How do I install the Recipe Decompose skill?

Run npx skillmds@latest add shinpr/recipe-decompose in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Recipe Decompose safe to use?

SkillMD's automated safety review verdict for this skill is CAUTION. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Recipe Decompose?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Recipe Decompose free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Recipe Decompose?

shinpr (@shinpr) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Recipe Decompose by shinpr — Agent Skill

Name: Recipe Decompose
Author: shinpr

Recipe Decompose

Decomposes Linear issues or requirement documents into the fewest implementation-ready value units, then registers the approved tasks in Linear. Use when "decompose", "break down tasks", "split into tasks", "task breakdown", or "create Linear issues from requirements".

shinpr Updated Sep 17, 2026

File contents

Orchestrator Definition

I am an orchestrator. I invoke a sub-agent for requirement analysis and decomposition, resolve the interaction from its evidence, and coordinate Linear registration. All Linear MCP operations are executed directly by the orchestrator. See references/register.md for the registration procedure.

Sub-agent: linear-prism:task-decomposer-linear

Input

$ARGUMENTS contains one of:

A Linear issue URL (e.g., https://linear.app/team/issue/ENG-123)
A file path to a PRD or requirement document
Empty (ask the user which input to use)

Prerequisites

Linear MCP connection is active. Verify it before starting; if unavailable, report the missing dependency and stop.

Workflow Overview

Round 1: Fetch input → Analyze outcome and scope → Continue or ask for a blocking user decision
Round 2: Propose decomposition strategy → Present task list → User adjusts
Round 3: Explicit approval of the current valid task list → Register in Linear

Detailed procedures for each round are in the references/ directory:

Round 1: See references/analyze.md
Round 2: See references/plan.md
Round 3: See references/register.md

Scope Boundaries

In scope:

Fetching and analyzing requirement content from Linear or local files.
Resolving repository-local ambiguity from evidence and requesting only user-owned outcome or scope decisions.
Decomposing requirements into typed tasks with dependency graphs.
Registering tasks as Linear issues.

Out of scope:

Modifying the source PRD or Linear issue directly.
Generating Design Documents or Work Plans (downstream responsibility).
Implementing any task produced by this skill.

Error Handling

Condition	Action
Linear MCP unavailable	Report the missing required dependency and stop
Input URL returns empty or inaccessible content	Present the error, ask user for alternative input
Agent returns `recommended_action: needs_input`	Ask only the returned user-owned questions, then re-analyze
User rejects task list in Round 2	Collect adjustment instructions, re-invoke agent in `revise` mode (see `references/plan.md`)

Completion Criteria

Every current in-scope desired behavior from the input maps to at least one task.
Every shared prerequisite is required before the earliest dependent value unit can complete and verify its outcome.
Each task has explicit service responsibility scopes.
Dependency edges represent real execution or verification blockers.
The exact task list approved by the user is registered in Linear.

---name: recipe-decomposedescription: Decomposes Linear issues or requirement documents into the fewest implementation-ready value units, then registers the approved tasks in Linear. Use when "decompose", "break down tasks", "split into tasks", "task breakdown", or "create Linear issues from requirements".---# Orchestrator DefinitionI am an orchestrator. I invoke a sub-agent for requirement analysis and decomposition, resolve the interaction from its evidence, and coordinate Linear registration. All Linear MCP operations are executed directly by the orchestrator. See `references/register.md` for the registration procedure.**Sub-agent**: `linear-prism:task-decomposer-linear`# Input`$ARGUMENTS` contains one of:- A Linear issue URL (e.g., `https://linear.app/team/issue/ENG-123`)- A file path to a PRD or requirement document- Empty (ask the user which input to use)# Prerequisites- Linear MCP connection is active. Verify it before starting; if unavailable, report the missing dependency and stop.# Workflow Overview```Round 1: Fetch input → Analyze outcome and scope → Continue or ask for a blocking user decisionRound 2: Propose decomposition strategy → Present task list → User adjustsRound 3: Explicit approval of the current valid task list → Register in Linear```Detailed procedures for each round are in the `references/` directory:- **Round 1**: See `references/analyze.md`- **Round 2**: See `references/plan.md`- **Round 3**: See `references/register.md`# Scope Boundaries**In scope**:- Fetching and analyzing requirement content from Linear or local files.- Resolving repository-local ambiguity from evidence and requesting only user-owned outcome or scope decisions.- Decomposing requirements into typed tasks with dependency graphs.- Registering tasks as Linear issues.**Out of scope**:- Modifying the source PRD or Linear issue directly.- Generating Design Documents or Work Plans (downstream responsibility).- Implementing any task produced by this skill.# Error Handling| Condition | Action ||-----------|--------|| Linear MCP unavailable | Report the missing required dependency and stop || Input URL returns empty or inaccessible content | Present the error, ask user for alternative input || Agent returns `recommended_action: needs_input` | Ask only the returned user-owned questions, then re-analyze || User rejects task list in Round 2 | Collect adjustment instructions, re-invoke agent in `revise` mode (see `references/plan.md`) |# Completion Criteria- [ ] Every current in-scope desired behavior from the input maps to at least one task.- [ ] Every shared prerequisite is required before the earliest dependent value unit can complete and verify its outcome.- [ ] Each task has explicit service responsibility scopes.- [ ] Dependency edges represent real execution or verification blockers.- [ ] The exact task list approved by the user is registered in Linear.

Frequently asked questions