Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a task feels too large or vague to start, a spec needs breaking into implementable units, work must be parallelized across agents or sessions, or implementation order is unclear. Produces ordered tasks with acceptance criteria. Skip when the spec already contains well-defined tasks.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Attribution: Sourced from addyosmani/agent-skills by Addy Osmani, licensed MIT. Modified for this collection.
Decompose work into small, verifiable tasks with explicit acceptance criteria. Good task breakdown is the difference between an agent that completes work reliably and one that produces a tangled mess. Every task should be small enough to implement, test, and verify in a single focused session.
When NOT to use: Single-file changes with obvious scope, or when the spec already contains well-defined tasks.
Before writing any code, operate in read-only mode:
Do NOT write code during planning. The output is a plan document, not implementation.
Map what depends on what:
Database schema
│
├── API models/types
│ │
│ ├── API endpoints
│ │ │
│ │ └── Frontend API client
│ │ │
│ │ └── UI components
│ │
│ └── Validation logic
│
└── Seed data / migrations
Implementation order follows the dependency graph bottom-up: build foundations first.
Instead of building all the database, then all the API, then all the UI — build one complete feature path at a time:
Bad (horizontal slicing):
Task 1: Build entire database schema
Task 2: Build all API endpoints
Task 3: Build all UI components
Task 4: Connect everything
Good (vertical slicing):
Task 1: User can create an account (schema + API + UI for registration)
Task 2: User can log in (auth schema + API + UI for login)
Task 3: User can create a task (task schema + API + UI for creation)
Task 4: User can view task list (query + API + UI for list view)
Each vertical slice delivers working, testable functionality.
Each task follows this structure:
## Task [N]: [Short descriptive title]
**Description:** One paragraph explaining what this task accomplishes.
**Acceptance criteria:**
- [ ] [Specific, testable condition]
- [ ] [Specific, testable condition]
**Verification:**
- [ ] Tests pass: `npm test -- --grep "feature-name"`
- [ ] Build succeeds: `npm run build`
- [ ] Manual check: [description of what to verify]
**Dependencies:** [Task numbers this depends on, or "None"]
**Files likely touched:**
- `src/path/to/file.ts`
- `tests/path/to/test.ts`
**Estimated scope:** [Small: 1-2 files | Medium: 3-5 files | Large: 5+ files]
Arrange tasks so that:
Add explicit checkpoints:
## Checkpoint: After Tasks 1-3
- [ ] All tests pass
- [ ] Application builds without errors
- [ ] Core user flow works end-to-end
- [ ] Review with human before proceeding
| Size | Files | Scope | Example |
|---|---|---|---|
| XS | 1 | Single function or config change | Add a validation rule |
| S | 1-2 | One component or endpoint | Add a new API endpoint |
| M | 3-5 | One feature slice | User registration flow |
| L | 5-8 | Multi-component feature | Search with filtering and pagination |
| XL | 8+ | Too large — break it down further | — |
If a task is L or larger, it should be broken into smaller tasks. An agent performs best on S and M tasks.
When to break a task down further:
# Implementation Plan: [Feature/Project Name]
## Overview
[One paragraph summary of what we're building]
## Architecture Decisions
- [Key decision 1 and rationale]
- [Key decision 2 and rationale]
## Task List
### Phase 1: Foundation
- [ ] Task 1: ...
- [ ] Task 2: ...
### Checkpoint: Foundation
- [ ] Tests pass, builds clean
### Phase 2: Core Features
- [ ] Task 3: ...
- [ ] Task 4: ...
### Checkpoint: Core Features
- [ ] End-to-end flow works
### Phase 3: Polish
- [ ] Task 5: ...
- [ ] Task 6: ...
### Checkpoint: Complete
- [ ] All acceptance criteria met
- [ ] Ready for review
## Risks and Mitigations
| Risk | Impact | Mitigation |
|------|--------|------------|
| [Risk] | [High/Med/Low] | [Strategy] |
## Open Questions
- [Question needing human input]
When multiple agents or sessions are available:
| Rationalization | Reality |
|---|---|
| "I'll figure it out as I go" | That's how you end up with a tangled mess and rework. 10 minutes of planning saves hours. |
| "The tasks are obvious" | Write them down anyway. Explicit tasks surface hidden dependencies and forgotten edge cases. |
| "Planning is overhead" | Planning is the task. Implementation without a plan is just typing. |
| "I can hold it all in my head" | Context windows are finite. Written plans survive session boundaries and compaction. |
Before starting implementation, confirm:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
日本語の概要は準備中です。原文の説明を表示しています。
Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
日本語の概要は準備中です。原文の説明を表示しています。
Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
日本語の概要は準備中です。原文の説明を表示しています。
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
日本語の概要は準備中です。原文の説明を表示しています。