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Absolute Docs · maddhruv | DecimalAI

Documentationby maddhruv

Absolute Docs

Diátaxis-driven documentation for AI coding agents: write, improve, or audit tutorials, how-tos, reference, explanation, and developer docs (README, CONTRIBUTING, ADRs). Detects the docs stack; gates on the outline before writing prose; verifies every claim against the code before it ships.

Never made a case worseMaintained

SkillScore

SkillScore87/100

Test results 82 · Live runs 92

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

59%82%of test cases passed +23 pts

Without skill13 of 22

With skill18 of 22

5 fixed13 passed either way4 still failing0 broke

Measured on gemini-3.6-flash8/22/2026v121 comparable

Track record

Agent runs: 60; 36 in the last 30 days

See one task, with and without the skill

One test taskI'm satisfied with the revised architectural decision record in `docs/adr/0002-event-bus.md`. Please stage the change and execute a git commit with a descriptive message to git.

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 maddhruv-absolute-docs
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("maddhruv-absolute-docs")
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 maddhruv-absolute-docs

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/maddhruv-absolute-docs/SKILL.md https://app.decimal.ai/s/maddhruv-absolute-docs/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/maddhruv-absolute-docs/SKILL.md

This gets SKILL.md only. The skill's 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/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/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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("maddhruv-absolute-docs")
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/maddhruv-absolute-docs/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/maddhruv-absolute-docs/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 7 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 maddhruv-absolute-docs

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/maddhruv-absolute-docs/SKILL.md https://app.decimal.ai/s/maddhruv-absolute-docs/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/maddhruv-absolute-docs/SKILL.md

This gets SKILL.md only. The skill's 7 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/maddhruv-absolute-docs/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/maddhruv-absolute-docs.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/22/2026
Source: maddhruv/absolute ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Start your first response with the 📚 emoji.

Absolute Documentations: Diátaxis-Driven Documentation

Absolute Documentations turns "write some docs" into documentation a reader can actually use. Every document it produces serves exactly one reader need, identified with the Diátaxis framework, written in the project's own voice and docs stack, and verified against the actual codebase before it ships. It writes new docs, rewrites existing ones to their quadrant's standard, and audits whole doc sites for structural rot.

It never writes a full document before the outline is approved, and it never documents behavior it has not verified in the code.

The Diátaxis Compass

Every piece of documentation answers exactly one kind of reader need. Classify before writing — a page that mixes quadrants serves nobody.

	Serves the reader's STUDY	Serves the reader's WORK
Practical steps	Tutorial — a lesson. Guides a newcomer through a guaranteed-success experience.	How-to guide — a recipe. Helps a competent user accomplish a specific goal.
Theoretical knowledge	Explanation — a discussion. Deepens understanding of a topic, gives context and reasons.	Reference — a dictionary. States facts about the machinery, completely and neutrally.

To classify, ask two questions:

Is the reader studying (acquiring skill) or working (applying skill)?
Does the reader need action (steps to follow) or cognition (knowledge to absorb)?

Reader situation	Quadrant
"I'm new, show me what this is like"	Tutorial
"I know the basics, I need to get X done"	How-to guide
"What exactly does this option/endpoint/flag do?"	Reference
"Why does it work this way? What's the bigger picture?"	Explanation

The cardinal sin is mixing. A tutorial that stops to explain architecture loses the learner. A reference page that gives advice stops being trustworthy as a pure description. When you feel the urge to mix, that is a signal to link to the other quadrant, not to merge into it.

Modes

Detect the mode from the request:

User says	Mode
"write a tutorial / guide / README / docs for X", "document this feature"	WRITE
"improve / rewrite / clean up this doc", "this README is bad"	IMPROVE
"audit our docs", "our docs are a mess", "restructure the documentation"	AUDIT

WRITE Mode

Step 1 — Recon (codebase first, questions second)

Before asking the user anything, learn everything the repo can teach:

Detect the docs stack (see Stack Detection below) and load references/docs-stacks.md if writing site pages.
Read existing docs — tone, terminology, heading style, frontmatter schema, sidebar/nav structure, where each quadrant lives.
Read the code being documented — public API surface, actual option names, actual defaults, actual error messages. The code is the source of truth, not your memory of similar tools.
Check project metadata — package.json/pyproject/go.mod for the real name, version, install command, supported runtimes.

Step 2 — Intake

Four things must be pinned down before any outline. Answer them from recon where possible; ask the user only what the repo cannot answer, one question at a time (use AskUserQuestion where available), always with a recommended answer:

Document type — which Diátaxis quadrant (or which developer-doc form).
Target audience — novice end user? experienced operator? contributor? What can you assume they already know?
Reader's goal — what will the reader be able to do after reading?
Scope — what is explicitly in, and just as important, what is explicitly out.

Step 3 — Outline gate (hard gate)

Propose, before writing any prose:

the file path(s) the doc will live at, matching the stack's routing conventions
a heading-level outline with one line per section describing its content
the quadrant each page serves (multi-page requests get one quadrant per page)
any sidebar/nav changes needed

STOP and wait for explicit approval. Do not write the document until the user confirms the outline. This is the single gate in the workflow — everything before it is cheap to change, everything after it is expensive.

Step 4 — Write

Follow the per-quadrant playbook in references/ (load the matching file).
Write in the project's established voice; follow references/style-and-voice.md.
Use the stack's components and frontmatter (from references/docs-stacks.md); plain Markdown when no stack is detected.
Apply the Accuracy Protocol below to every factual claim and code block.

Step 5 — Self-review

Score the draft against the rubric below. Fix anything scoring under 4 before presenting. Present the doc with a one-paragraph summary of what was written, where it lives, and any nav changes made.

IMPROVE Mode

For "fix this README" / "improve this page":

Classify the page's intended quadrant from its location, title, and content. If it serves two masters, say so — that is usually the root problem.
Diff against the quadrant's standard (load its reference playbook). List concrete violations: missing sections, mixed purposes, stale claims, broken snippets, wrong audience level.
Verify before preserving: every code snippet, option name, and version claim in the existing doc gets checked against the current code. Stale facts are the most common defect in old docs.
Rewrite preserving everything accurate and project-specific. Do not bleach the project's voice into generic doc-speak.
Single-page improvements need no gate. If the fix requires splitting or moving pages, that is a restructure — propose the move map and gate on approval first.

AUDIT Mode

For "our docs are a mess" / "audit the documentation":

Inventory — list every docs page (site pages, README, docs/ folder) with path and title.
Classify — assign each page its dominant quadrant; flag pages that are mixed (the most common finding), misfiled, duplicated, or orphaned from nav.
Gap map — build the 4-quadrant grid for the project's main user journeys and mark what is missing. A typical project has reference and nothing else; the first tutorial is usually the highest-value gap.
Report — a table of findings: page → current state → quadrant → action (keep / rewrite / split / merge / move / delete), ordered by reader impact.
Gate — restructuring moves files and breaks links. Present the map, get approval, then execute with redirects/link updates included.

The audit deliverable is the report and map. Executing it is a follow-up the user approves explicitly.

Stack Detection

Read cached config first: if .absolute.config.json or ~/.absolute/config.json exists (from /absolute init), resolve the effective config (project file → global projects["<cwd>"] → global defaults) and use conventions.docs.stack + conventions.docs.dir — skip the marker-file scan and write pages under docs.dir. With no config (or no docs block), soft-suggest init and detect by checking for marker files in this order; first match wins:

Marker	Stack	Content format
`source.config.ts` / `fumadocs-*` in package.json	Fumadocs	MDX + fumadocs-ui components
`docusaurus.config.*`	Docusaurus	MDX + admonitions (`:::note`)
`astro.config.*` with `@astrojs/starlight`	Starlight	MDX/Markdoc + Starlight components
`mkdocs.yml`	MkDocs (often Material)	Markdown + admonitions (`!!! note`)
`.vitepress/config.*`	VitePress	Markdown + containers (`::: tip`)
`mint.json` / `docs.json` (Mintlify)	Mintlify	MDX + Mintlify components
none of the above	Plain Markdown	GitHub-flavored Markdown, no components

Per-stack frontmatter, component vocabulary, nav registration, and quadrant-to-component mapping live in references/docs-stacks.md — load it whenever writing pages for a detected stack. Never use one stack's syntax in another (no :::note in MkDocs, no <Callout> outside MDX stacks).

Quadrant Rules at a Glance

Full playbooks with templates live in references/. The non-negotiables:

Quadrant	Must	Must not
Tutorial	Work first try, every time; concrete single path; visible result at every step; first person plural ("we")	Offer choices, explain theory in-line, assume unstated setup, branch
How-to	Start from a real task; assume competence; state prerequisites; show the steps and only the steps	Teach basics, explain why at length, cover every edge case inline
Reference	Be complete, accurate, and neutral; mirror the code's structure; state defaults, types, constraints	Give advice, tell stories, omit "obvious" entries, drift from the code
Explanation	Give context, reasons, trade-offs, history; admit alternatives; connect concepts	Contain instructions, pretend to be the only valid view, duplicate reference facts

Developer doc	Quadrant blend	Playbook
README	Landing page: pitch + quickstart (mini-tutorial) + links out	`references/developer-docs.md`
CONTRIBUTING	How-to guide for contributors	`references/developer-docs.md`
ARCHITECTURE	Explanation with reference elements	`references/developer-docs.md`
ADR	Explanation, decision-shaped, immutable once accepted	`references/developer-docs.md`
CHANGELOG	Reference, reverse-chronological, Keep a Changelog format	`references/developer-docs.md`
Runbook	How-to guide under stress: terse, imperative, copy-pasteable	`references/developer-docs.md`
API reference	Reference, generated where possible, hand-written prose around it	`references/reference.md`

Accuracy Protocol

Documentation that lies is worse than no documentation. For every draft:

Code snippets come from the codebase, not from memory. If the doc shows an API call, find that API in the source and copy its real signature. If a snippet is runnable in this environment, run it.
Names are exact — flags, options, env vars, endpoints, file paths are copied from source, never paraphrased. --dry-run and --dryrun are different products.
Defaults and versions are read, not recalled — from the code and manifest files at the moment of writing.
Never document what does not exist. If the user asks you to document a feature you cannot find in the code, stop and say so — do not write aspirational documentation.
Links resolve — every internal link points at a file or route that exists; every anchor matches a real heading.
Outputs are real — if the doc says "you should see X", X is what the command actually prints.

Style Core

The full guide is references/style-and-voice.md. The rules that are never waived:

One idea per sentence. One purpose per paragraph. One quadrant per page.
Address the reader as "you" (tutorials may use "we" for shared journey).
Imperative mood for instructions: "Run the build", not "You can run the build".
Ban the condescension words: simply, just, easy, obviously, of course. If it were simple, the reader wouldn't be here.
Present tense, active voice. "The server returns 404", not "a 404 will be returned by the server".
Every code block declares its language. File-content blocks name the file.
Headings are scannable claims, not labels: "Configure the webhook" beats "Configuration".
Define a term once, then use it consistently — no elegant variation between "config file", "settings file", and "manifest" for the same thing.
Warnings come before the dangerous step, never after.
Cut every sentence that serves the writer (apologies, throat-clearing, marketing) rather than the reader.

Self-Review Rubric

Score 1–5 on each axis before presenting. Anything under 4 gets fixed first.

Axis	5 looks like
Quadrant purity	Every section serves the page's single declared purpose; tangents are links
Audience fit	Assumes exactly the declared knowledge — no more, no less
Accuracy	Every snippet, name, default, and output verified against the code
Completeness	Scope from intake fully covered; declared exclusions actually excluded
Followability	A reader can act on it top-to-bottom without backtracking or guessing
Voice	Indistinguishable from the project's best existing page
Stack fitness	Frontmatter, components, and nav match the detected stack's conventions

Red Flags — stop and fix

A tutorial offering the reader choices ("you can use npm or pnpm or...") — pick one, mention alternatives in a how-to.
A how-to guide that opens with three paragraphs of background — move it to an explanation page and link.
Reference material with personality ("this handy option...") — neutralize it.
An explanation page containing numbered steps — extract them to a how-to.
A README longer than ~300 lines — it is hoarding content that belongs in docs pages; split and link.
More than 3 callouts/admonitions on one page — they have stopped standing out.
A code block with no language tag, or a snippet you have not verified.
"As mentioned above" / "see below" — restructure so order doesn't need narrating.
Documenting around a bug instead of flagging it — tell the user, let them decide.

References

Load on demand from references/:

File	Load when
`tutorials.md`	Writing or fixing a tutorial / getting-started page
`how-to-guides.md`	Writing or fixing a how-to / task guide
`reference.md`	Writing or fixing reference / API docs
`explanation.md`	Writing or fixing concept / architecture / background pages
`developer-docs.md`	README, CONTRIBUTING, ARCHITECTURE, ADRs, changelogs, runbooks
`style-and-voice.md`	Any prose-heavy writing; calibrating to project voice
`docs-stacks.md`	A docs stack was detected; writing site pages

What this skill does not do

No marketing copy, blog posts, or release announcements — docs serve the reader's task, not the product's funnel.
No inline code comments or docstrings — that is code work, not documentation work.
No invented features — if it is not in the code, it is not in the docs.
No commits — it writes files and reports; the user reviews and commits.

Companion commands

Sibling commands in this skill pair well with docs:

/absolute work — build the feature you are now documenting.
/absolute ui — design the interface a tutorial walks through.
/absolute simplify — tidy code before documenting it.

Suggest them where relevant; they are always available (same skill, no extra install).

When it activates

Automatically, when a task matches. Triggers on "absolute docs", "write docs", "write a tutorial", "write a README", "document this", "improve this doc", "audit our docs".

About this skill

Automatic

Doc Ops pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

maddhruv / absolute

skills/absolute-docs/SKILL.md

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License MITImported 8/3/2026Version v1Bundled 7 files

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SkillScore

Safety

What it changes

Track record

Use this skill

Absolute Documentations: Diátaxis-Driven Documentation

The Diátaxis Compass

Modes

WRITE Mode

Step 1 — Recon (codebase first, questions second)

Step 2 — Intake

Step 3 — Outline gate (hard gate)

Step 4 — Write

Step 5 — Self-review

IMPROVE Mode

AUDIT Mode

Stack Detection

Quadrant Rules at a Glance

Accuracy Protocol

Style Core

Self-Review Rubric

Red Flags — stop and fix

References

What this skill does not do

Companion commands

When it activates

SkillSafety

Source

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