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spec-kitty-mission-system | DecimalAI

Agent Toolingby spec-kitty

spec-kitty-mission-system

Understand how Spec Kitty missions work: the 4 built-in mission types, how they define workflows via step contracts and action indices, how missions and work packages relate, how templates are resolved through the 6-tier chain, and how doctrine artifacts (procedures, tactics, directives) compose mission behavior. Triggers: "what missions are available", "how do missions work", "which mission should I use", "explain the mission system", "what is a mission", "change the mission", "mission template

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%100%of test cases passed +82 pts

Without skill4 of 22

With skill22 of 22

18 fixed4 passed either way0 still failing0 broke

Measured on gemini-3.8-flash11/2/2026v2

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskA developer wants to claim WP03 immediately, but WP01 and WP02 are listed in WP03's dependencies list. What exact status must WP01 and WP02 reach before WP03 can be claimed?

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 spec-kitty-spec-kitty-mission-system
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("spec-kitty-spec-kitty-mission-system")
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 spec-kitty-spec-kitty-mission-system

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/spec-kitty-spec-kitty-mission-system/SKILL.md https://app.decimal.ai/s/spec-kitty-spec-kitty-mission-system/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/spec-kitty-spec-kitty-mission-system/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/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/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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("spec-kitty-spec-kitty-mission-system")
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/spec-kitty-spec-kitty-mission-system/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/spec-kitty-spec-kitty-mission-system/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 1 other file is 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 spec-kitty-spec-kitty-mission-system

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/spec-kitty-spec-kitty-mission-system/SKILL.md https://app.decimal.ai/s/spec-kitty-spec-kitty-mission-system/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/spec-kitty-spec-kitty-mission-system/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/spec-kitty-spec-kitty-mission-system/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/spec-kitty-spec-kitty-mission-system.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 11/2/2026
Source: spec-kitty/spec-kitty ↗
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

spec-kitty-mission-system

Understand how missions structure work in Spec Kitty. A mission is a domain-specific workflow blueprint that defines what steps you go through, the template each step provides, what artifacts you produce, and how to validate success.

How Missions Work

The Core Concept

A mission answers: "What process should we follow to achieve this goal?"

Different goals need different processes. Building a software component is different from conducting research or writing documentation. Each mission provides domain-appropriate:

Steps — the ordered phases of work (specify → plan → implement → review); each step provides the prompt/content template agents follow
Artifacts — expected outputs (spec.md, plan.md, tasks.md)
Guards — conditions that must be met before advancing (e.g., spec.md must exist before planning)
Validation — checks that verify the output quality
Agent context — personality and instructions for the AI agent

The Hierarchy: Mission Type → Mission → Work Package → Workspace

Mission Type (e.g., software-dev)
  └── Mission (kitty-specs/042-auth-system/)
        ├── meta.json           ← links mission to mission type + target branch
        ├── spec.md             ← what we're building
        ├── plan.md             ← how we'll build it
        ├── tasks.md            ← WP breakdown
        └── tasks/
              ├── WP01.md       ← work package prompt
              ├── WP02.md
              └── WP03.md
                    └── Workspace (.worktrees/042-auth-system-lane-b/)

Mission Type = the workflow blueprint (reusable across missions)
Mission = a concrete thing you're building, linked to a mission type via meta.json
Work Package (WP) = one parallelizable slice of work within a mission
Workspace = an isolated git worktree owned by one execution lane

meta.json (Mission → Mission Type Link)

Every mission has a meta.json that records which mission type it uses:

{
  "feature_number": "042",
  "slug": "042-auth-system",
  "mission": "software-dev",
  "target_branch": "<target-branch>",
  "created_at": "2026-03-22T10:00:00Z",
  "vcs": "git"
}

The mission field determines which templates and validation rules apply. Default is software-dev if omitted.

The 4 Built-In Mission Types

software-dev (default)

Full software development lifecycle with work packages and code review.

Steps (runtime DAG):

discovery → specify → plan → tasks → implement → review → accept

Required artifacts: spec.md, plan.md, tasks.md

Gating: step ordering comes from the mission-runtime.yaml DAG (depends_on); work-package lane transitions are validated by the status model, and a WP cannot be claimed past its dependencies until each is approved or done. Review must be approved before acceptance.

Agent context: TDD practices, library-first architecture, tests before code.

Use when: Building components, fixing bugs, refactoring code — any work that produces code changes.

research

Systematic research with evidence-gated synthesis.

Steps (runtime DAG):

scoping → methodology → gathering → synthesis → output → accept

Required artifacts: spec.md, plan.md, tasks.md, findings.md

Gating: step ordering comes from the mission-runtime.yaml DAG (depends_on). The mission expects at least 3 documented sources before synthesis, and publication is approved at acceptance.

Special: Source gathering is iterative — sources are registered as they are found until the evidence base is sufficient for synthesis. Source tracking in source-register.csv, evidence in evidence-log.csv.

Use when: Investigating technologies, conducting literature reviews, evaluating options, any work requiring structured evidence gathering.

plan

Goal-oriented planning with iterative refinement.

Steps:

specify → research → plan → review

Use when: Planning a project, designing architecture, creating roadmaps — any work that produces planning artifacts but not code.

documentation

Documentation creation following the Divio 4-type system.

Workflow phases:

discover → audit → design → generate → validate → publish

Required artifacts: spec.md, plan.md, tasks.md, gap-analysis.md

Divio types: Tutorial (learning-oriented), How-To (task-oriented), Reference (information-oriented), Explanation (understanding-oriented).

Special: Supports auto-generation via JSDoc, Sphinx, or rustdoc. Gap analysis identifies missing documentation by classifying existing docs and finding coverage gaps.

Use when: Creating docs for a project, filling documentation gaps, documenting a specific component or API.

Mission Type Definition Files

Each mission type lives in packs/built-in/missions/{mission-key}/ with:

mission-runtime.yaml (Runtime DAG)

Defines steps as a directed acyclic graph with dependencies:

mission:
  key: software-dev
  name: Software Dev Kitty
  version: "2.1.0"

steps:
  - id: specify
    title: Specification
    depends_on: [discovery]
    prompt_template: specify.md
    description: Define user scenarios and acceptance criteria

  - id: plan
    depends_on: [specify]
    prompt_template: plan.md

  - id: implement
    depends_on: [tasks]
    prompt_template: implement.md

This is what spec-kitty next uses to determine step ordering.

mission.yaml (Configuration)

Mission configuration: workflow phases, expected artifacts, commands, agent context, and validation rules. (The former v1 state-machine blocks — initial, states, transitions, guards, inputs, outputs — were retired with the mission-DSL v1 runtime in dead-port-disposition-01M1TZVN; step sequencing is authored in mission-runtime.yaml instead.)

name: "Software Dev Kitty"
domain: "software"
artifacts:
  required: [spec.md, plan.md, tasks.md]
  optional: [data-model.md, quickstart.md]
workflow:
  phases:
    - name: "research"
    - name: "implement"
    - name: "review"
agent_context: |
  You are a software development agent following TDD practices.
mcp_tools:
  required: [filesystem, git]
  recommended: [code-search, test-runner]
validation:
  checks: [git_clean, all_tests_pass, kanban_complete]

mission-steps/ (Agent Prompts)

Markdown files shown to agents at each step:

mission-steps/software-dev/specify/prompt.md — Instructions for writing the specification
mission-steps/software-dev/plan/prompt.md — Instructions for creating the implementation plan
mission-steps/software-dev/tasks/prompt.md — Instructions for creating tasks and work packages
mission-steps/software-dev/implement/prompt.md — Instructions for implementing a work package
mission-steps/software-dev/review/prompt.md — Instructions for reviewing a work package
mission-steps/software-dev/accept/prompt.md — Instructions for final acceptance validation

templates/ (Content Templates)

Scaffolding files for artifacts:

spec-template.md — Starting structure for spec.md
plan-template.md — Starting structure for plan.md
task-prompt-template.md — Starting structure for WP prompt files
tasks-template.md — Starting structure for tasks.md

Doctrine Composition Layer

Missions are backed by structured doctrine artifacts that define action behavior and link to reusable knowledge.

MissionStepContract (Action Contracts)

Each public action (specify, plan, implement, review) has a step contract that defines its internal structure:

# implement.step-contract.yaml
id: implement
action: implement
mission: software-dev
schema_version: "1.0"
steps:
  - id: setup-workspace
    description: "Create or enter the WP workspace"
  - id: implement-code
    description: "Write code following governance constraints"
    delegates_to:
      kind: tactic
      candidates: [tdd-red-green-refactor, zombies-tdd]
  - id: validate
    description: "Run tests and lint checks"

The delegates_to field links a step to doctrine artifacts. This is how mission behavior connects to the knowledge layer: the contract says what to do, the referenced tactic/directive/procedure says how.

Procedure (Reusable Workflow Primitives)

Procedures are multi-step doctrine artifacts with prerequisites and ordered steps. They are the reusable building blocks that step contracts delegate to. Each procedure describes a complete mini-workflow (e.g., a refactoring sequence, a test-first bug fix, a situational assessment).

Procedures live in packs/built-in/procedures/ (shipped) or .kittify/procedures/ (project-local). Access via ActiveCharterService:

procedure = service.procedures.get("refactoring")
# procedure.steps → ordered list of actions
# procedure.prerequisites → what must be true before starting
# All procedures: read packs/built-in/procedures/ (built-in) or
# .kittify/charter-packs/procedure/ (project layer, written by `spec-kitty charter new`)

To validate project-layer doctrine artifacts:

spec-kitty charter validate .kittify/charter-packs

Agent Profiles (Role-Based WP Assignment)

Agent profiles define roles, specializations, and boundaries for work package assignment. Each profile has these sections: purpose, specialization (languages, frameworks, boundaries), collaboration (handoffs, outputs), mode_defaults, and initialization_declaration. Doctrine references are authored on the top-level *-references fields (directive-references, tactic-references, toolguide-references, styleguide-references); the retired context-sources block was removed (mission doctrine-drg-silent-drop-boundary; #3629).

Profiles do not use relationship fields such as specializes_from. Lineage and specialization relationships belong in the doctrine DRG; profile matching uses weighted signals (language, framework, file path, keyword, exact-id).

Work-package actions are matched to roles through the profiles themselves, not through a mission.yaml block: the shipped built-in mission.yaml files carry no task_types mapping. A work package records its intended role on its agent_profile frontmatter, profile matching scores the candidates by the same weighted signals (language, framework, file path, keyword, exact-id), and spec-kitty dispatch routes an ad-hoc request to the fitting profile.

# Discover activated profiles (--all for the full on-disk catalog)
spec-kitty agent profile list

# Inspect a profile's boundaries and initialization context (resolved
# through DRG lineage and context sources)
spec-kitty agent profile show <profile-id>

There is no separate hierarchy command: specialization lineage is declared in the doctrine DRG (see the org-pack DRG YAML / generated graph.yaml); profile show displays the resolved result.

Action Indices (Doctrine Scoping)

Each mission action has an index that declares which doctrine artifacts are relevant to that step:

# packs/built-in/missions/software-dev/actions/implement/index.yaml
action: implement
directives: [TEST_FIRST]
tactics: [tdd-red-green-refactor, zombies-tdd, acceptance-test-first]
styleguides: [python-implementation]
toolguides: []
procedures: [implementation-handoff]

The charter context builder uses these indices to scope what gets injected into the agent prompt at each step. This prevents agents from seeing review-scoped doctrine during implementation and vice versa.

Gating (post-retirement)

The mission-DSL v1 guard expressions (artifact_exists(...), gate_passed(...), all_wp_status(...), any_wp_status(...), input_provided(...), event_count(...)) were retired with the DSL runtime (dead-port-disposition-01M1TZVN). Gating now lives on two surfaces:

Step progression — the mission-runtime.yaml DAG: a step runs only after every step in its depends_on list has completed.
WP lane transitions — the status model (append-only event log): the 9-lane transition matrix, review gates, and dependency gating (a WP with dependencies cannot be claimed until each is approved or done).

Artifact expectations are declared as artifacts.required in mission.yaml and checked by the mission's validation.checks at acceptance.

Template Resolution (6-Tier Chain)

When a command prompt is needed, spec-kitty resolves the current doctrine mission-step prompt:

Scope	Path	Purpose
Package	`packs/built-in/missions/mission-steps/<mission>/<step>/prompt.md`	Built-in default
Project layer	`.kittify/charter-packs/...`	Project-local overrides where supported
Org layer	org Charter Pack	Shared organization doctrine where installed

The package default is always the fallback. Legacy command-templates paths are pre-migration artifacts, not the current package layout.

Selecting a Mission Type

The mission type is set when you create a mission with /spec-kitty.specify. It's recorded in meta.json and cannot be changed after creation.

Commands:

# List available mission types (activated for this project)
spec-kitty mission-type list
# Every visible mission type, activated or not
spec-kitty charter mission-type list --include-inactive

# Specify a mission with a specific mission type
spec-kitty specify --mission-type research "What are the best auth patterns?"

# Check which mission type a mission uses
cat kitty-specs/<mission-slug>/meta.json | jq .mission

Decision guide:

If you're...	Use mission type
Building a component, fixing a bug, refactoring	`software-dev`
Investigating, evaluating options, literature review	`research`
Planning architecture, roadmaps, design docs	`plan`
Writing tutorials, API docs, how-to guides	`documentation`

The Two State Surfaces

Missions track two orthogonal state surfaces:

Mission-type state — which phase of the workflow are we in?

discovery → specify → plan → tasks → implement → review → accept → merge

Managed by mission-runtime.yaml DAG and spec-kitty next.

WP status — where is each work package in its lifecycle?

planned → claimed → in_progress → for_review → in_review → approved → done
                                                    ↕
                                                 blocked / canceled

Managed by the status model (append-only event log).

Together they determine what spec-kitty next returns: "we're in the implement phase, WP01 is done, WP02 is in_progress, WP03 is planned — your next action is implement WP03."

Runtime Bootstrap

On every CLI invocation, ensure_runtime() runs:

Checks ~/.kittify/cache/version.lock — if version matches, fast path (< 100ms)
If stale: copies mission files from installed package to ~/.kittify/missions/
Uses file locking to prevent concurrent corruption

This ensures ~/.kittify/ always matches the installed spec-kitty version.

CLI Surface Contract

`agent action implement` / `agent action review` — text-only output

The canonical agent surfaces spec-kitty agent action implement <wp_id> and spec-kitty agent action review <wp_id> return plain text only. They do not accept a --json flag. Passing --json to either command produces a Typer exit-2 error.

The top-level command spec-kitty implement does accept --json, but it is the internal allocator used by the harness — not the surface intended for agent prompt steps. Do not invoke spec-kitty implement --json from prompt steps; that is an internal-only surface. Use spec-kitty agent action implement <wp_id> for work-package execution.

Summary:

Command	`--json`?	Intended for
`spec-kitty agent action implement <wp_id>`	no	Agent prompt steps
`spec-kitty agent action review <wp_id>`	no	Agent prompt steps
`spec-kitty implement <wp_id>`	yes	Internal harness allocator only

If you need structured output from the implement action, use spec-kitty agent tasks status --json to query the resulting WP state after the action completes.

Workspace recovery

If a worktree is missing or corrupted, use the real recovery command (post-#2135, the former worktree repair subcommand no longer exists):

spec-kitty doctor workspaces --fix

This removes husk directories (entries in .worktrees/ that lack a .git entry) without touching registered live worktrees.

References

references/mission-comparison-matrix.md -- Side-by-side comparison of all 4 mission types

About this skill

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spec-kitty / spec-kitty

src/charter/offering/skills/spec-kitty-mission-system/SKILL.md

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License MITImported 10/14/2026Version v2Bundled 1 file

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spec-kitty-mission-system

How Missions Work

The Core Concept

The Hierarchy: Mission Type → Mission → Work Package → Workspace

meta.json (Mission → Mission Type Link)

The 4 Built-In Mission Types

software-dev (default)

research

plan

documentation

Mission Type Definition Files

mission-runtime.yaml (Runtime DAG)

mission.yaml (Configuration)

mission-steps/ (Agent Prompts)

templates/ (Content Templates)

Doctrine Composition Layer

MissionStepContract (Action Contracts)

Procedure (Reusable Workflow Primitives)

Agent Profiles (Role-Based WP Assignment)

Action Indices (Doctrine Scoping)

Gating (post-retirement)

Template Resolution (6-Tier Chain)

Selecting a Mission Type

The Two State Surfaces

Runtime Bootstrap

CLI Surface Contract

agent action implement / agent action review — text-only output

Workspace recovery

References

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`agent action implement` / `agent action review` — text-only output