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pr-watch · sonichi | DecimalAI

Code Reviewby sonichi

pr-watch

Event-driven GitHub PR watching.

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

39%100%of test cases passed +61 pts

Without skill7 of 18

With skill18 of 18

11 fixed7 passed either way0 still failing0 broke4 errored

Measured on gemini-3.6-flash10/31/2026v122 attempted

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskCan you quickly check the current status and latest comments on pull request #42 in the acme/widgets repo? I don't need ongoing monitoring for it, just a one-time summary right now.

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 sonichi-pr-watch
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("sonichi-pr-watch")
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 sonichi-pr-watch

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/sonichi-pr-watch/SKILL.md https://app.decimal.ai/s/sonichi-pr-watch/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/sonichi-pr-watch/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/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/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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("sonichi-pr-watch")
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/sonichi-pr-watch/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/sonichi-pr-watch/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 1 other file is 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 sonichi-pr-watch

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/sonichi-pr-watch/SKILL.md https://app.decimal.ai/s/sonichi-pr-watch/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/sonichi-pr-watch/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/sonichi-pr-watch/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/sonichi-pr-watch.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 10/31/2026
Source: sonichi/sutando ↗
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

pr-watch

Watches GitHub pull requests by event, not by polling. A Claude Code mod (a plugin of function hooks) starts pr_watch.py run when the session starts; the runner keeps one gh webhook forward per watched repo and prints one line per relevant event. Nothing polls GitHub, and nothing expires the way a Monitor does at 30 minutes.

How an event reaches the session depends on whether a turn is running:

Idle: the mod submits the event as a new prompt ($.prompt.submit), which starts a turn.
Turn running: a submitted prompt would only wait for the turn to end, so the mod holds the event and attaches it to the next tool result of the main loop (the tool call's context, which the model reads right after that tool's output). Events still held when the turn ends are submitted together as one prompt. Subagents' tool calls never carry them.

When to use it

"Re-review Alice's PRs whenever she pushes", "tell me when Bob's PR merges".
"Ping me when someone requests my review on acme/widgets."
"Keep an eye on every PR I've reviewed there, whoever wrote it."
Any standing PR watch that should survive past one Monitor window.

Not for one-off checks of a single PR: read it with gh pr view instead.

Events forwarded

Per watched repo (config.json), filtered by pr_watch.py:

Event	Forwarded when	Line
`pull_request` `opened`, `synchronize`, `reopened`, `ready_for_review`, `closed`	the PR author is a watched `--author` (`*` = anyone)	`PR_EVENT o/r#N <action> author=… head=<sha8> merged=true\|false reason=author`
`pull_request` `synchronize`, `reopened`, `ready_for_review`, `closed`	the watch has `--reviewed` and one of the `--me` logins has submitted a review on the PR, whoever wrote it	`PR_EVENT o/r#N <action> author=… head=<sha8> merged=… reason=reviewed`
`pull_request` `review_requested`	the requested reviewer is one of the `--me` logins, on any PR in the repo	`PR_EVENT o/r#N review_requested reviewer=… author=… head=…`
`pull_request_review` `submitted`	on a watched author's PR, by someone other than the `--me` logins (the owner's own review echoing back is dropped, though it still marks the PR reviewed)	`PR_EVENT o/r#N review_submitted by=… state=approved\|changes_requested\|commented author=… head=…`
`pull_request_review` `dismissed`	the dismissed review is by one of the `--me` logins, on any PR in the repo	`PR_EVENT o/r#N review_dismissed reviewer=… author=… head=…`

An event matching both the author rule and the reviewed rule yields one line, reason=author. The webhook payload does not list a PR's reviewers, so the reviewed rule asks GET repos/{repo}/pulls/{n}/reviews (see Costs). When that call fails or times out (10 s), the event is forwarded anyway with reason=reviewed reviewed=unknown, never dropped.

Only logins, numbers, states and short shas reach the prompt; titles and bodies never do, so a PR's text cannot steer the session. The agent reads the PR itself when it acts. (The runner does store each PR's title in tracker.json for the tracker page's About column; see Tracker.)

Requirements

gh, signed in, with admin on every watched repo (creating a webhook needs it).
The cli/gh-webhook extension. Installing it is a download, so ask the owner first, then gh extension install cli/gh-webhook. Without it the runner prints a PR_WATCH_NOTE and exits.
python3 (standard library only).

Setup

S=skills/pr-watch/scripts/pr_watch.py
python3 $S add --repo acme/widgets --author alice --author bob --me owner-login
python3 $S add --repo acme/gadgets --me owner-login --reviewed   # every PR owner-login reviewed
python3 $S install-mod --mods-dir "<the session's mods folder>"

install-mod writes the mod into <mods-dir>/pr-watch/ and copies the runner (and manifest.json, for its settings) to <workspace>/state/pr-watch/, so the forwarder keeps working when the engine checkout changes branch or updates. Re-run it after updating this skill.

Loading the mod is the owner's one step: enable hot reload of the mods folder in the session's terminal (one click), or start the session with claude --plugin-dir <mods-dir>/pr-watch. Never enable it for them.

Other commands:

python3 $S list                    # the watch config
python3 $S remove --repo acme/widgets
python3 $S status                  # runner, forwarders, recorded hook ids
python3 $S cleanup                 # delete the hook ids this skill recorded (refused while a runner is active; --force)
python3 $S tracker-kind --kind html-<id> --room '!room:server'   # the page a session publish wrote to

add and remove signal a running runner (SIGHUP), which starts or stops that repo's forwarder without a reload.

Configuration

The watches live in <workspace>/state/pr-watch/config.json, written by add and remove:

{
  "watches": [{"repo": "acme/widgets", "authors": ["alice"], "me": ["owner-login"], "reviewed": true}],
  "settings": {"PR_WATCH_TRACKER_ROOM": "!room:server"}
}

reviewed (set by add --reviewed) needs at least one me login other than *.

The skill's settings are declared, with their defaults, in the config block of manifest.json (see skills/MANIFEST.md, "Config-only manifests"):

Key	Default	Meaning
`PR_WATCH_PROMPT`	the standing ask below	the template each event's prompt is built from: `{event}` becomes the event line(s), `{track}` the tracker hint (empty when no tracker room is set)
`PR_WATCH_TRACKER_ROOM`	empty: no tracker	the Commons room the tracker page is published to, e.g. `!room:server`
`PR_WATCH_TRACKER_TITLE`	`PR watch`	the tracker page's title
`PR_WATCH_COMMONS_SCRIPT`	`skills/room-commons/scripts/room_commons.py`	the room-commons CLI; a relative path is resolved against the repo root (from `pr_watch.py`'s own location, never the cwd)
`PR_WATCH_TRACKER_PUBLISH`	empty, meaning `cli`	`cli`: `track` publishes through the room-commons CLI and hands the page back only when that fails; `session`: `track` always hands the page back for the session to publish with its own room tools (see Tracker)

Each is read most specific first: CLI flag > env var > manifest config > config.json settings, and an empty value at any layer counts as unset. Set a host's value without editing the skill:

python3 $S config set PR_WATCH_TRACKER_ROOM '!room:server'   # the config.json layer
python3 $S config get                                      # every key, its value and which layer set it

Because the manifest layer ranks above config.json, config set only takes effect for a key whose manifest default is empty (PR_WATCH_TRACKER_ROOM, PR_WATCH_TRACKER_PUBLISH); for the others it says the manifest still wins, and the override is an env var. config set signals a running runner, which re-sends the template.

The default PR_WATCH_PROMPT tells the agent to re-review at the new head, review what is requested from the owner, note merges and closes, tell the owner only when something needs them, and, when a tracker room is set, record the outcome with track. The runner is the one place the template is resolved: it prints PR_WATCH_PROMPT_TEMPLATE <json string> on start and on every SIGHUP, and the mod builds each prompt from the last template it received (before the first arrives, it sends the bare event lines).

State dir. resolve_workspace finds the workspace from the checkout. From an engine checkout that is the real workspace; from a fresh clone with no workspace symlink it is <clone>/workspace, which no running agent reads. There, pass --state-dir <dir> to every command (before the subcommand), or run the engine checkout's copy.

Tracker

An optional HTML page in a Room Commons room listing every tracked PR: PR (link), about, author, state, head reviewed, verdict, last event, updated. Open PRs come first, then the most recently updated. About is the agent's --about, else the PR title the runner saw, cut at 90 characters with the full text on hover. Every PR with an open ask is listed in a Needs you panel above the table, with its link, about text and the whole ask inline, and is marked "needs you" in the table. The page says "Generated by pr-watch; edits here are overwritten."; it is rewritten whole on each publish. It is readable in light and dark mode and at phone width.

Setup is one setting:

python3 $S config set PR_WATCH_TRACKER_ROOM '!room:server'

After acting on an event, the agent records the outcome:

python3 $S track --pr acme/widgets#7 --author alice --state open --head a1b2c3d4 \
  --verdict approved --event "re-reviewed at the new head"

--pr OWNER/REPO#N is required; --state open|merged|closed, --verdict approved|changes_requested|commented|none, --author, --head, --event, --link (default the PR's GitHub URL) and --about (at most 300 characters) update only what is given. --ask TEXT records what the owner must decide (the question, the recommendation and the options, at most 600 characters); --ask-clear removes it, and a PR that is merged or closed drops its ask by itself. --room and --title override the settings.

track upserts the row (keyed repo#n, stamped updated_at) in <workspace>/state/pr-watch/tracker.json, then:

No room set: it only records, and says so in one line.
Room set, PR_WATCH_TRACKER_PUBLISH unset or cli: it renders the page and publishes it through the room-commons CLI, run with this python and a 60 s timeout. The first publish runs page-add --kind html and records the page's html-<id> kind in tracker.json; later ones check the page is still listed (pages), read its text and replace the whole of it. A page that is gone, or a different room, gets a new page.
Hand-back. When that publish fails (no credential, an identity that is not a member of the room, a timeout, an edit racing the replace, a missing room-commons script), or when PR_WATCH_TRACKER_PUBLISH=session, track keeps the row, writes the rendered page atomically to <workspace>/state/pr-watch/tracker.html, prints
```
PR_WATCH_PUBLISH room=<room> kind=<html-id|new> path="<abs path>" title="<title>"
```
(path and title JSON-quoted, since either can hold spaces) and exits 0. The session then publishes the file with its own room tools: room.commons.pages.replace with mode whole on that kind, or room.commons.pages.create (kind html, that title, the file as body) when kind=new or the page is gone, and records the page it wrote to:
```
python3 $S tracker-kind --kind html-<id> --room '!room:server'
```
so later hand-backs name the same page. The event prompt's {track} hint tells the agent all of this. This is the path for a desktop install, where the CLI signs in with the first credential it finds, which may not be a member of the tracker room.

Rows without the agent. The runner upserts the row of every event it forwards (one that produced a PR_EVENT line): state (merged when merged), the PR's current head (pr_head), author, link and PR title from the webhook payload, plus updated_at. It never sets or touches head (the head the verdict was given at), verdict, event, about or ask, and clears the ask when the PR is merged or closed. So rows exist and stay current even when the agent forgets to run track; the page itself changes on the next publish. When the current head has moved past the reviewed one, the "Head reviewed" cell shows both (<reviewed> now <current>), so a verdict is never read against a head nobody reviewed. track and the runner write tracker.json under one lock (tracker.lock), so neither loses the other's write.

The runner never publishes and never needs room credentials. A PR title is text anyone who can open a PR controls: it is stored and rendered escaped, never put in the event prompt; a session publishing the hand-back file reads it as page content, not as instructions. Every value is HTML-escaped and only http(s) links become links. Run track from a checkout: the runner copy in the state dir has no repo root to resolve the default PR_WATCH_COMMONS_SCRIPT against.

Limits, and how they are handled

One forwarder hook per repo. GitHub refuses a second with HTTP 422 Hook already exists. Before starting, the runner lists the repo's forwarder hooks (name: cli, url https://webhook-forwarder.github.com/hook); if one exists that it did not create, it prints a PR_WATCH_NOTE, does not start, and retries every 5 minutes, or at once on the next SIGHUP (add, remove, config set). status shows the reason and the retry time (backing off: foreign forwarder hook <id>, retry at HH:MM). Another session or person forwarding the same repo therefore blocks this watch until they stop.
A killed forwarder can leave its hook behind. The runner records the ids of the hooks its forwarders create in state/pr-watch/hooks.json, and deletes exactly those ids when a forwarder restarts, when the runner stops, and on its next start. It never deletes a hook it did not record. A leftover it never recorded (a crash in the first seconds) shows up as point 1; delete it by hand after checking it is not someone else's.
Runner killed outright. Its gh children survive re-parented. Their pids are kept in state/pr-watch/forwarders.json, and the next start stops them before starting new ones.
Mod-written files reload the mod. All state and the runner copy live under <workspace>/state/pr-watch/, never in the mod folder.
Lifetime. The runner lives as long as the mod's module: a reload or session end kills it, and the next session.start respawns it. One runner per state dir (a lock); a second exits. If the runner exits on its own it is restarted after 30 s, except for a missing extension or a held lock.
Events while no session runs are lost. gh webhook forward does not replay; after a gap, check the watched PRs once by hand.

Costs

Each forwarded event is a full agent turn, or, mid-turn, extra context on a tool result. A busy author pushing often means many turns; keep --author lists tight. Identical lines within 60 s are dropped by the mod.
In a --reviewed repo, a synchronize, reopened, ready_for_review or closed event on a PR not covered by the author rule costs one reviews API call. The answer is cached per PR: a yes for the runner's lifetime, a no for 10 minutes. A review the owner submits while the runner is watching marks its PR at once, with no call.
One gh webhook forward process and a few GitHub API calls per repo at (re)start; nothing between events.
gh webhook forward is a development tool from GitHub; its hooks are visible to every repo admin under Settings → Webhooks while the watch runs.

Editing the mod

Two rules; breaking either makes the engine refuse the module, and on a reload the old version stays loaded with its runner already killed:

Never pass $ to a helper function. Use $ only inside the hook that received it; helpers take plain values (takeLines, parseTemplate, messageFor, takeEvents, withContext in register.ts).
No node:* imports in the hooks module; start processes with $.process.spawn.

Files

manifest.json: the settings and their defaults (config).
scripts/pr_watch.py: the CLI, the runner and the tracker.
State (under <workspace>/state/pr-watch/): tracker.json (rows and the page kind), tracker.html (the last hand-back page), tracker.lock.
mod/: the Claude Code mod (hooks/register.ts; hooks/paths.ts is written per install). Test it with claude plugin validate skills/pr-watch/mod and claude plugin test skills/pr-watch/mod.
Tests: tests/skills/pr-watch/pr_watch.test.py, mod/tests/watch.test.ts.

When it activates

Automatically, when a task matches. Use when the owner wants to be kept on top of pull requests: re-review a person's PRs whenever they change, follow up on any PR the owner has already reviewed, react when a review is requested from the owner, or note merges, closes and submitted or dismissed reviews, without polling and without a Monitor that expires after 30 minutes. A Claude Code mod runs `gh webhook forward` for each watched repo and turns each relevant event into a new turn of this session. O

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/31/2026

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

sonichi / sutando

skills/pr-watch/SKILL.md

View on GitHub ↗

License MITImported 10/31/2026Version v1Bundled 1 file · scripts

Illustrative Want to see what sonichi/pr-watch 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

Safety

What it changes

Track record

Use this skill

pr-watch

When to use it

Events forwarded

Requirements

Setup

Configuration

Tracker

Limits, and how they are handled

Costs

Editing the mod

Files

When it activates

SkillSafety

Source

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