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/flow-next:make-pr — PR-as-cognitive-aid · gmickel | DecimalAI

Code Reviewby gmickel

/flow-next:make-pr — PR-as-cognitive-aid

Render a cognitive-aid PR body from flow-next state and open via gh.

Maintained

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

32%32%of test cases passed 0 pts

Without skill8 of 25

With skill8 of 25

6 fixed2 passed either way11 still failing6 broke

Measured on gemini-3.6-flash8/18/2026v79 comparable

Track record

Agent runs: 0; All time

Use this skill

Your agent runs on

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

pip install "decimalai>=0.15"
decimalai skills add gmickel-flow-next-make-pr
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("gmickel-flow-next-make-pr")
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 gmickel-flow-next-make-pr

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/gmickel-flow-next-make-pr/SKILL.md https://app.decimal.ai/s/gmickel-flow-next-make-pr/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/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/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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("gmickel-flow-next-make-pr")
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/gmickel-flow-next-make-pr/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/gmickel-flow-next-make-pr/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 gmickel-flow-next-make-pr

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/gmickel-flow-next-make-pr/SKILL.md https://app.decimal.ai/s/gmickel-flow-next-make-pr/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/gmickel-flow-next-make-pr/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/gmickel-flow-next-make-pr/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/gmickel-flow-next-make-pr.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: v7, tested 8/18/2026
Source: gmickel/flow-next ↗
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

/flow-next:make-pr — PR-as-cognitive-aid

A reviewable PR body is itself an artefact: it lets a human decide where to focus before skimming the diff. flow-next already collects every input that body needs — the spec with R-IDs, per-task done summaries and evidence commits, decisions / bug / architecture-patterns memory entries, glossary changes, strategy alignment, deferred review findings, the git diff itself. This skill stitches those into a structured body, optionally adds mermaid diagrams for module-boundary changes, and pushes via gh pr create.

The host agent (Claude Code / Codex / Droid) reads the structured payload from flowctl spec export-cognitive-aid and synthesizes the body directly. Every claim in the body must trace to a structured field in the export payload — never fabricate file paths, SHAs, R-ID attributions, or "why" reasoning. Unknown attribution is honest ("uncovered" / "unclear") rather than invented. The host is competent at "what looks important here?" given the rich input; no second-model review pass is needed (the structured payload does the heavy lifting).

flowctl provides only thin plumbing: flowctl spec export-cognitive-aid <spec-id> --base <ref> --json aggregates the inputs into a single JSON payload (Task 1 of this spec). The skill renders the body, then pushes and creates the PR directly — no confirm prompt (invoking make-pr is the intent; the body is deterministic; the default is a reversible draft). --dry-run prints the body without creating; --ready/--draft set draft state.

Read workflow.md for Phases 0–3 (pre-flight → gather → render body → mermaid) + the §4.0 --dry-run short-circuit — each phase ends with its inline ### Done when checklist. You MUST also read pr-cognitive-aid.md before composing any body — it IS Phase 1.5 (compose → flowctl pr-cognitive-aid validate/write → deterministic render), runs on EVERY invocation including --dry-run, and its rendered walkthrough supersedes the legacy Verification section, and the legacy R-ID coverage section when coverage is fully evidenced (with any unevidenced or undeclared criterion that table renders beside the walkthrough - pr-cognitive-aid.md §4 owns the rule); a body composed without executing it is a contract violation, not a style choice. Phase 1.5b additionally loads html-lens.md only when HTML artifacts are enabled and the run is not --dry-run. The post-render create + finalize machinery (§4.1 title → §4.6 gh pr create/--update → Phase 5 receipt/footer) lives in create-and-finalize.md, read ONLY on a real create (after §4.0 does not short-circuit) — a --dry-run preview never loads it. Read mermaid-rules.md before emitting any mermaid codefence — it defines reserved words, escape patterns, shape selection, the hard caps + allocation rule, the prose-summary rule, the pre-emission validation checklist, the Phase-3 hallucination guardrails, and the diff-fenced structural sketch alternate emission (§8); the --no-mermaid / no-trigger / skip-rule paths never load it. Phase 2's §2.11b Live QA section is likewise gated: references/live-qa-section.md is read only when the spec's qa_verdict receipt is present (the uncommon case).

Preamble

CRITICAL: flowctl is BUNDLED — NOT installed globally. which flowctl will fail (expected). Define once; subsequent blocks (here and in workflow.md) use $FLOWCTL:

FLOWCTL="${CODEX_HOME:-$HOME/.codex}/scripts/flowctl"
[ -x "$FLOWCTL" ] || FLOWCTL="<plugin-root>/scripts/flowctl"   # <plugin-root> = the directory two levels above this skill's SKILL.md file (the harness gave you that file's absolute path when the skill loaded); substitute it literally
[ -x "$FLOWCTL" ] || FLOWCTL=".flow/bin/flowctl"

Inline skill (no context: fork) — plain-text numbered prompt must stay reachable for the Phase 0 info prompts (resolve a missing base ref / undetected spec id — never a confirm gate). Subagents can't call plain-text numbered prompts (Claude Code issues #12890, #34592). There is no Phase 4 confirm prompt — make-pr creates the PR directly.

Mode Detection

Parse $ARGUMENTS as a flag list. Recognized flags: --draft, --ready, --no-mermaid, --memory, --dry-run, --base <ref> (consumes the next token), and the literal token mode:autonomous. Strip recognized tokens; the remainder (if any) is the optional spec id.

RAW_ARGS="$ARGUMENTS"
DRAFT_FORCE="auto"      # auto | draft | ready
NO_MERMAID=0
WRITE_MEMORY=0
DRY_RUN=0
BASE_REF=""
SPEC_ID=""
AUTONOMOUS=0

# Tokenize and walk the argument list. The loop handles both `--base=<ref>`
# and space-separated `--base <ref>` via a PREV token holder. Deliberately NO
# bash positional parameters here — the host's argument interpolation rewrites
# positional tokens inside skill code blocks (pilot dogfood finding, 1.13.0).
PREV=""
for ARG in $RAW_ARGS; do
  case "$PREV" in
    --base) BASE_REF="$ARG"; PREV=""; continue ;;
  esac
  case "$ARG" in
    --draft)      DRAFT_FORCE="draft" ;;
    --ready)      DRAFT_FORCE="ready" ;;
    --no-mermaid) NO_MERMAID=1 ;;
    --memory)     WRITE_MEMORY=1 ;;
    --dry-run)    DRY_RUN=1 ;;
    --base)       PREV="$ARG" ;;
    --base=*)     BASE_REF="${ARG#--base=}" ;;
    mode:autonomous) AUTONOMOUS=1 ;;
    -*) echo "Unknown flag: $ARG" >&2; exit 2 ;;
    *)  SPEC_ID="$ARG" ;;
  esac
done
[[ -n "$PREV" ]] && { echo "Flag $PREV given without a value" >&2; exit 2; }

# Secondary signal: process-level autonomous driver (env survives only
# within one process tree; the token is the primary, prose-safe carrier).
if [[ "${FLOW_AUTONOMOUS:-}" == "1" ]]; then
  AUTONOMOUS=1
fi

Flag	Effect
`--draft`	Force draft PR regardless of open-items count or Ralph context.
`--ready`	Force non-draft PR. Conflicts with `--draft` (last flag wins; surface the conflict).
`--no-mermaid`	Skip Phase 3 entirely. Mermaid prose summaries are also skipped.
`--memory`	After PR creation, write a `knowledge/architecture-patterns/` memory entry summarizing what shipped. Idempotent — rerun adds no second entry for the same spec id.
`--dry-run`	Skip Phase 4 entirely. Render body to stdout. Useful for inspection or `… --dry-run \| pbcopy`.
`--base <ref>`	Override base-branch detection cascade. Useful when the team's default branch is `develop`, etc.
`mode:autonomous`	Autonomous mode: Phase 0 info prompts hard-error instead of asking; draft forced. Sets `AUTONOMOUS=1` only — NEVER `RALPH`. Also derived from `FLOW_AUTONOMOUS=1`.

Ralph mode (FLOW_RALPH=1 or REVIEW_RECEIPT_PATH set) is detected separately in workflow.md §0.0 — the skill is not Ralph-blocked. Under Ralph the skill hard-errors instead of asking the Phase 0 info prompts, forces --draft, and emits the PR URL to stdout. (The PR is created directly in both modes — the only difference is forced-draft + no Phase 0 prompts under Ralph.) Autonomous mode is a SEPARATE flag: AUTONOMOUS=1 derives only from the mode:autonomous token or FLOW_AUTONOMOUS=1 and never sets RALPH. Under RALPH || AUTONOMOUS the Phase 0 info prompts hard-error and --draft is forced (--ready ignored with a note); the PR_URL= stdout contract and all receipt/harness semantics remain Ralph-only.

Interaction Principles

Ask the user via plain text. Render the options below as a numbered list 1. … N., followed by a final option N+1. Other — type your own answer. Print the question, then the numbered list, then stop and wait for the user's next message before continuing. Parse the reply as: a bare number 1–N+1 → that option; the literal text of an option label → that option; free text after Other → custom answer.

Ask one question at a time via plain-text numbered prompt. Never silently skip the question.
Lead with the recommended option and a one-sentence rationale.
No confirm gate. make-pr opens the PR without asking. Phase 0 asks only to resolve info it cannot derive (no --base and no detection match; no spec detected) — never "do you want to create it?". Not-all-tasks-done warns and proceeds (the open items make it a draft). Skip questions when context resolves cleanly.
Ralph and autonomous modes skip all questions. Detect both once at Phase 0 and route deterministically; a genuinely unanswerable gap hard-errors with a clear message (NEEDS_HUMAN-style) instead of hanging on a prompt.

Hallucination guardrails

The body is synthesized from the export payload. Every claim must trace to a structured field. The skill explicitly forbids:

Inventing file paths. Only paths returned by git diff --name-status (via the diff.files array) appear in Critical Changes / Review plan. No "I think there's also a config file" content.
Inventing risk or verification claims. Every "must review because…" clause in the Review plan traces to a diff_summary risk signal (churn / public export / security path / cross-module edge / user-facing surface); every "the pipeline verified…" line in the How-to-review block traces to tasks[].evidence / R-ID coverage / reviews.*. No narrated risk and no claimed verification without a payload anchor — absent verification is stated honestly ("no cross-model review recorded on this PR").
Fabricating commit SHAs. SHAs come from tasks[].evidence[].commits and git log --oneline base..HEAD only.
Guessing R-ID coverage. Coverage is computed from task satisfies frontmatter. Declared and evidenced are distinct: an R-ID no task claims (undeclared_r_ids) gets a ⚠️ flag; one claimed by a task that is not done yet renders as ⏳ claimed, not yet evidenced. Never a confident attribution either way.
Inventing "why" reasoning. Decision context comes from memory.decisions[] entries' bodies. If no decision entry exists for a change, the body says so explicitly rather than narrating a plausible-sounding rationale.
Quoting raw diff content. The body talks ABOUT the diff (paths, churn, modules). Never includes code snippets — privacy + secret-leakage risk; GitHub renders the actual diff below the body.
Synthesizing review findings. Findings come from reviews.deferred[] and reviews.suppressed_count. The body never editorializes severity or fabricates findings.
Generating fictitious memory IDs. When the body references memory entries (decisions / bugs / patterns), the IDs come from the export payload — never interpolated.
Synthesizing strategy alignment. Strategy section content comes verbatim from strategy.tracks[] and the spec's ## Strategy Alignment block. The body never invents alignment claims.
Inventing glossary terms. Glossary section content comes from glossary.changes[]. New terms / renamed terms are surfaced only if the export reports them.
Hallucinating mermaid relationships. Diagram nodes + edges come from real cross-module imports detected via git diff analysis (Phase 3 details in the mermaid-rules.md ref file). The skill never adds "I think module X also imports Y" edges.

When data is missing, the body says so honestly (e.g. *No decision-track memory entries for this spec. Surface decisions in PR review comments if needed.*) rather than confabulating content. Honest "unclear" beats plausible "wrong".

Forbidden

Ralph-blocking the skill. This skill is the autonomous-loop terminus per spec R24. Detect Ralph but proceed (with --draft forced). A FLOW_RALPH/REVIEW_RECEIPT_PATH exit-2 guard at the top of the skill has broken this.
Re-adding a confirm gate. make-pr creates the PR without prompting. A run that asks "do you want to create it?" before push has broken this — the escape hatch is --dry-run, not a question.
Pushing or creating PRs in --dry-run mode. Phase 4 short-circuits before any git push or gh pr create. The body lands on stdout only.
Squashing the existing-PR check. A bare gh pr view --json url 2>/dev/null returns rc=0 for CLOSED and MERGED PRs as readily as OPEN. Filter .state == "OPEN" via jq (validated empirically during fn-42 spike). Closed/merged PRs on a reused branch must NOT trigger refusal.
Manual git push workflows when gh is missing. When gh isn't installed or authenticated, surface the install / gh auth login instructions and exit. Don't try to fall back to half-baked PR creation.
Writing memory entries without --memory. Default off. The user opts in for structurally-significant specs — every-PR memory inflation is the failure mode this gate prevents.
Quoting raw diff content in the body. See hallucination guardrails — the body describes the diff, never copies code.
Calling gh pr merge. Out of scope. The skill creates and exits; merge is a human decision.
git add -A (or any broad stage) for a legacy PR-artifact commit. A supported current v1 lens stays local-only so it cannot stale its own head-bound input. The fallback committed path stages exactly .flow/artifacts/<spec-id>/pr.html with the fixed message chore(flow): pr artifact <spec-id> — unrelated working-tree changes are not make-pr's concern.
Opening a Lavish session or running lavish-axi poll from make-pr. The PR artifact is a read-only review instrument — no annotate loop, interactive or autonomous. Review conversation belongs to the code host.
Emitting an artifact blob link that can 404. Gitignored .flow/artifacts/ → local-open guidance only; committed mode links only after the narrow artifact commit landed on the branch being pushed.

Workflow

Execute the phases in workflow.md in order:

Pre-flight — gh installed + authenticated; resolve spec id (arg or branch-match); base-branch detection cascade; branch validity (HEAD ahead of base); all tasks done (warn + proceed as draft if not — no prompt; Ralph exits 2); existing-PR refusal filtered on .state == "OPEN". Detects Ralph environment for downstream phases.
Gather inputs — single call to flowctl spec export-cognitive-aid <spec-id> --base <ref> --json; parse the structured payload (spec / tasks / memory / glossary / strategy / diff / reviews). 1.5. Structured PR cognitive aid (ALWAYS — read pr-cognitive-aid.md and execute it) — the existing host composes one grounded v1 walkthrough from the export-time head. flowctl validates/persists/selects/renders it before optional HTML and final body creation. Not optional and not HTML-gated — only 1.5b is opt-in. No extra model call. Supported current artifacts render compact/full deterministically; stale/invalid artifacts select the existing fallback without mixing fields. The post-creation makePr tracker facade remains unchanged. 1.5b. HTML render lens (opt-in) — only when artifacts.html.enabled is true AND not --dry-run: generate .flow/artifacts/<spec-id>/pr.html (read-only review instrument per the shared disclosure reference plugins/flow-next/references/html-artifacts.md §5 — diff-derived, R-ID-verified with flagged mismatch rows). A supported current v1 object is embedded as the exact HTML-safe semantic carrier and stays local-only so HEAD remains equal to its headSha; only the visibly labeled legacy fallback may use the narrow committed-artifact path. Record the render-lens line for the body summary block. Never opens a Lavish session or polls (interactive AND autonomous). Failure is non-fatal — one stderr note, PR proceeds. With the mode off/unset there is zero artifact-related behavior or output beyond the single config read.
Render body — TL;DR, R-ID coverage table, Critical changes, How to review this PR (trust-calibration coaching block), Review plan (risk-ranked Must review / Spot-check / Safe to skim with a ≤~30% focus budget), Decisions made, Memory left behind, Glossary/strategy notes, Open items, footer breadcrumb. Sections without content are omitted (never empty placeholder headings).
Mermaid generation — gated by 5 trigger conditions (cross-module imports, public interface changes, new/removed top-level dirs, high fan-out spec). Hard caps: 3 diagrams, 12 nodes, 25 edges, 12K characters. Each codefence preceded by a 3-5 sentence plain-language prose summary (load-bearing for forges that don't render mermaid). Validates each codefence against the mermaid-rules.md §6 checklist (reserved words, escape patterns, no emoji / MathJax, no inheritance cycles) before emitting. Skipped under --no-mermaid or when no triggers fire / a skip rule applies (pure-additive single-module diff <50 LOC, flat-layout repo).
Push + create PR — git push -u origin HEAD, then gh pr create --title --body. Draft when OPEN_ITEMS_COUNT > 0 OR Ralph OR --draft; ready when --ready. --dry-run short-circuits before push.
Output + footer — emit PR URL on success; print breadcrumb (Generated by /flow-next:make-pr from <spec-id> against <base>) plus the invisible machine marker  land's authorship probe keys on (issue #274); optionally write knowledge/architecture-patterns/ memory entry under --memory.

When it activates

Automatically, when a task matches. Triggers on /flow-next:make-pr with optional spec id and flags (--draft, --ready, --no-mermaid, --base <ref>, --memory, --dry-run). Auto-detects spec from current branch when no id given. NOT Ralph-blocked — autonomous loops can surface a draft PR for human review.

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/17/2026

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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

gmickel / flow-next

optimization/make-pr/baseline/SKILL.md

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License MITImported 8/17/2026Version v7Bundled 2 files

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

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

bilal140202/flow-next-make-prRender a cognitive-aid PR body from flow-next state and open via gh. Triggers on /flow-next:make-pr with optional spec id and flags (--draft, --ready, --no-mermaid, --base <ref>, --memory, --dry-run). Auto-detects spec from current branch when no id given. NOT Ralph-blocked — autonomous loops can surface a draft PR for human review.Near-identical description+32 pts pass-rate lift

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What it changes

Track record

Use this skill

/flow-next:make-pr — PR-as-cognitive-aid

Preamble

Mode Detection

Interaction Principles

Hallucination guardrails

Forbidden

Workflow

When it activates

SkillSafety

Source

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