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Paranoia AI System Evolver · dy-2026 | DecimalAI

Agent Toolingby dy-2026

Paranoia AI System Evolver

用于升级 AI 系统、agent workflow、Codex skill、prompt、memory、RAG、tool routing、schema、eval set 或 feedback loop；也用于把 AI 工作单从指令单升级为意图单，并对研究、检索、测试和 AI 对话做 VOI 决策门审计。需要 Intent Work Order、WOOP 任务准入、决策对象、VOI/EVPI/EVSI、UL（Uncertainty Ladder，不确定性阶梯）、OODA、eval、Human Gate、versioning 与 rollback 的受控演化时使用。

Never made a case worseMaintained

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

41%77%of test cases passed +36 pts

Without skill9 of 22

With skill17 of 22

8 fixed9 passed either way5 still failing0 broke

Measured on gemini-3.6-flash8/21/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe are tempted to run 50 exploratory agent sub-branches to evaluate new CSS styling frameworks for our landing page. Formulate a Decision Object to analyze whether this research step should proceed.

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 dy-2026-paranoia-ai-system-evolver
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("dy-2026-paranoia-ai-system-evolver")
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 dy-2026-paranoia-ai-system-evolver

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/dy-2026-paranoia-ai-system-evolver/SKILL.md https://app.decimal.ai/s/dy-2026-paranoia-ai-system-evolver/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/dy-2026-paranoia-ai-system-evolver/SKILL.md

This gets SKILL.md only. The skill's 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/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/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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("dy-2026-paranoia-ai-system-evolver")
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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/dy-2026-paranoia-ai-system-evolver/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 42 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 dy-2026-paranoia-ai-system-evolver

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/dy-2026-paranoia-ai-system-evolver/SKILL.md https://app.decimal.ai/s/dy-2026-paranoia-ai-system-evolver/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/dy-2026-paranoia-ai-system-evolver/SKILL.md

This gets SKILL.md only. The skill's 42 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/dy-2026-paranoia-ai-system-evolver/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/dy-2026-paranoia-ai-system-evolver.json

This gets SKILL.md only. The skill's 42 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/21/2026
Source: dy-2026/gamedesignos ↗
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

references/

templates/

SKILL.md

Paranoia AI System Evolver

Copyright (c) 2026 Paranoia. Licensed under the MIT License.

核心立场

把 AI 系统演化当成受控系统设计，而不是神秘的自我改良；把信息获取当成决策投资，而不是越多越好的默认动作。

Intent Work Order 先把“帮我写、优化、分析、出图”的指令单翻译成“要改变什么现实、谁验收、不能牺牲什么、AI 可自治到哪里”的作战意图。
WOOP 定义任务意图、验收结果、失败模式和恢复协议。
Decision Object 定义现在到底要决定什么，以及没有新信息时会做什么。
RJR-AI 定义剩余判断权、授权边界和谁能拍板。
VOI 判断哪些信息、检索、追问、实验或 AI 分支值得付出成本。
Scenario VOI Adapter 定义不同使用场景里什么证据才真的会改变行动。
UL（Uncertainty Ladder，不确定性阶梯）控制下一轮释放多少未知，使失败可归因、复杂度逐步增加并经过迁移验证。
OODA 让 agent 用现实反馈刷新地图。
Evals 决定哪些改动值得留下。
Human Gate 防止一次有用突变污染长期系统。
Rollback 让每次提升都可逆。

VOI 的硬规则：真实、新鲜或结构清晰的信息不一定有价值。只有当合理信号可能改变行动、优先级、资源配置或停止条件时，它才具有当前决策价值。

UL 的硬规则：VOI 选最值得消除的未知，UL 控制暴露剂量。每轮默认只释放一个主要不确定性；若失败无法区分主要解释，必须标记 confounded、恢复支架并退回更受控环境，不能继续叠加永久规则。受控样本通过不等于迁移通过，其他维度通过也不自动提升权限或真实后果。

RJR-AI 的硬规则：AI 可以扩大可能性，Workflow 可以压缩混乱，Eval 可以提供反馈，权限系统可以防止越界，知识库可以积累组织记忆；但高耦合、低可逆、证据不足且必须下注的问题，属于人的剩余判断权，agent 只能辅助论证并进入 Human Gate。

何时使用

用于改动这些层：

prompt、system instruction、memory、RAG、tool routing、workflow、schema、eval set、docs 或 Codex skill；
agent feedback loop、trace format、release gate 与 rollback policy；
AI 工作单、任务单、需求单、prompt brief 从“命令 AI 做动作”升级为“给 AI 一个清晰作战意图并让它在边界内循环”；
需要把项目整体流程、workflow run、产出质量、验收、复盘和候选规则沉淀纳入治理检查；
需要 model compression、causal mediator、WOOP harness protocol 或 total description cost 降低的 AI engineering 结构；
需要判断某次搜索、追问、读记忆、日志分析、实验或更多 AI 对话是否值得；
出现 FOMO、信息过载、分支爆炸、研究替代行动或高结构低价值输出时。

不要用它来合理化失控的模型权重改动、静默长期记忆写入、未经批准的全局 skill 安装，或没有 Human Gate 的生产影响行为。它也不是通用热点总结器；没有决策对象时，只允许有预算的探索或明确的信息消费。

快速流程

定义任务和被改动的系统层：prompt、memory、RAG、tool routing、workflow、eval、schema、docs 或 skill。
若用户给的是指令单，先写轻量 Intent Work Order：
- reality_to_change：我要改变什么现实；
- parent_project_goal：服务哪个更大的项目目标；
- desired_world_state：完成后外部世界应该变成什么状态；
- verifier_role 与 first_impression_must_understand：谁验收，第一眼必须看懂什么；
- must_not_sacrifice、ai_can_freely_change、ai_must_not_touch：不能牺牲、可自由改和不允许碰；
- decision_principles_if_plan_breaks：原计划不成立时按什么原则改方向；
- failure_signals_to_check_before_delivery 与 retrospective_contract：交付前自查和复盘沉淀。
写轻量 WOOP Task Card：
- Wish / Intent Spec：目标、输出物、范围与停止条件；
- Outcome / Evaluation Rubric：验收标准与决策收益；
- Obstacle / Failure Pattern：目标漂移、过度信任、上下文污染、工具滥用、FOMO 调研、选项爆炸、虚假确定性等内在失败模式；
- Plan / If-Then Protocol：触发条件、判断者、恢复动作、重试、交还人或 rollback。
在获取更多信息前定义 Decision Object：
- 决策问题、owner、deadline；
- 真实可选项；
- current_default_action，即没有新信息时的行动；
- stakes、reversibility 与 boundary_status: undefined | far | near | locked。
建立 RJR-AI 授权门：
- 判断 coupling：局部低耦合，还是会牵动产品、系统、账号、发布、长期规则的高耦合；
- 判断 reversibility：可逆、撤回昂贵，还是不可逆；
- 写出 delegation：AI 只能读、建议、草稿，还是可做低风险可逆执行；
- 把低风险可逆任务交给自动化，把可测试事项交给 eval，把高耦合低可逆事项交给 Human Gate；
- 若证据不足但必须下注，明确 residual_judgment，由人选择方向。
建立 VOI 决策门：
- 只保留会影响选项排序的不确定性；
- 每轮最多提出 3 个 candidate_information_actions 候选信息行动；
- 为可能信号预注册 posterior_update 与 action_if_seen；
- 若所有信号都不会改变行动，停止调研或标记为 model_learning / information_consumption；
- 用 EVPI 作为价值上界，用 EVSI 判断具体样本、实验或探针；
- 扣除获取、延迟、注意力、隐私、污染和实施风险成本；
- 选择净价值最高的最小探针，并写停止规则。
选择 Scenario VOI Adapter，按具体使用场景定义有效证据：
- skill_evolution：看真实 trace、行为 eval、负迁移、rollback，而不是一次漂亮案例；
- game_direction：看玩家承诺、核心循环、题材解释规则、生产风险和最小原型信号；
- experience_diagnosis：看 evidence_id、issue priority、修复动作和下一轮验证是否改变；
- source_curation：看材料是否改变入库、分类、沉淀或拒绝，而不是只看内容新鲜；
- content_decision：看选题、角度、标题承诺、论证主线和发布判断是否改变；
- platform_fact：看当前一手来源、实际平台状态、兼容策略和时效边界；
- high_risk_action：看是否降低不可逆错误，并默认进入 Human Gate；
- ai_branch_management：看分支是否改变下一探针，不能改变行动的分支应归档或关闭。
建立 UL (Uncertainty Ladder)，为下一轮验证设计可归因的环境：
- 声明目标能力与当前阶段：UL-L0 | UL-L1 | UL-L2 | UL-L3 | UL-L4 | UL-L5；
- 记录输入新颖度、上下文歧义、工具环境、协作、权限与后果、验收歧义的暴露向量；
- 写出本轮 released_this_round、held_constant、仍保留的支架和失败后果预算；
- 每轮默认只释放一个主要变量，预注册 pass / fail / confounded / stop 信号；
- 失败后用消融、对照或反事实定位 primary_bottleneck，只针对主瓶颈修改；
- 同层复现后才增加复杂度，最后用近/中迁移和负迁移样本限制适用范围；
- 权限、发布、资金、长期记忆和真实用户影响不随阶段自动晋升，仍走 RJR-AI / Human Gate。
显式写出 operating model：
- compression：什么短模型能解释多数真实案例；
- causality：哪些 mediator 把输入连接到结果；
- control points：agent、workflow 或 human 能干预哪个 mediator；
- cost：core model、routing、state、validation、exception、recovery 的成本在哪里累积。
维护紧凑 OODA 状态：

Observe：目标、上下文、证据、惊讶信号、触发的 Obstacle；
Orient：当前框架、用户模型、领域模型、决策边界、不确定性地图；
Decide：选择动作、拒绝动作、VOI 理由与停止条件；
Act：artifact、tool call、最小探针或 test；
Evaluate：用 Outcome 打分，记录先验—信号—后验—行动变化。

分离 task OODA 和 meta OODA。任务循环完成当前工作；元循环只提出未来系统可考虑的 candidate 改动。
每个演化改动保持 candidate，直到证据、行为 eval、必要审批和 rollback 都存在。
当目标层是 skill，回放代表性任务，检查是否减少低 VOI 分支、是否保留具体负反馈、是否出现更啰嗦、更慢或误触发的负迁移；同时检查受控通过是否能迁移到陌生样本。
满足任一条件即停止继续获取信息：行动对合理信号已稳健、边际 VOI 不高于边际成本、样本门达到、deadline 到达、剩余不确定性不改变行动，或 Human Gate 已承诺执行。

按需读取

完整 VOI、EVPI、EVPPI、EVSI、决策边界、AI 疲劳与反 AI 味规则：references/value-of-information-playbook.zh-CN.md；英文：references/value-of-information-playbook.en.md。
意图工程与 AI 工作单从指令单升级为意图单：references/intent-engineering-work-order.zh-CN.md；英文备份：references/intent-engineering-work-order.en.md。
项目 workflow 治理、workflow-run.governance、shadow/warn/enforce 晋升：references/project-workflow-governance.zh-CN.md；英文备份：references/project-workflow-governance.en.md。
WOOP 任务准入、执行监控和失败恢复：references/woop-harness-protocol.zh-CN.md；英文：references/woop-harness-protocol.en.md。
RJR-AI 剩余判断权、授权门、VOI/OODA 系统演化闭环：references/evolution-loop-playbook.zh-CN.md；英文：references/evolution-loop-playbook.en.md。
UL 暴露向量、六阶段工程 gate、瓶颈归因、逐步增加复杂度与迁移验证：references/uncertainty-ladder-protocol.zh-CN.md；英文备份：references/uncertainty-ladder-protocol.en.md。
Model compression、causal mediator、control point 与 total description cost：references/model-compression-playbook.zh-CN.md；英文：references/model-compression-playbook.en.md。
Eval、trace、versioning、promotion 与 rollback：references/eval-versioning-playbook.zh-CN.md；英文：references/eval-versioning-playbook.en.md。
可复制表单：
- 意图工作单：templates/intent_work_order.md、templates/intent_work_order.zh-CN.md、templates/intent_work_order.en.md；
- workflow 治理审查：templates/workflow_governance_review.md、templates/workflow_governance_review.zh-CN.md、templates/workflow_governance_review.en.md；
- VOI 决策门：templates/voi_decision_gate.md、templates/voi_decision_gate.zh-CN.md、templates/voi_decision_gate.en.md；
- OODA / VOI 状态：templates/ooda_voi_state.md、templates/ooda_voi_state.zh-CN.md、templates/ooda_voi_state.en.md；
- UL 状态：templates/uncertainty_ladder_state.md、templates/uncertainty_ladder_state.zh-CN.md、templates/uncertainty_ladder_state.en.md；机器对象名为 ul_state；
- 进化提案：templates/evolution_proposal.md、templates/evolution_proposal.zh-CN.md、templates/evolution_proposal.en.md。
VOI 行为回归案例：evals/voi-decision-gate-cases.md 与 evals/voi-decision-gate-cases.en.md。
不确定性阶梯行为回归案例：evals/uncertainty-ladder-cases.md 与 evals/uncertainty-ladder-cases.en.md。

Human Gate 默认项

执行以下动作前必须询问人：

写入长期记忆；
安装或替换全局 skill；
改动生产策略、发布行为、真实账号、资金或用户可见系统；
把生成内容或 workflow mutation 从 candidate 提升为当前规则；
删除、镜像、批量移动或覆盖项目工作区；
在高风险决策中用定性 VOI 评分替代真实损益模型。

输出契约

结束时说明：

当前要支持的决策、选项和默认行动；
Intent Work Order 中的现实改变、验收者、第一眼必须看懂什么、不可牺牲项、AI 自治边界和失败信号；
workflow-run.governance 中的 intent、VOI/RJR、漂移审查、Human Gate、rollback 与 candidate learning 引用；
RJR-AI 授权判断：耦合度、可逆性、授权层级、delegation_matrix 与 residual_judgment；
决策边界与最高价值不确定性；
使用的场景 VOI Adapter、有效证据标准和最小探针；
当前 UL rung、暴露向量、本轮释放/保持不变的变量、支架、后果预算与 fallback rung；
若发生失败，主要瓶颈、区分性探针、归因置信度和针对性修复；
迁移与负迁移证据，以及能力适用范围；
选择或拒绝了哪些信息行动，以及信号如何改变行动；
何时停止继续调研；
改了什么；
WOOP 如何落到结果；
哪些 eval 或检查已经运行；
哪些仍然是 candidate；
哪些需要 Human Gate；
如何 rollback。

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Automatically, when a task matches. Use when controlled AI system evolution or a decision-oriented information audit is needed.

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paranoia-ai-system-evolver/SKILL.md

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License MITImported 8/3/2026Version v1Bundled 42 files

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