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Writing Lean Proofs · trailofbits | DecimalAI

Codingby trailofbits

Writing Lean Proofs

Writes and reviews structured Lean 4 proofs and designs Lean libraries following Mathlib conventions.

Maintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

77%86%of test cases passed +9 pts

Without skill17 of 22

With skill19 of 22

3 fixed16 passed either way2 still failing1 broke

Measured on gemini-3.6-flash8/29/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI am setting up a collaborative Lean 4 formalization project for a math framework involving three contributors. I want a guide on how to structure our theorem statements and stub out proofs so everyone can work simultaneously without blocking each other. Please provide a clear workflow that explains how to construct our initial file layout, how to represent incomplete proofs safely, and how to transition from design to full verification.

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 trailofbits-writing-lean-proofs
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("trailofbits-writing-lean-proofs")
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 trailofbits-writing-lean-proofs

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/trailofbits-writing-lean-proofs/SKILL.md https://app.decimal.ai/s/trailofbits-writing-lean-proofs/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/trailofbits-writing-lean-proofs/SKILL.md

This gets SKILL.md only. The skill's 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/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/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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("trailofbits-writing-lean-proofs")
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/trailofbits-writing-lean-proofs/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/trailofbits-writing-lean-proofs/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 8 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 trailofbits-writing-lean-proofs

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/trailofbits-writing-lean-proofs/SKILL.md https://app.decimal.ai/s/trailofbits-writing-lean-proofs/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/trailofbits-writing-lean-proofs/SKILL.md

This gets SKILL.md only. The skill's 8 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/trailofbits-writing-lean-proofs/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/trailofbits-writing-lean-proofs.json

This gets SKILL.md only. The skill's 8 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/29/2026
Source: trailofbits/skills ↗
License: CC-BY-SA-4.0

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

Writing Lean Proofs

When to Use
When NOT to Use
The workflow
The extraction ladder
Quick reference
Rationalizations to reject
References

Structured Lean 4 proof writing and library design, distilled from Mathlib's style and review conventions and from the methodology of large formalization projects (Liquid Tensor Experiment, PFR, Fermat's Last Theorem).

Core principle: design top-down, prove bottom-up. Lean propositions are proof-irrelevant — only a theorem's statement can affect later declarations. Statements are the stable interface; proofs are disposable and freely replaceable. Put design effort into definitions and statements, then fill in proofs against skeletons that already compile (modulo sorry).

When to Use

Proving theorems in Lean 4, from single lemmas to multi-file developments
Formalizing mathematics, protocols, or software specifications in Lean
Defining new types, structures, or functions in a Lean library
Reviewing Lean code for readability, maintainability, or Mathlib readiness
Refactoring a long or fragile tactic proof into lemmas
Setting up a formalization project that several people or agents will contribute to in parallel
Setting up CI, linters, or verification gates for a Lean project — do this at project start, before patterns propagate
Diagnosing slow proofs, maxHeartbeats timeouts, or expensive reduction
Writing custom tactics, macros, or project-specific linters

When NOT to Use

Lean 4 as a general-purpose programming language (no proofs involved) — most of this skill targets proof and API structure
Coq, Isabelle, Agda, or Lean 3 — conventions and tactic names differ; Lean 3 idioms (ge_or_gt linting, discrete_field) are obsolete
Verified-software Lean projects with their own house style (e.g. spec-traceability-first codebases): Mathlib conventions are the community default, but check the project's CONTRIBUTING first and defer to it

The workflow

1. Design definitions and their API first

Definitions carry the design weight. Before proving anything about a new concept:

Prefer total functions with junk values over subtypes or Option in signatures (Mathlib: (0 : ℝ)⁻¹ = 0). Side conditions then appear only on the lemmas that need them, not at every use site.
Bundle: new morphism kinds are structures with a FunLike instance; new subobject kinds use SetLike; carry property proofs as structure fields, not separate IsHom-style predicates.
Pick the canonical spelling (simp-normal form) for every concept with multiple equivalent forms, and state all API lemmas for that form only.
Write the API in the same file, immediately: ext, @[simp], coercion, and injectivity lemmas — before the definition is used anywhere. Downstream proofs use the API, never unfold/show ... from rfl.

See library-design.md for the full set of design rules with rationale.

2. Build a sorry skeleton

State everything before proving anything, at every scale:

Project scale: state the target theorem and the lemmas it needs, all with := sorry, and make the file compile. Each sorry is now an independent work unit — a contributor (human or LLM) can discharge one without understanding the rest. This is how LTE, PFR, and FLT scale to dozens of parallel contributors.
Proof scale: inside a proof, lay out the have/suffices/calc skeleton with sorry justifications, get Lean to accept the structure, then fill each step. Keeping the structure intact is what produces useful error messages while you work.

example (a b c d : ℝ) (h : c = d * a + b) (h' : b = a * d) : c = 2 * a * d := by
  calc
    c = d * a + b     := sorry
    _ = d * a + a * d := sorry
    _ = 2 * a * d     := sorry

3. Fill goals, one focused goal at a time

Every new subgoal gets a focusing dot · with an indented block — never leave several goals active in unfocused sequence (Mathlib's multiGoal linter enforces this). This is what kills fragile goal-ordering dependence.
Open each block with a redundant show stating its goal. The proof works without it; reviewers and future editors need it. If show would change the goal, use change instead — keep stated goals honest.
Chained rewrites of (in)equalities become calc blocks, relations aligned vertically.
have for forward stepping stones ("we first establish X"); suffices for backward reduction ("it suffices to show X").
While drafting, annotate the goal state as a comment before non-obvious tactics — emitted by Lean, never imagined. In a headless workflow, insert trace_state at the point of interest or a deliberate done where goals should be closed, then run lake env lean Path/To/File.lean; copy the reported hypotheses, case name, and target. Strip routine probes after the proof works. This is the single most effective technique for LLM-written proofs (see llm-techniques.md).

See proof-style.md for the full tactic-style rules, and naming-conventions.md for naming lemmas so their names are guessable from their statements.

4. Verify mechanically

Do not eyeball-check style — run the checkers. lake build is the floor, and it is only the floor: sorry is a warning, so a green build exits 0 with sorries still present.

Gate unproved obligations by asking the kernel, never by grepping. #print axioms myTheorem for a spot check; for CI, collect axioms per declaration with Lean.collectAxioms and assert the whole expected footprint ([propext, Classical.choice, Quot.sound] unless deliberately widened), so a stray sorry or a new trust assumption like native_decide fails loudly. Grep is wrong in both directions: it matches the word in comments, and it misses a theorem whose own text is clean but which applies an unproved helper. Working script in linting.md.
Choose lints by project role and put them in CI at project start. Do not enable linter.mathlibStandardSet wholesale in a downstream project: it combines proof-maintenance checks with public-API checks, house style, and Mathlib-specific repository policy. For a self-contained proof, start with linter.auxLemma, linter.style.maxHeartbeats, linter.style.multiGoal, linter.style.setOption, and linter.style.show. A reusable library should additionally enable linter.flexible, linter.style.missingEnd, linter.style.openClassical, and the two unused*InType checks. Treat nativeDecide as a trust-policy choice and formatting or deprecated-syntax checks as project style. No warning gates anything unless warnings fail the build. Run Batteries' declaration-level #lint checks, including simpNF, separately. Verify every option against the pinned Mathlib source and with a known-trigger fixture: a misspelled weak. option is intentionally ignored. The complete 26-member audit and lakefile profiles are in linting.md.
Write a custom linter for every project-specific convention (simp-set discipline, summary-lemma coverage, required attributes) — a declaration-level @[env_linter] is one structure, and it is the only thing that reliably catches "the attribute is missing on 29 of 30 declarations". See linting.md for the recipe and the engineering rules (vacuity anchors, prove-it-can-fail, allowlists).

The extraction ladder

When does proof structure graduate into separate lemmas?

Before extracting, state the fragment's type and search by shape. Put the proposed statement in a scratch example, run exact? and apply? on the bare goal, then try a type-pattern and source search. If an existing theorem fits, use it. Do not report an API gap without recording the searches that failed.

A sub-argument repeats within one proof → name it as a local have.

theorem min_comm (a b : ℝ) : min a b = min b a := by
  have h : ∀ x y : ℝ, min x y ≤ min y x := by
    intro x y
    apply le_min
    · show min x y ≤ y
      exact min_le_right x y
    · show min x y ≤ x
      exact min_le_left x y
  apply le_antisymm
  · show min a b ≤ min b a
    exact h a b
  · show min b a ≤ min a b
    exact h b a

The statement is independently interesting, or extraction sheds hypotheses the sub-argument does not need → standalone lemma. Dropping unneeded hypotheses is the stronger trigger: the extracted lemma becomes more general than the proof it came from.
The proof reads as "long and unwieldy" → split it. This is Mathlib's review criterion, and it is deliberately qualitative — there is no line threshold. Resolve doubt by attempting the extraction: if a fragment has a clean statement, it wanted to be a lemma.

Quick reference

Rule	Why	Enforced by
Never unfold definitions downstream; `erw` or trailing `rfl` = missing API	API lemmas are the abstraction boundary	review ("missing API" smell)
Terminal `simp` stays unsqueezed; non-terminal `simp` becomes `simp only [...]`	squeezed terminal calls bury the key lemmas and break on renames	style guide
One focused goal at a time (`·` blocks)	kills goal-ordering fragility	`linter.style.multiGoal`
`show` must not change the goal (use `change`)	stated goals stay honest	`linter.style.show`
No `set_option` debug/trace/profiler or unscoped `maxHeartbeats` in final code	debugging scaffolding	`linter.style.setOption`
State lemmas in simp-normal form, `<` not `>`	simp matches syntactically	`simpNF` linter
Golf only when the result is at least as readable; trivial results exempt	short ≠ better	review
`Fact` instances are local, never global	global instances degrade all typeclass search	review
Name lemmas from their statements (see naming reference)	names become guessable without search	`linter.style.nameCheck` catches only `__`; `#lint defsWithUnderscore` and review cover more
Search a bare goal by shape before writing a helper or claiming an API gap	names are not always guessable from the target	`exact?`, `apply?`, type/source search
Generally one tactic invocation per line; a one-line closing proof is the exception	preserves readable proof structure without inventing an absolute rule	style guide
Gate `sorry` with `collectAxioms`/`#print axioms`, never grep	grep matches comments, misses unproved helpers	axiom audit in CI
Prefer simp-lemma LHSs keyed on structure, not numerals; one spelling per constant	`2 ^ 32` never matches a goal normalized to `4294967296`	`simpNF`, review
Re-derive every `simp only` list with `simp?` at its own site	lists do not transfer between look-alike goals	`linter.flexible`
Every `maxHeartbeats` override is an unproven claim — measure before believing	copy-pasted budgets carry no information	`#count_heartbeats`, bisection
Conditional simp lemma fires shallow but not deep → raise `maxDischargeDepth` (default 2)	chained side conditions truncate silently, no diagnostic	diagnosis (proof-style, simp discipline)
Every project-specific convention gets a custom linter, in CI from day one	review misses the 29-of-30 failure mode	`@[env_linter]` + `#lint`

Full rationale for each row, plus the library-level anti-patterns, in anti-patterns.md.

Rationalizations to reject

Excuse	Reality
"The proof compiles, ship it"	Compiling is the floor. A monolithic tactic block that only Lean can read will break silently at the next Mathlib bump and no one will be able to repair it.
"Unfolding the definition is simpler than writing API lemmas"	Every downstream `unfold` couples a proof to the implementation. The first refactor breaks all of them at once. Write the missing lemma.
"Squeezing every simp makes the proof faster and more robust"	Backwards for terminal simp calls: the squeezed list breaks on every rename and drowns the signal. Squeeze non-terminal calls only.
"It's shorter, therefore better"	Mathlib review policy: golfing is fine only when it does not sacrifice readability. Length is not the target; legibility is.
"I'll restructure it into lemmas after it works"	After it works, the structure is load-bearing and tangled. State the skeleton first; the lemmas fall out for free.
"Adding `show` lines is redundant noise"	They are redundant to the kernel and essential to every human or model that reads the proof next.
"This helper is too specific to be a lemma"	If it has a clean statement, extract it — dropping the hypotheses it doesn't need usually reveals it was general all along.
"We'll add linters once the library stabilizes"	Backwards: patterns propagate by copy-paste, so a deferred linter meets a 400-warning backlog instead of one bad line. Enable what is already clean and gate it now.
"The check passed, so we're clean"	A check that can't fail proves nothing — sweeps reach zero files, misspelled `weak.` options are ignored, pipelines swallow exit codes. Prove every gate can fail before trusting that it passes.
"The proof is slow, raise maxHeartbeats"	An unmeasured budget is a claim, not a fix — and it masks the regression the next reader needs to see. Measure with `#count_heartbeats`; restructure the definition or decompose the goal.

References

library-design.md — definitions, APIs, bundling, abstraction boundaries, spec-driven project decomposition
proof-style.md — tactic proof structure: calc, have/suffices, focusing, and simp discipline including the why-doesn't-this-lemma-fire diagnoses (discharge depth, traversal order, numeral spellings)
naming-conventions.md — Mathlib naming so lemma names are computable from statements
anti-patterns.md — recognized anti-patterns, why each is harmful, and which linter catches it
llm-techniques.md — evidence-based techniques specific to LLM-written proofs
linting.md — axiom-based sorry gates, enabling project-specific linter profiles in CI early, the full Mathlib standard-set audit, adopting linters with a backlog, writing custom linters for project-specific constructs, and proving every gate can fail
performance.md — measuring per-declaration cost, where reduction cost comes from, optimizing definitions without losing semantics
tactics.md — metaprogramming discipline: extension-point selection, metavariable and recovery safeguards, bounded search, actionable errors, structured tracing, generated declarations, and failure-surface testing

When it activates

Automatically, when a task matches. Use when proving theorems in Lean, formalizing mathematics or specifications in Lean 4, defining new types or definitions in a Lean library, reviewing Lean proofs for readability and maintainability, refactoring long tactic proofs into lemmas, filling in sorry placeholders in a Lean development, setting up CI or linters for a Lean project, diagnosing slow proofs or maxHeartbeats timeouts, or wri

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

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LLM judge · does the skill do what it claims

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LLM judge · policy & harmful-content review

Source

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trailofbits / skills

plugins/writing-lean-proofs/skills/writing-lean-proofs/SKILL.md

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License CC-BY-SA-4.0Imported 8/10/2026Version v1Bundled 8 files

Illustrative Want to see what trailofbits/writing-lean-proofs 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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What it changes

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Writing Lean Proofs

Contents

When to Use

When NOT to Use

The workflow

1. Design definitions and their API first

2. Build a sorry skeleton

3. Fill goals, one focused goal at a time

4. Verify mechanically

The extraction ladder

Quick reference

Rationalizations to reject

References

When it activates

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