Use when checking the overall health of a skills library. Run doctor, validate, check for stale skills, and verify generated docs are in sync.
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Clarify requirements before implementing. Do not use automatically, only when invoked explicitly.
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See how to installReview the instructions the agent will receive before you install.
Ask the minimum set of clarifying questions needed to avoid wrong work; do not start implementing until the must-have questions are answered (or the user explicitly approves proceeding with stated assumptions).
Treat a request as underspecified if after exploring how to perform the work, some or all of the following are not clear:
If multiple plausible interpretations exist, assume it is underspecified.
Ask 1-5 questions in the first pass. Prefer questions that eliminate whole branches of work.
Make questions easy to answer:
defaults to accept all recommended/default choices)1b 2a 3c); restate the chosen options in plain language to confirmUntil must-have answers arrive:
If the user explicitly asks you to proceed without answers:
Once you have answers, restate the requirements in 1-3 sentences (including key constraints and what success looks like), then start work.
1) Scope?
a) Minimal change (default)
b) Refactor while touching the area
c) Not sure - use default
2) Compatibility target?
a) Current project defaults (default)
b) Also support older versions: <specify>
c) Not sure - use default
Reply with: defaults (or 1a 2a)
Originally created by @thsottiaux
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Use when checking the overall health of a skills library. Run doctor, validate, check for stale skills, and verify generated docs are in sync.
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Backend API design, database architecture, microservices patterns, and test-driven development. Use for designing APIs, database schemas, or backend system architecture.
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Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.
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Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.
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Use when regenerating README.md and WORK_AREAS.md in a managed library workspace. Always dry-run first to preview changes.
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Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
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