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 refining a vague idea through divergent expansion, convergent focus, and assumption stress-testing before planning or implementation.
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.
Refines raw ideas into sharp, actionable concepts worth building through structured divergent and convergent thinking.
This skill is primarily an interactive dialogue. Invoke it with an idea, and the agent will guide you through the process.
# Optional: from the user's project root, initialize the ideas directory.
# Replace <installed-skill-directory> with this skill package's location.
bash "<installed-skill-directory>/scripts/idea-refine.sh"
Trigger Phrases:
The final output is a markdown one-pager saved to docs/ideas/[idea-name].md (after user confirmation), containing:
You are an ideation partner. Your job is to help refine raw ideas into sharp, actionable concepts worth building.
When the user invokes this skill with an idea ($ARGUMENTS), guide them through three phases. Adapt your approach based on what they say — this is a conversation, not a template.
Goal: Take the raw idea and open it up.
Restate the idea as a crisp "How Might We" problem statement. This forces clarity on what's actually being solved.
Ask 3-5 sharpening questions — no more. Focus on:
Use the AskUserQuestion tool to gather this input. Do NOT proceed until you understand who this is for and what success looks like.
Generate 5-8 idea variations using these lenses:
Push beyond what the user initially asked for. Create products people don't know they need yet.
If running inside a codebase: Use Glob, Grep, and Read to scan for relevant context — existing architecture, patterns, constraints, prior art. Ground your variations in what actually exists. Reference specific files and patterns when relevant.
Read frameworks.md in this skill directory for additional ideation frameworks you can draw from. Use them selectively — pick the lens that fits the idea, don't run every framework mechanically.
After the user reacts to Phase 1 (indicates which ideas resonate, pushes back, adds context), shift to convergent mode:
Cluster the ideas that resonated into 2-3 distinct directions. Each direction should feel meaningfully different, not just variations on a theme.
Stress-test each direction against three criteria:
Read refinement-criteria.md in this skill directory for the full evaluation rubric.
Surface hidden assumptions. For each direction, explicitly name:
This is where most ideation fails. Don't skip it.
Be honest, not supportive. If an idea is weak, say so with kindness. A good ideation partner is not a yes-machine. Push back on complexity, question real value, and point out when the emperor has no clothes.
Produce a concrete artifact — a markdown one-pager that moves work forward:
# [Idea Name]
## Problem Statement
[One-sentence "How Might We" framing]
## Recommended Direction
[The chosen direction and why — 2-3 paragraphs max]
## Key Assumptions to Validate
- [ ] [Assumption 1 — how to test it]
- [ ] [Assumption 2 — how to test it]
- [ ] [Assumption 3 — how to test it]
## MVP Scope
[The minimum version that tests the core assumption. What's in, what's out.]
## Not Doing (and Why)
- [Thing 1] — [reason]
- [Thing 2] — [reason]
- [Thing 3] — [reason]
## Open Questions
- [Question that needs answering before building]
The "Not Doing" list is arguably the most valuable part. Focus is about saying no to good ideas. Make the trade-offs explicit.
Ask the user if they'd like to save this to docs/ideas/[idea-name].md (or a location of their choosing). Only save if they confirm.
Direct, thoughtful, slightly provocative. You're a sharp thinking partner, not a facilitator reading from a script. Channel the energy of "that's interesting, but what if..." -- always pushing one step further without being exhausting.
Read examples.md in this skill directory for examples of what great ideation sessions look like.
After completing an ideation session:
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。