Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

/flow-next:pilot — single-tick autonomous build-loop conductor · gmickel | DecimalAI

Agent Toolingby gmickel

/flow-next:pilot — single-tick autonomous build-loop conductor

Single-tick autonomous build-loop conductor for host drivers (/loop, /goal). Each invocation advances exactly ONE ready spec by ONE pipeline stage (plan, plan-review, work, qa [opt-in, gated on pipeline.qa==on], make-pr) and ends with a terminal PILOT_VERDICT line for the driver to read.

Never made a case worseMaintained

SkillScore

SkillScore73/100

Test results 73

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%73%of test cases passed +55 pts

Without skill4 of 22

With skill16 of 22

12 fixed4 passed either way6 still failing0 broke

Measured on gemini-3.6-flash8/18/2026v617 comparable

Track record

Agent runs: 18; All time

See one task, with and without the skill

One test taskIn backlog autonomy mode, which auxiliary tool is called to fetch issue comment rounds or parked question context?

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 gmickel-flow-next-pilot
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-pilot")
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-pilot

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

Copy SKILL.md

Save it in your project, as

.claude/skills/gmickel-flow-next-pilot/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-pilot

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-pilot")
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-pilot/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-pilot/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-pilot

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-pilot")
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-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

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

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-pilot")
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-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

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-pilot")
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-pilot/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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-pilot")
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-pilot/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-pilot/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-pilot

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-pilot")
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-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

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

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-pilot")
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-pilot/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-pilot

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-pilot")
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-pilot/SKILL.md

Give it to your agent

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

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

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-pilot")
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-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

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-pilot")
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-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

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

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-pilot")
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-pilot/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-pilot/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-pilot

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-pilot")
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-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

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

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

Copy SKILL.md

Save it in your project, as

.agents/skills/gmickel-flow-next-pilot/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-pilot/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/gmickel-flow-next-pilot.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: v6, 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:pilot — single-tick autonomous build-loop conductor

A tick is one invocation of /flow-next:pilot: select one ready spec, classify its current stage (plan, plan-review, work, the opt-in qa, make-pr), dispatch exactly one existing stage skill, verify state advanced, and end with one terminal PILOT_VERDICT line. It is intentionally not a runner; /loop in Claude Code or /goal in Claude Code / Codex owns repeated invocation.

Pilot and Ralph are alternative autonomous drivers. Ralph is an external shell loop with receipt plumbing; pilot is an in-session conductor for host loop primitives. Never nest them, and never reuse Ralph harness state inside pilot.

Human judgment lives before pilot: the spec content, depends_on_epics, and the fn-58 ready gate are the consent boundary. Pilot executes the mechanical pipeline one stage at a time, with ambiguity reported as NEEDS_HUMAN.

Chart is outside the build loop (fn-135)

Chart is optional pre-capture discovery - never a pilot pipeline stage. Pilot never advances a chart tick. Optional unattended chart driving (host /loop on /flow-next:chart) stops terminally at attended decisions (CHART_VERDICT=NEEDS_HUMAN) - pilot does not absorb or continue that work. Capture, interview, and chart stay human-gated upstream of the ready consent boundary.

Preamble

CRITICAL: flowctl is BUNDLED — NOT installed globally. which flowctl will fail (expected). Define once; subsequent blocks (here and in workflow.md) use $FLOWCTL. Subagents that run in fresh context fall back to the repo-local copy:

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"

Cold session / tick start: $FLOWCTL brief first for session-scope orientation (one budgeted call).

Hard guards (before anything else)

Run these guards before selection, ledger writes, branch changes, or skill dispatch.

if [[ -n "${FLOW_RALPH:-}" || -n "${REVIEW_RECEIPT_PATH:-}" ]]; then
  echo "Ralph and pilot are alternative drivers — never nest them" >&2
  echo 'PILOT_VERDICT=NEEDS_HUMAN spec=- stage=- reason="nested under Ralph harness (FLOW_RALPH/REVIEW_RECEIPT_PATH set) — refuse to run"'
  exit 1
fi

if git status --porcelain | grep -v '^.. \.flow/' >/dev/null; then
  echo 'PILOT_VERDICT=NEEDS_HUMAN spec=- stage=- reason="dirty working tree at tick start"'
  exit 0
fi

Dirty tree means dirty outside .flow/; pilot leaves state untouched. No cleanup, no claim reset, no strike.

Mode Detection

Parse $ARGUMENTS for the scope lock, dry-run switch, and passthroughs. Unknown flags warn to stderr and are ignored. Defaults are research=grep, depth=short, and review resolved later via $FLOWCTL review-backend.

The loop handles both --flag=value and space-separated --flag value forms directly via a PREV token holder. It deliberately avoids bash positional parameters (shift-based parsing) — the host's argument interpolation rewrites positional tokens inside skill code blocks, which corrupts a case-on-positionals parse (observed live in the 1.13.0 dogfood).

RAW_ARGS="$ARGUMENTS"
PILOT_SPEC=""
PILOT_DRY_RUN=0
PILOT_REVIEW=""
PILOT_RESEARCH="grep"
PILOT_DEPTH="short"
PILOT_BACKLOG_OVERRIDE=""   # "" = use config; "1" = force backlog (--backlog/--auto)

PREV=""
for ARG in $RAW_ARGS; do
  case "$PREV" in
    --spec)     PILOT_SPEC="$ARG"; PREV=""; continue ;;
    --review)   PILOT_REVIEW="$ARG"; PREV=""; continue ;;
    --research) PILOT_RESEARCH="$ARG"; PREV=""; continue ;;
    --depth)    PILOT_DEPTH="$ARG"; PREV=""; continue ;;
  esac
  case "$ARG" in
    --spec|--review|--research|--depth) PREV="$ARG" ;;
    --spec=*)     PILOT_SPEC="${ARG#--spec=}" ;;
    --dry-run)    PILOT_DRY_RUN=1 ;;
    --backlog|--auto) PILOT_BACKLOG_OVERRIDE=1 ;;
    --review=*)   PILOT_REVIEW="${ARG#--review=}" ;;
    --research=*) PILOT_RESEARCH="${ARG#--research=}" ;;
    --depth=*)    PILOT_DEPTH="${ARG#--depth=}" ;;
    -*) echo "Unknown flag: $ARG (ignored by /flow-next:pilot)" >&2 ;;
    *)  echo "Unknown argument: $ARG (ignored by /flow-next:pilot)" >&2 ;;
  esac
done
[[ -n "$PREV" ]] && echo "Flag $PREV given without a value (ignored by /flow-next:pilot)" >&2
export PILOT_SPEC PILOT_DRY_RUN PILOT_REVIEW PILOT_RESEARCH PILOT_DEPTH PILOT_BACKLOG_OVERRIDE

No branch flag exists in v1. Branch resolution is pilot-owned from the selected spec's branch_name.

Autonomy mode resolution (R1) — gate the wide backlog behavior

Resolve PILOT_AUTONOMY once, here, so every downstream block keys off a single value. This block also captures the tick's ROOT CONFIG SNAPSHOT (fn-110) — the ONLY config get invocation across all pilot files (SKILL.md, workflow.md, references/backlog-mode.md): workflow.md's pipeline.qa probe and the pilot.gateClasses reads derive from the snapshot file via jq, never a second config call. The gate is a strict scalar string-enum — backlog mode activates only on the literal backlog (config pilot.autonomy), or when the per-run --backlog / --auto flag forced the override. Any other config value (ready, null, a coerced bool true, a typo) leaves pilot in ready mode — byte-for-byte unchanged behavior (the references/backlog-mode.md file is never even read):

# Root config snapshot: {"key":null,"value":{<merged config>}}. Persisted to a file
# because bash vars do not survive across prompt turns; later fences RECOMPUTE this
# same deterministic repo-hash-keyed path and jq it. The path lives under
# ${TMPDIR} — NEVER under repo-controlled .flow/tmp (autonomous symlink safety:
# a committed symlink must not redirect this write out of tree) — so a dry-run
# tick mutates nothing inside the repo; dry-run terminals also `rm -f` this
# snapshot (workflow.md), leaving no persistent scratch state. On capture
# FAILURE remove the file —
# downstream jq reads then error, which keeps the pipeline.qa probe's fail-open
# contract (probe error ⇒ ACTIVE) intact.
PILOT_CFG_SNAPSHOT="${TMPDIR:-/tmp}/flow-pilot-config-$(git rev-parse --show-toplevel 2>/dev/null | cksum | cut -d' ' -f1).json"
rm -f "$PILOT_CFG_SNAPSHOT" 2>/dev/null   # drop any stale/planted file (incl. a symlinked leaf) before the fresh write
$FLOWCTL config get --json > "$PILOT_CFG_SNAPSHOT" 2>/dev/null \
  || rm -f "$PILOT_CFG_SNAPSHOT"
PILOT_AUTONOMY="$(jq -r '.value.pilot.autonomy' "$PILOT_CFG_SNAPSHOT" 2>/dev/null)"
if [ "$PILOT_BACKLOG_OVERRIDE" = "1" ]; then
  PILOT_AUTONOMY="backlog"                       # --backlog / --auto forces backlog this run
elif [ "$PILOT_AUTONOMY" != "backlog" ]; then
  PILOT_AUTONOMY="ready"                         # ONLY the literal `backlog` enables — never bool true / typos / null
fi
export PILOT_AUTONOMY

When PILOT_AUTONOMY=ready (the default), pilot behaves exactly as documented in workflow.md Phases 1–6 — no backlog-mode code path runs and references/backlog-mode.md is not loaded. When PILOT_AUTONOMY=backlog, read references/backlog-mode.md top to bottom, execute its backlog-only setup, then continue with workflow.md Phase 1. The reference owns the backlog-only verdict extension plus SELECT/TRIAGE/ASK context; workflow.md keeps the enforcing guards and action sites.

The verdict contract (read this before the workflow)

The /goal validator is transcript-blind: it reads conversation output only and never runs tools. Every tick therefore echoes its verification evidence into the output: flowctl status fields, task counts, task status transitions, and the gh-confirmed PR URL for make-pr.

Every tick ends with exactly one terminal line, the last line of the response, with nothing after it. The common ready-mode grammar is:

PILOT_VERDICT=<ADVANCED|NO_WORK|DEFERRED_TO_LAND|BLOCKED|NEEDS_HUMAN> spec=<id> stage=<stage> reason="<one line>"

Use spec=- and stage=- when no spec was selected. Stage values are exactly plan, plan-review, work, qa (opt-in — only when pipeline.qa==on), make-pr, land, or -.

Dry-run snapshot cleanup. Under --dry-run (PILOT_DRY_RUN=1), at EVERY terminal PILOT_VERDICT emission — the classification stop, the diagnostic TRIAGED exit, every NO_WORK / DEFERRED_TO_LAND / hard-guard exit — remove the root config snapshot BEFORE printing the verdict, so a dry-run leaves no persistent scratch state:

rm -f "${TMPDIR:-/tmp}/flow-pilot-config-$(git rev-parse --show-toplevel 2>/dev/null | cksum | cut -d' ' -f1).json"

Recompute the path exactly as above (vars die across prompt turns). Live (non-dry-run) ticks keep the snapshot for the tick's remaining fences; it is overwritten fresh by the next tick's capture. Never blocks, fail-open (rm -f on a missing file is a no-op).

DEFERRED_TO_LAND is a distinct non-terminal-work verdict (stage land): every remaining all-done candidate has an open PR that land — not pilot — owns. It is deliberately separated from NO_WORK so a driver can route it to /flow-next:land instead of stopping; an all-done spec with an open PR is real outstanding work, never absence of work.

Driver condition examples:

/goal keep running /flow-next:pilot until it prints PILOT_VERDICT=NO_WORK, or stop after 20 turns
/goal keep running /flow-next:pilot --review=codex until PILOT_VERDICT=NO_WORK or PILOT_VERDICT=NEEDS_HUMAN

Forbidden

Asking the user anything in the tick path. Pilot is autonomous; ambiguity maps to NEEDS_HUMAN. In backlog mode, ambiguity that needs a person is surfaced async via the ask stage (ASKED) — never an interactive plain-text numbered prompt.
Dispatching any skill outside the stage set {plan, plan-review, work, make-pr} - plus qa only when pipeline.qa==on (fn-72: an opt-in, gate-reversed stage at the all-done juncture before make-pr; with the gate off, qa is forbidden and the stage set is byte-for-byte unchanged). Backlog mode (PILOT_AUTONOMY=backlog) additionally invokes /flow-next:tracker-sync for the reconcile / list-open / list-comments / list-relations / question ops - these are read/surface-only tracker calls (list-comments reads parked question rounds; list-relations reads dependency relations for dep-ordering), not a pipeline stage, and run only on the backlog path. Capture, interview, chart, resolve-pr, merge, and release are never pilot stages - they stay forbidden for their distinct reasons (capture/interview/chart are human authoring and discovery upstream of the consent boundary; resolve-pr/merge/release are land's territory downstream of the PR). Opening qa under its gate, or tracker-sync under backlog mode, sets no precedent for any of those — a tick that dispatched one of them by analogy has broken this.
Re-implementing sub-skill logic. Pilot owns selection, dispatch, verification, verdicts, and the strikes ledger only. The backlog-mode SELECT/TRIAGE/ASK workflow lives in references/backlog-mode.md (loaded only when PILOT_AUTONOMY=backlog); the question-anchor authoring + answer round-trip live in tracker-sync — backlog mode invokes them, never re-implements them.
Never merging / never invoking land (R6) — in either mode, the terminus is make-pr (draft). Merge stays human-gated. Backlog mode never calls /flow-next:land, gh pr merge, or any merge path.
Never authoring a spec (backlog mode) — capture/interview are human-gated upstream. A missing/too-thin spec is surfaced as a "needs capture/interview" gap and parked (ASKED), never auto-written. The only writing the ask stage may do is fill an obvious blank in an existing spec — never create a spec stub from a bare ticket.
Touching gh anywhere except the all-done classification branch's PR probe and the make-pr verification probe.
Printing anything after the PILOT_VERDICT line.
Running under Ralph (FLOW_RALPH / REVIEW_RECEIPT_PATH).

Workflow

Execute workflow.md in order:

guards — refuse Ralph nesting, refuse dirty non-.flow/ start state, resolve the .git strikes ledger (read-only at this point). Done when: both guards have passed and LEDGER_JSON is loaded without a write.
select — two-pass ready-spec selection with dependency, claim, and re-bless checks. Done when: exactly one spec is selected, or the pool is empty and the terminal split has been chosen.
classify — derive one stage from flowctl state; probe gh only in the all-done branch. Done when: exactly one stage from the allowed set is named, with the consulted status fields echoed.
branch — resolve the spec branch matrix before work or make-pr. Done when: the worktree sits on the branch the matrix row names, or the tick has stopped NEEDS_HUMAN on a failed checkout.
dispatch — invoke exactly one stage skill with mode:autonomous plus review/research/depth passthroughs. Done when: one stage skill has been invoked and returned.
verify — re-read flowctl state, or gh for make-pr, and echo before/after evidence. Done when: the before/after evidence block plus the stage-outcome line are in the transcript and advanced is decided from observed state.
report — clear or record strikes, optionally unready on the second healthy no-advance tick, and print the terminal verdict. Done when: the ledger reflects this tick and the terminal PILOT_VERDICT line is the last line of the response.

Backlog mode (PILOT_AUTONOMY=backlog). When the autonomy gate resolved to backlog, the SELECT and TRIAGE/ASK behavior follows references/backlog-mode.md — the agentic floor scheduler (loaded only in this mode). It widens SELECT (pull-before-scan, union tracker-only items, dep-order, skip parked) and adds the triage/ask stages in front of CLASSIFY; a workable item flows into the existing classify → branch → dispatch → verify path unchanged. workflow.md Phase 0.5 resolves the mode and routes; workflow.md Phase 1.5/Phase 3.5 carry the backlog SELECT + TRIAGE/ASK hooks and the safety invariants. The single-tick contract is unchanged: one item, one stage or one durable park, one terminal verdict.

Unattended runs — rp caveat

The rp review backend runs headlessly through the CE-first CLI ladder — it needs RepoPrompt CE running on the same Mac (cold start: open -ga "RepoPrompt CE"; a stopped app fails fast). Classic is only the final compatibility fallback. On remote/CI machines, use --review=codex, --review=copilot, --review=cursor, --review=host (host-native subagent; needs a cross-family pin in AGENTS.md model-routing), or --review=none. Wall-clock limits and iteration caps belong to the driver (/goal --tokens, /goal stop clauses, or /loop cadence); a pilot tick has no timeout machinery.

When it activates

Automatically, when a task matches. Triggers on /flow-next:pilot, optionally with --spec <id>, --dry-run, --review=<backend>, --research=<grep|rp>, --depth=<level>. Autonomous by design — never asks the user questions; reports NEEDS_HUMAN instead.

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

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

gmickel / flow-next

plugins/flow-next/skills/flow-next-pilot/SKILL.md

View on GitHub ↗

License MITImported 8/17/2026Version v6Bundled 2 files

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

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

/flow-next:pilot — single-tick autonomous build-loop conductor

Chart is outside the build loop (fn-135)

Preamble

Hard guards (before anything else)

Mode Detection

Autonomy mode resolution (R1) — gate the wide backlog behavior

The verdict contract (read this before the workflow)

Forbidden

Workflow

Unattended runs — rp caveat

When it activates

SkillSafety

Source

Related skills

More in the registry