According to AlphaMoat, as of 2026-10, Brainstorming has 10,220 downloads and 5 stars — ranked by downloads #2,669 among 10,914 AI skills in this ranking, and #357 of 1,632 in AI Agent.
The list defaults to total downloads and can also be sorted by stars; detail-page rankings use total downloads.
| Platform | Downloads | Stars | Comments |
|---|---|---|---|
| ClawHub ↗ | 6.8K | 2 | - |
| SkillHub ↗ | 10.2K | 5 | — |
Platforms count differently and update at different times.
Reviews project context, asks focused questions, compares alternatives and validates design sections before implementation. It is a planning method rather than proof of design correctness. Obtain authorization before writing design files, committing to git or setting up implementation work.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-10 | 10.2K | — | 5 |
| 2026-09 | 8.3K | — | 5 |
| 2026-08 | 7.6K | +13.9% | 5 |
| 2026-07 | 6.7K | — | 5 |
| 2026-05 | 5.3K | — | 4 |
| 2026-03 | 1.6K | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: brainstorming description: "You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation." --- # Brainstorming Ideas Into Designs ## Overview Help turn ideas into fully formed designs and specs through natural collaborative dialogue.
Start by understanding the current project context, then ask questions one at a time to refine the idea. Once you understand what you're building, present the design in small sections (200-300 words), checking after each section whether it looks right so far. ## The Process **Understanding the idea:** - Check out the current project state first (files, docs, recent commits) - Ask questions one at a time to refine the idea - Prefer multiple choice questions when possible, but open-ended is fine too - Only one question per message - if a topic needs more exploration, break it into multiple questions - Focus on understanding: purpose, constraints, success criteria **Exploring approaches:** - Propose 2-3 different approaches with trade-offs - Present options conversationally with your recommendation and reasoning - Lead with your recommended option and explain why **Presenting the design:** - Once you believe you understand what you're building, present the design - Break it into sections of 200-300 words - Ask after each section whether it looks right so far - Cover: architecture, components, data flow, error handling, testing - Be ready to go back and clarify if something doesn't make sense ## After the Design **Documentation:** - Write the validated design to `docs/plans/YYYY-MM-DD-<topic>-design.md` - Use elements-of-style:writing-clearly-and-concisely skill if available - Commit the design document to git **Implementation (if continuing):** - Ask: "Ready to set up for implementation?" - Use superpowers:using-git-worktrees to create isolated workspace - Use superpowers:writing-plans to create detailed implementation plan ## Key Principles - **One question at a time** - Don't overwhelm with multiple questions - **Multiple choice preferred** - Easier to answer than open-ended when possible - **YAGNI ruthlessly** - Remove unnecessary features from all designs - **Explore alternatives** - Always propose 2-3 approaches before settling - **Incremental validation** - Present design in sections, validate each - **Be flexible** - Go back and clarify when something doesn't make sense
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
Finds skills in local and remote catalogs from a plain-language request and recommends what to install.
A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
A guide to creating and updating effective Claude skills.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
以李白(701—762,字太白,号青莲居士,世称诗仙)的诗学观、意象系统与口吻,进行唐诗赏析、文学评论、考据问答与拟体创作。触发词:李白、李太白、太白、青莲居士、诗仙、谪仙人、将进酒、静夜思、蜀道难、唐诗、盛唐诗人、浪漫主义诗人、拟李白风格、以李白视角、李白会怎么写、用李白口吻。能力:诗词鉴赏与风格DNA解析、诗学观与文学谱系评述、诗句出处考据与真伪边界标注、以李白口吻答问、拟李白体旧体诗(必显式标注非原作)。适用边界:仅用于唐代文学与古典诗歌相关话题,非此类话题不触发;本 skill 为文学普及与研究辅助,不代表李白本人立场,所拟之作均非李白原作;涉及生平佚事、篇目归属等内容不构成学术定论,皆标存疑或待考。
Data as of 2026-10. Downloads and stars are aggregated monthly from public skill registries (ClawHub, SkillHub). See data methodology.