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Ralph init · gmickel | DecimalAI

Agent Toolingby gmickel

Ralph init

Scaffold repo-local Ralph autonomous harness under scripts/ralph/.

Maintained

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%77%of test cases passed +41 pts

Without skill8 of 22

With skill17 of 22

10 fixed7 passed either way4 still failing1 broke

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

Track record

Agent runs: 21; All time

See one task, with and without the skill

One test taskWe are setting up Ralph on Factory Droid. The project has no .factory/hooks.json file, but .factory/settings.json exists with a "hooks" key. Which target file should be edited?

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-ralph-init
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-ralph-init")
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-ralph-init

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

Copy SKILL.md

Save it in your project, as

.claude/skills/gmickel-flow-next-ralph-init/SKILL.md

This gets SKILL.md only. The skill's 9 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-ralph-init

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-ralph-init")
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-ralph-init/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-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/gmickel-flow-next-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/gmickel-flow-next-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/gmickel-flow-next-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/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-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/gmickel-flow-next-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/gmickel-flow-next-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/gmickel-flow-next-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/gmickel-flow-next-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/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-ralph-init/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 9 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-ralph-init

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-ralph-init")
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-ralph-init/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/gmickel-flow-next-ralph-init/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 9 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-ralph-init

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

Copy SKILL.md

Save it in your project, as

.agents/skills/gmickel-flow-next-ralph-init/SKILL.md

This gets SKILL.md only. The skill's 9 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-ralph-init/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/gmickel-flow-next-ralph-init.json

This gets SKILL.md only. The skill's 9 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

Ralph init

Scaffold or update repo-local Ralph harness. Opt-in only.

Preamble

The plugin root resolves once via the cross-platform env-var fallback (Droid uses DROID_PLUGIN_ROOT; Claude Code documents CLAUDE_PLUGIN_ROOT as its compat alias). Subsequent blocks use $PLUGIN_ROOT:

PLUGIN_ROOT="${CODEX_HOME:-$HOME/.codex}"

Rules

Every write lands under scripts/ralph/ in the current repo. A file written anywhere else has broken this.
If scripts/ralph/ already exists, offer to update (preserves config.env).
Copy templates from templates/ into scripts/ralph/ (includes ralphctl.py for pause/resume/stop/status).
Copy flowctl, flowctl.cmd, flowctl.py, flowctl_bootstrap.py, flowctl-help.txt, and the flowctl_tracker/ package (from $PLUGIN_ROOT/scripts/) and pick-python.sh (from $PLUGIN_ROOT/scripts/lib/) into scripts/ralph/ — flat, so the resolver lands at scripts/ralph/pick-python.sh where ralph.sh and the hook wrapper source it. A pick-python.sh that landed under scripts/ralph/lib/ has broken this.
Set executable bit on scripts/ralph/ralph.sh, scripts/ralph/ralph_once.sh, scripts/ralph/flowctl, and scripts/ralph/ralphctl.py.
Hook registration is agent-driven skill prose only. The plugin ships no hooks by default; you (the host agent) merge the guard entries into the project's host settings via Read+Edit, idempotently, without clobbering unrelated hooks. The boundary is hard: there is no flowctl subcommand for hook install/remove/status and no hook machinery in Python. A settings file whose hooks object now holds only flow-next entries, or a hook-install subcommand added to the CLI, has broken this.

Workflow

Resolve repo root: git rev-parse --show-toplevel
Check if scripts/ralph/ exists:
- If exists: ask "Update existing Ralph setup? (preserves config.env and runs/) [y/n]"
  - If no: stop
  - If yes: set UPDATE_MODE=1
- If not exists: set UPDATE_MODE=0

Detect available review backends (skip if UPDATE_MODE=1):

if command -v rpce-cli >/dev/null 2>&1 \
  || [ -x "$HOME/RepoPrompt/repoprompt_ce_cli" ] \
  || [ -x "$HOME/Library/Application Support/RepoPrompt CE/repoprompt_ce_cli" ] \
  || command -v rp-cli >/dev/null 2>&1; then HAVE_RP=1; else HAVE_RP=0; fi
HAVE_CODEX=$(which codex >/dev/null 2>&1 && echo 1 || echo 0)
HAVE_COPILOT=$(which copilot >/dev/null 2>&1 && echo 1 || echo 0)
HAVE_CURSOR=$(which cursor-agent >/dev/null 2>&1 && echo 1 || echo 0)

Determine review backend (skip if UPDATE_MODE=1):
- If several are available, ask the user. Show only the options whose CLIs were detected:
```
Multiple review backends available. Which one?
a) RepoPrompt (macOS, visual builder)
b) Codex CLI (cross-platform, reviewer models via a Codex subscription)
c) GitHub Copilot CLI (cross-platform, Claude/GPT via Copilot)
d) Cursor CLI (cross-platform, runs cursor-agent; reviewer models via a Cursor subscription)

(Reply: "a", "rp", "b", "codex", "c", "copilot", "d", "cursor", or just tell me)
```
  Wait for response. Default if empty/ambiguous: prefer rp > codex > copilot > cursor.
- If only the RepoPrompt CLI ladder resolves: use rp
- If only codex available: use codex
- If only copilot available: use copilot
- If only cursor-agent available: use cursor
- If none available: use none

Copy files using bash — the copies run through cp, never the Write tool. A template reproduced by writing its contents has broken this:

If UPDATE_MODE=1 (updating):

# Backup config.env
cp scripts/ralph/config.env /tmp/ralph-config-backup.env

# Update templates (preserves runs/)
cp "${CODEX_HOME:-$HOME/.codex}/templates/flow-next-ralph-init/ralph.sh" scripts/ralph/
cp "${CODEX_HOME:-$HOME/.codex}/templates/flow-next-ralph-init/ralph_once.sh" scripts/ralph/
cp "${CODEX_HOME:-$HOME/.codex}/templates/flow-next-ralph-init/prompt_plan.md" scripts/ralph/
cp "${CODEX_HOME:-$HOME/.codex}/templates/flow-next-ralph-init/prompt_work.md" scripts/ralph/
cp "${CODEX_HOME:-$HOME/.codex}/templates/flow-next-ralph-init/prompt_completion.md" scripts/ralph/
cp "${CODEX_HOME:-$HOME/.codex}/templates/flow-next-ralph-init/watch-filter.py" scripts/ralph/
cp "${CODEX_HOME:-$HOME/.codex}/templates/flow-next-ralph-init/ralphctl.py" scripts/ralph/
cp "$PLUGIN_ROOT/scripts/flowctl" "$PLUGIN_ROOT/scripts/flowctl.cmd" "$PLUGIN_ROOT/scripts/flowctl.py" "$PLUGIN_ROOT/scripts/flowctl_bootstrap.py" "$PLUGIN_ROOT/scripts/flowctl-help.txt" "$PLUGIN_ROOT/scripts/lib/pick-python.sh" scripts/ralph/
rm -rf scripts/ralph/flowctl_tracker && cp -R "$PLUGIN_ROOT/scripts/flowctl_tracker" scripts/ralph/flowctl_tracker
# fn-139.5: verify the tracker package post-copy - fail loudly here, never
# later as an ImportError mid-run
python3 "$PLUGIN_ROOT/scripts/lib/verify_tracker_manifest.py" scripts/ralph
mkdir -p scripts/ralph/hooks
cp "$PLUGIN_ROOT/scripts/hooks/ralph-guard.py" "$PLUGIN_ROOT/scripts/hooks/ralph-guard" scripts/ralph/hooks/
chmod +x scripts/ralph/ralph.sh scripts/ralph/ralph_once.sh scripts/ralph/flowctl scripts/ralph/ralphctl.py scripts/ralph/hooks/ralph-guard.py scripts/ralph/hooks/ralph-guard

# Restore config.env
cp /tmp/ralph-config-backup.env scripts/ralph/config.env

If UPDATE_MODE=0 (fresh install):

mkdir -p scripts/ralph/runs scripts/ralph/hooks
cp -R "${CODEX_HOME:-$HOME/.codex}/templates/flow-next-ralph-init/." scripts/ralph/
cp "$PLUGIN_ROOT/scripts/flowctl" "$PLUGIN_ROOT/scripts/flowctl.cmd" "$PLUGIN_ROOT/scripts/flowctl.py" "$PLUGIN_ROOT/scripts/flowctl_bootstrap.py" "$PLUGIN_ROOT/scripts/flowctl-help.txt" "$PLUGIN_ROOT/scripts/lib/pick-python.sh" scripts/ralph/
rm -rf scripts/ralph/flowctl_tracker && cp -R "$PLUGIN_ROOT/scripts/flowctl_tracker" scripts/ralph/flowctl_tracker
# fn-139.5: verify the tracker package post-copy - fail loudly here, never
# later as an ImportError mid-run
python3 "$PLUGIN_ROOT/scripts/lib/verify_tracker_manifest.py" scripts/ralph
cp "$PLUGIN_ROOT/scripts/hooks/ralph-guard.py" "$PLUGIN_ROOT/scripts/hooks/ralph-guard" scripts/ralph/hooks/
chmod +x scripts/ralph/ralph.sh scripts/ralph/ralph_once.sh scripts/ralph/flowctl scripts/ralph/ralphctl.py scripts/ralph/hooks/ralph-guard.py scripts/ralph/hooks/ralph-guard

Note: cp -R templates/. copies all files including dotfiles (.gitignore).

Edit scripts/ralph/config.env to set the chosen review backend (skip if UPDATE_MODE=1):
- Replace PLAN_REVIEW={{PLAN_REVIEW}} with PLAN_REVIEW=<chosen>
- Replace WORK_REVIEW={{WORK_REVIEW}} with WORK_REVIEW=<chosen>
- Replace COMPLETION_REVIEW={{COMPLETION_REVIEW}} with COMPLETION_REVIEW=<chosen>

Register project hooks (agent-driven; required for the guard to fire).

Detect host (same signals as /flow-next:setup Step 0 when available; otherwise probe the settings paths below). Then Read the target file, merge the flow-next Ralph guard entries, Edit/Write the result. The host's existing hooks survive the merge — a settings file whose hooks object now contains only flow-next entries has broken this. Idempotent: if an entry's command already contains scripts/ralph/hooks/ralph-guard, leave that matcher group alone (or refresh the command string to the canonical form below if it drifted).

Fingerprint for "this is a flow-next Ralph guard entry": the hook command string contains scripts/ralph/hooks/ralph-guard (wrapper and/or .py fallback).

Canonical guard command (same on every host that can run bash wrappers):

if [ -f scripts/ralph/hooks/ralph-guard ]; then bash scripts/ralph/hooks/ralph-guard; elif [ -f scripts/ralph/hooks/ralph-guard.py ]; then scripts/ralph/hooks/ralph-guard.py; fi

Timeout: 5 seconds. Type: command.

Claude Code → merge into `.claude/settings.json`

Target: project file .claude/settings.json (create {"hooks":{}} skeleton if missing; preserve every non-hooks key).

Merge these four event groups under hooks (Claude schema). Matchers use regex OR so Droid interop and Claude share one entry shape:

Event	Matcher	Notes
`PreToolUse`	`Bash\|Execute`	shell (Claude `Bash`, Droid `Execute`)
`PreToolUse`	`Edit\|Write`	file tools (Claude host names)
`PostToolUse`	`Bash\|Execute`	shell
`PostToolUse`	`Edit\|Write`	file tools (receipt-path gate parity)
`Stop`	(no matcher)	stop gate
`SubagentStop`	(no matcher)	subagent stop gate

Each event's array entry is one matcher group with a single hook object {type, command, timeout} using the canonical command above.

Consent gate: Claude Code's project-hooks trust prompt is the human consent surface. Do not invent a second consent ceremony. After merge, tell the user they may need to accept/trust project hooks in the host UI for them to load this session.

Factory Droid → merge into `.factory/hooks.json`

Target (verified against Factory hooks-reference): project file .factory/hooks.json. Prefer that path. Fallback only if the project already stores hooks under the hooks key of .factory/settings.json and has no .factory/hooks.json — merge there instead; never invent a third path.

Host-appropriate matchers for Droid (Factory's shell tool is Execute; file tools include Create / ApplyPatch). The guard body accepts the full dual-platform sets (Bash/Execute, Edit/Write/Create/ApplyPatch).

Event	Matcher	Notes
`PreToolUse`	`Bash\|Execute`	shell
`PreToolUse`	`Edit\|Write\|Create\|ApplyPatch`	Droid file tools
`PostToolUse`	`Bash\|Execute`	shell
`PostToolUse`	`Edit\|Write\|Create\|ApplyPatch`	file tools (receipt-path gate)
`Stop`	(no matcher)	stop gate
`SubagentStop`	(no matcher)	subagent stop gate

Prefer project-relative command as above (Ralph harness is repo-local). If the host requires absolute paths, rewrite with "$FACTORY_PROJECT_DIR"/scripts/ralph/hooks/... but keep the same fingerprint substring scripts/ralph/hooks/ralph-guard.

Codex → write/merge project `.codex/hooks.json`

Codex has no Claude-schema plugin hooks auto-load from the marketplace plugin. Project scope is .codex/hooks.json.

Codex subset (no SubagentStop; no Edit/Write matchers — Codex only intercepts shell):

Event	Matcher
`PreToolUse`	`Bash\|Execute`
`PostToolUse`	`Bash\|Execute`
`Stop`	(no matcher)

The top-level JSON is exactly {"hooks":{...}} — a sibling description key has broken this (Codex rejects unknown fields and disables all hooks).

If .codex/config.toml exists, ensure exactly one hooks = true under [features] (drop deprecated codex_hooks). Same normalization intent as setup's historical Codex hooks step; do it with a careful edit, not a second copy of setup's python block unless you need it.

Cursor / Grok

Cursor: Ralph hooks are unsupported (Cursor hook schema is afterFileEdit / beforeShellExecution). Scaffold scripts/ralph/ only; print that the guard will not fire on Cursor; do not invent a Cursor-format hook file.
Grok Build: reads Claude-compat plugin/project surfaces; use the Claude Code path (.claude/settings.json).

Re-run / update

On UPDATE_MODE=1 still re-merge hooks so a project that had scaffold but lost settings entries is repaired. Skip only when every required event already has a fingerprinted entry with the canonical command.

Done when: the host's registered event set matches its platform (Codex without SubagentStop or file-tool matchers; Droid with its own file-tool matchers; Cursor scaffold-only plus the printed will-not-fire note), every entry carries the canonical command, and unrelated hooks are still present.

Print next steps — the run ends by printing them, never by starting the loop inside the session. A session that executed ralph.sh has broken this:

If UPDATE_MODE=1:

Ralph updated! Your config.env was preserved.

Hooks: project settings were re-merged (idempotent). Accept the host's
project-hooks trust prompt if it appears.

Run from terminal:
- ./scripts/ralph/ralph_once.sh (one iteration, observe)
- ./scripts/ralph/ralph.sh (full loop, AFK)
- ./scripts/ralph/ralphctl.py status|pause|resume|stop  (run control; not flowctl)

If UPDATE_MODE=0:

Ralph initialized!

Next steps (run from terminal, NOT inside the agent session):
- Accept project-hooks trust if the host prompts (required once)
- Edit scripts/ralph/config.env to customize settings
- ./scripts/ralph/ralph_once.sh (one iteration, observe)
- ./scripts/ralph/ralph.sh (full loop, AFK)
- ./scripts/ralph/ralphctl.py status|pause|resume|stop  (run control; not flowctl)

Maintenance:
- Re-run /flow-next:ralph-init after plugin updates to refresh scripts + re-merge hooks
- Uninstall: /flow-next:uninstall removes hook entries; then manually rm -rf scripts/ralph/ if desired

When it activates

Automatically, when a task matches. Use when user runs /flow-next:ralph-init.

About this skill

Automatic

AI Engineer 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-ralph-init/SKILL.md

View on GitHub ↗

License MITImported 8/17/2026Version v6Bundled 9 files

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

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From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

Ralph init

Preamble

Rules

Workflow

Claude Code → merge into .claude/settings.json

Factory Droid → merge into .factory/hooks.json

Codex → write/merge project .codex/hooks.json

Cursor / Grok

Re-run / update

When it activates

SkillSafety

Source

Related skills

More in the registry

Claude Code → merge into `.claude/settings.json`

Factory Droid → merge into `.factory/hooks.json`

Codex → write/merge project `.codex/hooks.json`