-clusters.json with this exact structure:

{
  "classified_issues": [
    {"number": 123, "category": "feature_gap", "pain_statement": "Users need X which does not exist yet"}
  ],
  "cluster_themes": [
    {"theme_name": "Missing export options", "category": "feature_gap", "issue_numbers": [123, 456, 789]}
  ],
  "category_counts": {"feature_gap": 5, "bug_pattern": 3, "ux_complaint": 4, "performance": 2, "integration_missing": 6, "docs_missing": 1}
}

After writing the file, confirm with:

python3 -c "
import json
d = json.load(open('/tmp/ghd-clusters.json'))
print(f'Classified: {len(d[\"classified_issues\"])} issues, {len(d[\"cluster_themes\"])} themes')
print('Categories:', d['category_counts'])
"

Step 6: Messaging Brief

Compute total demand score per cluster and print the top 3:

python3 << 'PYEOF'
import json

filtered = json.load(open("/tmp/ghd-filtered-issues.json"))
clusters = json.load(open("/tmp/ghd-clusters.json"))
target = open("/tmp/ghd-target.txt").read().strip()

demand_by_issue = {i["number"]: i["demand_score"] for i in filtered}
issue_titles = {i["number"]: i["title"] for i in filtered}
issue_reactions = {i["number"]: i["reactions_plus1"] for i in filtered}

enriched_themes = []
for theme in clusters.get("cluster_themes", []):
    issue_nums = theme.get("issue_numbers", [])
    total_demand = sum(demand_by_issue.get(n, 0) for n in issue_nums)
    top_issues = sorted(issue_nums, key=lambda n: demand_by_issue.get(n, 0), reverse=True)[:3]
    enriched_themes.append({
        "theme_name": theme["theme_name"],
        "category": theme["category"],
        "issue_count": len(issue_nums),
        "total_demand_score": round(total_demand, 1),
        "top_issues": [
            {"number": n, "title": issue_titles.get(n, ""), "reactions": issue_reactions.get(n, 0)}
            for n in top_issues
        ]
    })

enriched_themes.sort(key=lambda x: x["total_demand_score"], reverse=True)
json.dump(enriched_themes, open("/tmp/ghd-enriched-themes.json", "w"), indent=2)

print(f"Top 3 clusters for messaging brief (repo: {target}):")
for t in enriched_themes[:3]:
    print(f"\n  {t['theme_name']} ({t['category']}) -- total demand: {t['total_demand_score']}")
    for ti in t["top_issues"]:
        print(f"    #{ti['number']}: \"{ti['title']}\" ({ti['reactions']} reactions)")
PYEOF

Generate a GTM messaging brief from the top 3 clusters printed above.

Rules:

Each positioning angle must cite the specific cluster it comes from
Each outreach hook must quote a verbatim issue title in quotation marks
Headlines must include a number or specific named pain -- no generic statements
No em dashes. No forbidden words: powerful, robust, seamless, innovative, game-changing, streamline, leverage, transform

Write your brief to /tmp/ghd-brief.json with this exact structure:

{
  "positioning_angles": [
    {
      "angle_name": "3-5 word label",
      "cluster_source": "theme_name from cluster",
      "positioning_statement": "2-3 sentences on what your product does that this competitor does not",
      "evidence": "verbatim issue title that best illustrates this gap"
    }
  ],
  "outreach_hooks": [
    {
      "hook_type": "pain quote hook",
      "hook_text": "2-3 sentences quoting a verbatim issue title in quotes",
      "best_for": "audience this hook works for"
    }
  ],
  "cluster_headlines": [
    {
      "theme_name": "from the cluster",
      "headline": "specific headline with a number or named pain",
      "sub_copy": "1 sentence expanding the headline"
    }
  ]
}

After writing the file, confirm with:

python3 -c "
import json
d = json.load(open('/tmp/ghd-brief.json'))
print('Positioning angles:', len(d.get('positioning_angles', [])))
print('Outreach hooks:', len(d.get('outreach_hooks', [])))
print('Cluster headlines:', len(d.get('cluster_headlines', [])))
"

Step 7: Self-QA

Run before presenting. Verify evidence. Remove violations. Check output integrity.

python3 << 'PYEOF'
import json

filtered = json.load(open("/tmp/ghd-filtered-issues.json"))
clusters = json.load(open("/tmp/ghd-clusters.json"))
themes = json.load(open("/tmp/ghd-enriched-themes.json"))
brief = json.load(open("/tmp/ghd-brief.json"))
target = open("/tmp/ghd-target.txt").read().strip()

failures = []
real_titles = {i["number"]: i["title"] for i in filtered}

# Verify: classified_issues only reference real issue numbers
real_numbers = set(real_titles.keys())
hallucinated = [
    c["number"] for c in clusters.get("classified_issues", [])
    if c["number"] not in real_numbers
]
if hallucinated:
    failures.append(f"Removed {len(hallucinated)} hallucinated issue numbers from classified_issues: {hallucinated[:5]}")
    clusters["classified_issues"] = [
        c for c in clusters.get("classified_issues", [])
        if c["number"] in real_numbers
    ]

# Verify: top-10 list is sorted by demand_score descending
top10 = filtered[:10]
for i, issue in enumerate(top10):
    if i > 0 and issue["demand_score"] > top10[i-1]["demand_score"]:
        failures.append("Top-10 list was not sorted by demand_score -- re-sorted.")
        filtered.sort(key=lambda x: x["demand_score"], reverse=True)
        top10 = filtered[:10]
        break

# Verify: ignored demand issues meet all 3 criteria
ignored = [i for i in filtered if i["ignored_demand"]]
for issue in ignored:
    if issue["reactions_plus1"] < 10 or issue["age_days"] < 180:
        issue["ignored_demand"] = False
        failures.append(f"Removed issue #{issue['number']} from ignored demand -- did not meet all 3 criteria")

# Check messaging brief counts
if len(brief.get("positioning_angles", [])) != 3:
    failures.append(f"Expected 3 positioning angles, got {len(brief.get('positioning_angles', []))}")
if len(brief.get("outreach_hooks", [])) != 3:
    failures.append(f"Expected 3 outreach hooks, got {len(brief.get('outreach_hooks', []))}")
if len(brief.get("cluster_headlines", [])) != 3:
    failures.append(f"Expected 3 cluster headlines, got {len(brief.get('cluster_headlines', []))}")

# Check for em dashes in brief
brief_str = json.dumps(brief)
if "\u2014" in brief_str:
    brief_str = brief_str.replace("\u2014", " - ")
    brief = json.loads(brief_str)
    failures.append("Fixed: em dash characters removed from messaging brief")

# Check for forbidden words
forbidden = ["powerful", "robust", "seamless", "innovative", "game-changing", "streamline", "leverage", "transform"]
full_text = (json.dumps(clusters) + json.dumps(brief)).lower()
for word in forbidden:
    if word in full_text:
        failures.append(f"Warning: forbidden word '{word}' found in output -- review before presenting")

# Build final output bundle
output = {
    "repo": target,
    "issues_analyzed": len(filtered),
    "clusters": clusters,
    "enriched_themes": themes,
    "filtered_issues": filtered,
    "messaging_brief": brief,
    "data_quality_flags": failures
}

json.dump(output, open("/tmp/ghd-output.json", "w"), indent=2)
print(f"QA complete. Issues addressed: {len(failures)}")
for f in failures:
    print(f"  - {f}")
if not failures:
    print("All QA checks passed.")
PYEOF

Step 8: Save and Present Output

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

output = json.load(open("/tmp/ghd-output.json"))
target = output["repo"]
repo_slug = target.replace("/", "-")
date_str = datetime.now(tz=timezone.utc).strftime("%Y-%m-%d")

filtered = output["filtered_issues"]
themes = output["enriched_themes"]
clusters = output["clusters"]
brief = output["messaging_brief"]
flags = output["data_quality_flags"]

ignored = [i for i in filtered if i.get("ignored_demand")]
top10 = filtered[:10]

# Build category summary from cluster data
category_counts = clusters.get("category_counts", {})

lines = [
    f"## Demand Gap Report: {target}",
    f"Issues analyzed: {output['issues_analyzed']} | Date: {date_str}",
    "",
    "---",
    "",
    "### Demand Gap Leaderboard",
    "",
    "| Rank | Theme | Category | Issues | Total Demand Score | Top Issue Reactions |",
    "|---|---|---|---|---|---|",
]

for i, theme in enumerate(themes[:8], 1):
    top_reactions = theme["top_issues"][0]["reactions"] if theme["top_issues"] else 0
    lines.append(
        f"| {i} | {theme['theme_name']} | {theme['category']} | "
        f"{theme['issue_count']} | {theme['total_demand_score']} | {top_reactions} |"
    )

lines += ["", "---", ""]

if ignored:
    lines += [
        "### Ignored Demand (High Reactions, No Maintainer Response)",
        "",
        "These issues have 10+ reactions, are 6+ months old, and have no planned/in-progress label.",
        "This is your opportunity window.",
        "",
    ]
    for issue in ignored[:10]:
        lines.append(
            f"- [{issue['title']}]({issue['url']}) -- "
            f"{issue['reactions_plus1']} reactions, {issue['age_days']} days old"
        )
    lines += ["", "---", ""]

lines += [
    "### Top 10 Highest-Demand Issues",
    "",
    "| Rank | Issue | Reactions | Comments | Demand Score | Link |",
    "|---|---|---|---|---|---|",
]
for i, issue in enumerate(top10, 1):
    short_title = issue["title"][:70] + ("..." if len(issue["title"]) > 70 else "")
    lines.append(
        f"| {i} | {short_title} | {issue['reactions_plus1']} | "
        f"{issue['comments']} | {round(issue['demand_score'], 1)} | "
        f"[#{issue['number']}]({issue['url']}) |"
    )

lines += ["", "---", "", "### Cluster Deep Dives", ""]

for theme in themes[:3]:
    lines.append(f"#### {theme['theme_name']}")
    lines.append(f"Category: {theme['category']} | Issues: {theme['issue_count']} | Total demand score: {theme['total_demand_score']}")
    lines.append("")
    lines.append("Top issues in this cluster:")
    for ti in theme["top_issues"]:
        lines.append(f"- \"{ti['title']}\" -- {ti['reactions']} reactions")
    lines.append("")

lines += ["---", "", "### Messaging Brief", ""]

for angle in brief.get("positioning_angles", []):
    lines.append(f"**{angle.get('angle_name', 'Angle')}**")
    lines.append(angle.get("positioning_statement", ""))
    lines.append(f"Evidence: \"{angle.get('evidence', '')}\"")
    lines.append("")

lines += ["---", "", "### GTM Angles", ""]

for hook in brief.get("outreach_hooks", []):
    lines.append(f"**{hook.get('hook_type', 'Hook')}**")
    lines.append(hook.get("hook_text", ""))
    lines.append(f"Best for: {hook.get('best_for', '')}")
    lines.append("")

lines += ["---", ""]
if flags:
    lines.append(f"Data quality notes: {'; '.join(flags)}")
else:
    lines.append("Data quality notes: None")

output_path = f"docs/demand-signals/{repo_slug}-{date_str}.md"
import os
os.makedirs("docs/demand-signals", exist_ok=True)
open(output_path, "w").write("\n".join(lines))
print(f"Saved to: {output_path}")

# Print to console
print("\n" + "\n".join(lines))
PYEOF

Clean up temp files:

rm -f /tmp/ghd-target.txt /tmp/ghd-raw-issues.json /tmp/ghd-filtered-issues.json \
      /tmp/ghd-cluster-request.json /tmp/ghd-clusters.json /tmp/ghd-enriched-themes.json \
      /tmp/ghd-brief-request.json /tmp/ghd-brief.json /tmp/ghd-output.json

When it activates

Automatically, when a task matches. Use when asked to scan a competitor's GitHub issues, find what their users are begging for, turn GitHub complaints into product positioning, identify competitor feature gaps, or generate messaging from real user demand. T

About this skill

Automatic

Product Management pack

SkillSafety

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Code scan15 of 15 checks clean

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

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

varnan-tech / opendirectory

skills/gh-issue-to-demand-signal/SKILL.md

View on GitHub ↗

License MITImported 10/17/2026Version v1Bundled 2 files

Illustrative Want to see what varnan-tech/gh-issue-to-demand-signal 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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SkillScore

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What it changes

Track record

Use this skill

GitHub Issue Demand Signal

Common Mistakes

Step 1: Setup Check

Step 2: Gather Input

Step 3: Fetch Issues from GitHub REST API

Step 4: Pre-Process Locally -- Filter, Score, Detect Ignored Demand

Step 5: Cluster Issues

Step 6: Messaging Brief

Step 7: Self-QA

Step 8: Save and Present Output

When it activates

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