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Skill Pipeline · pskoett | DecimalAI

Agent Toolingby pskoett

Skill Pipeline

Pipeline orchestrator that classifies incoming coding tasks and routes them through the correct combination of skills at the right depth. Implements two feedback loops: the inner loop (detect, verify, recover) runs within a session via plan-interview, intent-framed-agent, session-context, context-surfing, verify-gate, self-healing (active recovery on failure), simplify-and-harden, and self-improvement. The outer loop (inspect, encode, regress-test) runs across sessions via learning-aggregator, s

Never made a case worseMaintained

SkillScore

SkillScore84/100

Test results 84

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

24%84%of test cases passed +60 pts

Without skill6 of 25

With skill21 of 25

15 fixed6 passed either way4 still failing0 broke

Measured on gemini-3.8-flash11/2/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe are wrapping up a Medium task that passed verify-gate with a non-trivial diff, but the user passed `--no-review`. Which stage must be omitted?

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 pskoett-skill-pipeline
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("pskoett-skill-pipeline")
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 pskoett-skill-pipeline

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/pskoett-skill-pipeline/SKILL.md https://app.decimal.ai/s/pskoett-skill-pipeline/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/pskoett-skill-pipeline/SKILL.md

This gets SKILL.md only. The skill's 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/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/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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("pskoett-skill-pipeline")
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/pskoett-skill-pipeline/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/pskoett-skill-pipeline/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 3 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 pskoett-skill-pipeline

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/pskoett-skill-pipeline/SKILL.md https://app.decimal.ai/s/pskoett-skill-pipeline/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/pskoett-skill-pipeline/SKILL.md

This gets SKILL.md only. The skill's 3 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/pskoett-skill-pipeline/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/pskoett-skill-pipeline.json

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 11/2/2026
Source: pskoett/pskoett-ai-skills ↗
License: Not recorded

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

Skill Pipeline

The conductor, not a player. This skill classifies tasks, selects the pipeline variant, calibrates depth, and orchestrates handoffs between skills. It produces no artifacts of its own — its output is routing decisions that activate other skills.

Task Classification

On every coding task, classify before acting. Evaluate scope signals and map to a task class.

Input signals: file count, task description, existing plan/handoff files, batch indicators, CI environment.

Task received
  │
  ├─ Trivial (typo, rename, version bump)
  │  → No skills. Just do it.
  │
  ├─ Small (isolated fix, single-file, <10 logic lines)
  │  → verify-gate + simplify-and-harden
  │
  ├─ Medium (feature in known area, 2-5 files)
  │  → intent-framed-agent + session-context + verify-gate + simplify-and-harden
  │
  ├─ Large (complex refactor, new architecture, unfamiliar codebase, high-risk logic)
  │  → Full standard pipeline
  │  → Recommend /plan-interview before starting
  │
  ├─ Long-running (multi-session, high context pressure, prior handoff exists)
  │  → Full standard pipeline with context-surfing as critical skill
  │
  └─ Batch (multiple features from spec, 5+ discrete tasks, issue triage)
     → Orchestrated standard pipeline with bounded work units and independent audits

When uncertain, start with Medium. Add skills if drift or quality issues appear mid-task.

For detailed heuristics, edge cases, and examples: read references/classification-rules.md.

Pipeline Selection

Route task class to the right variant:

Task Class	Variant	Rationale
Trivial	None	No overhead needed
Small	Standard (minimal)	Verify + S&H only
Medium	Standard (partial)	Intent + session context + verify + review
Large	Standard (full)	Full inner loop with planning
Long-running	Standard (full)	Context-surfing is critical
Batch	Orchestrated standard	Bounded work units with per-unit gates
CI environment	CI	Headless review
Periodic	Outer loop	Cross-session improvement

Heuristic: Use the standard pipeline for both depth and breadth. For batch work, split the scope into bounded units and use control-session-orchestrator only when persistent multi-session coordination is available and useful. CI pipeline applies when CI=true or GITHUB_ACTIONS=true.

Activation Sequences

Standard Pipeline (Inner Loop)

pre-flight-check (SessionStart hook — surfaces prior learnings + heals)
  → classify
  → (recommend /plan-interview if Large or Long-running)
  → intent-framed-agent (clarify material gaps; emit initial frame)
  → session-context (invoked by intent framing; bounded history retrieval)
  → finalize approach in the same intent frame
  → context-surfing (Large/Long-running or explicit high-context-pressure use; concurrent with intent monitoring)
  → [IMPLEMENTATION]
  → self-healing  ← inner-loop recovery primitive; called whenever a command/test/build/external call fails or a helper is missing.
  →                Diagnoses, patches, verifies, files HEAL- entry. Resumes when verified.
  → verify-gate (compile + test + lint; fix loop if red — fix loop calls self-healing)
  → simplify-and-harden (post-completion, if non-trivial diff)
  → self-improvement (on errors, corrections, S&H learning candidates, recurring heal handoffs)

Skill-by-class activation:

Skill	Trivial	Small	Medium	Large	Long-running
pre-flight-check	Hook	Hook	Hook	Hook	Hook
plan-interview	-	-	-	Recommend	Recommend
intent-framed-agent	-	-	Activate	Activate	Activate
session-context	-	On request	After frame	After frame	After frame; reuse on resume
context-surfing	-	-	-	Activate	Critical
verify-gate	-	Activate	Activate	Activate	Activate
self-healing	On failure	On failure	On failure	On failure	On failure
simplify-and-harden	-	If non-trivial	If non-trivial	If non-trivial	If non-trivial
self-improvement	On error only	On error only	On error/completion	On error/completion	On error/completion

intent-framed-agent owns the call to session-context; these are ordered steps, not two independent triggers. A missing history source or no useful matches must not block implementation. On resume, reuse prior findings and retrieve only for a concrete new gap. This routing is agent-followed guidance, not a startup hook.

Orchestrated Batch Pipeline

classify (Batch)
  → (recommend /plan-interview if no spec exists)
  → split the scope into bounded work units
  → control-session-orchestrator when persistent multi-session coordination is available
    ├─ each unit follows its appropriate standard-pipeline depth
    ├─ verify-gate after each implementation unit
    ├─ independent simplify, harden, and spec auditors review the combined result
    └─ verify-gate after audit-driven fixes
  → self-improvement

If persistent orchestration is unavailable, process the same bounded units sequentially. Do not weaken verification, approval, or independent-review gates because parallel execution is unavailable.

CI Pipeline

classify (CI detected)
  → simplify-and-harden-ci (headless scan, PR changed files only)
  → self-improvement-ci (pattern aggregation, promotion recommendations)

Outer Loop Pipeline

The outer loop runs across sessions, not within them. Trigger on cadence (weekly, sprint boundary) or when pre-flight-check surfaces promotion-ready patterns.

learning-aggregator (read .learnings/, find patterns, rank promotion candidates)
  → self-improvement (human-approved promotions to project instructions)
  → eval-creator (create permanent test cases from promoted patterns)
  → eval-creator run (regression check on all existing evals)

When to trigger the outer loop:

Weekly: recommended minimum cadence
Sprint boundary: after a burst of sessions
When pre-flight-check reports promotion-ready count > 3
After a significant incident or recurring failure
Manually: user invokes /learning-aggregator

Outer loop is always human-gated. learning-aggregator produces a gap report. The main agent follows self-improvement to show proposed diffs for approval before applying promotions. No automatic writes to instruction files without human review.

Depth Calibration

Not just which skills — how deep each goes:

Dimension	Small	Medium	Large	Long-running	Batch
Pre-flight check	Hook	Hook	Hook	Hook	Hook
Planning passes	0	0-1	1-2	Deep iterative	Per-task or umbrella
Intent frame	-	Single frame	Full frame + monitoring	Full + handoff	Umbrella invariants + per-unit frames
Session context	On request	Bounded brief	Bounded brief	Bounded brief; reuse on resume	Share relevant brief; per-unit gaps only
Context-surfing	-	-	Active	Critical (exit protocol ready)	Per Large/Long-running unit
Verify-gate	Compile + test	Compile + test	Compile + test + lint	Compile + test + lint	Per unit + combined result
Self-healing	On failure (file HEAL)	On failure (file HEAL)	On failure + recurrence check	On failure + recurrence check	On failure per unit
S&H budget	20% diff, 60s	20% diff, 60s	20% diff, 60s	20% diff, 60s	Per non-trivial unit
Independent review	-	-	Recommended	Required	Required on combined result
Self-improvement	Error-triggered	Error-triggered	Error + S&H feed	Error + S&H feed	Error + S&H feed

Handoff Rules

Artifacts flow between skills. The orchestrator ensures each skill receives what it needs.

Key handoffs:

Plan file (docs/plans/plan-NNN-<slug>.md) — produced by plan-interview, consumed by intent-framed-agent (context), context-surfing (wave anchor), and an optional control-session-orchestrator work-unit breakdown.
Intent Frame — produced by intent-framed-agent, consumed first by session-context (task and scope for retrieval), then by context-surfing (wave anchor strengthening). Useful history refines the approach in the same frame; it does not authorize changes. Copied into handoff files on drift exit.
Handoff file (.context-surfing/handoff-[slug]-[timestamp].md) — produced by context-surfing on drift exit, consumed by next session for resume.
Verify-gate signal — produced by verify-gate (pass/fail + diagnostics), consumed by simplify-and-harden (only activates after green gate) and the heal loop (on failure — verify-gate hands the diagnostics to self-healing, which diagnoses + patches + re-verifies, then signals verify-gate to re-check).
HEAL entries + artifacts (.learnings/HEALS.md, .learnings/heals/<HEAL-ID>/) — produced by self-healing, consumed by pre-flight-check (surfaces prior heals at session start by Pattern-Key / Active-Context), learning-aggregator (cross-session recurrence analysis), and self-improvement (when Handoff block flags promotion at Recurrence ≥ 3).
Learning candidates (learning_loop.candidates) — produced by simplify-and-harden, consumed by self-improvement for pattern tracking.
Learning entries (.learnings/*.md) — produced by self-improvement, consumed by learning-aggregator for cross-session analysis and by pre-flight-check at session start.
Gap report — produced by learning-aggregator, consumed by self-improvement for human-approved promotion and eval-creator for test case generation.
Eval cases (.evals/cases/*.md) — produced by eval-creator, consumed by regression runs and surfaced by pre-flight-check.

Precedence: If context-surfing and intent-framed-agent both fire simultaneously, context-surfing's exit takes precedence. Degraded context makes scope checks unreliable.

For the full artifact/signal/budget table: read references/handoff-matrix.md.

Decision Points

The orchestrator intervenes at these moments:

Task Arrival

Classify the task. Select pipeline variant and depth. Emit routing decision. If Large/Long-running, recommend /plan-interview. If Batch, define bounded work units and recommend orchestration only when the host supports it.

Plan Approval

When user approves a plan from plan-interview, flow directly into the execution stage — no separate "should I proceed?" prompt. Activate intent-framed-agent and emit a faithful Intent Frame, but reuse the plan approval rather than asking for a second confirmation. Ask only if the frame introduces a material decision, assumption, or scope change. Populate task tracking with checklist items.

Planning-to-Execution Transition

With or without plan-interview, activate intent-framed-agent for a clear implementation request. Ask only about unresolved material decisions, emit the initial frame, and let intent framing invoke session-context once. Finalize the approach using applicable evidence, preserving current approval and constraints. Do not add a blanket frame-confirmation gate or require an interview. Ask if the findings expose a material conflict; otherwise proceed to implementation.

Failure mid-implementation

A command, test, build, lint, or external call fails before verify-gate even runs (or any other mid-task gap appears — missing helper, env drift, API change). Route into self-healing: diagnose, patch, verify, file the HEAL entry. Resume execution from the working state. Most heals are recurrences — self-healing searches HEALS.md by Pattern-Key first.

Implementation Complete

Activate verify-gate to run compile, test, and lint checks. If any fail, route into self-healing for the diagnosis/patch/verify loop (up to 3 attempts per phase). After each heal, verify-gate re-runs the checks. Once all checks pass and the diff meets the non-trivial threshold (see references/classification-rules.md), activate simplify-and-harden. If the diff is trivial, signal completion directly after verify-gate passes.

Drift Detected

If context-surfing fires a drift exit, stop execution. Write handoff file. If the task was classified below Large, consider re-classifying upward for the next session.

Session Resume

Check for pending handoff files in .context-surfing/. If found, read the relevant file completely. Reuse an approved plan and intent unless material decisions remain, re-classify if needed, and resume from the recommended re-entry point. Reuse any existing session-context brief rather than repeating retrieval without a concrete new gap. Mark the handoff consumed only after re-entry succeeds.

Overrides

Users can override any routing decision:

Force depth: depth=small / depth=large — override classification
Force variant: variant=orchestrated / variant=standard — override pipeline selection
Skip review: --no-review — skip simplify-and-harden
Force planning: invoke /plan-interview on any task regardless of classification
Skip all skills: user says "just do it" on a non-trivial task — respect the override

Re-classification

Tasks can change class mid-execution. Watch for:

Escalation signals: scope expanded beyond original estimate, many more files affected than expected, context-surfing drift exit, intent-framed-agent detects significant scope change
De-escalation signals: task turns out simpler than planned, plan reveals minimal changes needed

When re-classification is warranted:

Note the signal that triggered re-classification
Adjust active skills (add or remove pipeline stages)
If escalating to Large: recommend /plan-interview if no plan exists
If de-escalating: drop unnecessary stages, proceed with lighter pipeline

Anti-Patterns

Do NOT re-implement skill logic. This skill classifies and dispatches. Each individual skill owns its own procedure.
Do NOT auto-invoke plan-interview. It is a human gate. Recommend it; let the user decide.
Do NOT create a third monitoring layer. During execution, intent-framed-agent monitors scope and context-surfing monitors quality. The orchestrator dispatches at decision points, then gets out of the way.
Do NOT override individual skill guardrails. If simplify-and-harden has a 20% budget cap, the orchestrator respects it.
Do NOT force skills on trivial tasks. The pipeline exists to help, not to slow down simple work.

Pipeline Variant Details

For complete step-by-step walkthroughs of each variant including hybrid scenarios and session resume: read references/pipeline-variants.md.

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

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0 critical · 0 warning · 0 info — scanner v8 — 10/26/2026

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live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

pskoett / pskoett-ai-skills

skills/skill-pipeline/SKILL.md

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Imported 10/26/2026Version v1Bundled 3 files

Illustrative Want to see what pskoett/skill-pipeline 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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Skills that do similar work, with their headline benchmark lift.

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SkillScore

Safety

What it changes

Track record

Use this skill

Skill Pipeline

Task Classification

Pipeline Selection

Activation Sequences

Standard Pipeline (Inner Loop)

Orchestrated Batch Pipeline

CI Pipeline

Outer Loop Pipeline

Depth Calibration

Handoff Rules

Decision Points

Task Arrival

Plan Approval

Planning-to-Execution Transition

Failure mid-implementation

Implementation Complete

Drift Detected

Session Resume

Overrides

Re-classification

Anti-Patterns

Pipeline Variant Details

SkillSafety

Source

Related skills

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