Scan memory/learnings to generate a Gotchas table for PRD/PRP sections.
npx skills add https://github.com/jpoley/flowspec --skill gather-learnings
Please help me install this Agent Skill. Skill: gather-learnings Source: https://github.com/jpoley/flowspec/tree/main/.claude/skills/gather-learnings Command: npx skills add https://github.com/jpoley/flowspec --skill gather-learnings
This Skill helps teams quickly extract, synthesize, and organize lessons learned from past projects to populate PRDs and PRPs with actionable Gotchas and risk insights.
Invoke the gather-learnings skill with inputs describing the feature, involved files, relevant keywords, and tags. It returns a markdown table suitable for PRD/PRP insertion.
High-intent search queries and answers about installing and using this skill.
Gather-learnings identifies and curates relevant lessons from your memory and learnings repositories based on file paths, keywords, and tags, then generates a structured Gotchas table with Impact, Mitigation, and Source columns ready to insert into PRDs and PRPs.
Invoke gather-learnings with your feature context, involved file paths, relevant keywords, and tags. It matches against past learnings by direct file reference and contextual relevance, returning a prioritized top-5-8 markdown table ordered by match strength.
Yes. Gather-learnings scans memory for documented failures and risks tagged with keywords matching your current work, synthesizes them into actionable gotchas with mitigation strategies, and links back to original sources for reference.
The Skill ranks findings by direct file path matches first, then by contextual relevance to your supplied keywords and tags, ensuring the most applicable lessons appear at the top of the output table.
Describe your feature, supply the file paths involved in development, provide relevant keywords and tags from your domain, and gather-learnings matches these against stored learnings to produce the table.
Yes. It's designed for feature development contexts where teams need to extract institutional knowledge from shared memory and learnings repositories to populate risk and gotchas sections in collaborative PRDs and PRPs.
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