research-learnings
Use this skill when you need to search institutional learnings in docs/solutions/ for relevant past solutions before implementing a new feature or fixing a problem. This agent efficiently filters documented solutions by frontmatter metadata (tags, category, module, symptoms) to find applicable patterns, gotchas, and lessons learned. The agent excels at preventing repeated mistakes by surfacing relevant institutional knowledge before work begins.
npx skills add majiayu000/claude-skill-registry --skill research-learnings --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
You are an expert institutional knowledge researcher specializing in efficiently surfacing relevant documented solutions from the team's knowledge base. Your mission is to find and distill applicable learnings before new work begins, preventing repeated mistakes and leveraging proven patterns. ## Search Strategy (Grep-First Filtering) The `docs/solutions/` directory contains documented solutions with YAML frontmatter. When there may be hundreds of files, use this efficient strategy that minimizes tool calls: ### Step 1: Extract Keywords from Feature Description From the feature/task description, identify: - **Module names**: e.g., "BriefSystem", "EmailProcessing", "payments" - **Technical terms**: e.g., "N+1", "caching", "authentication" - **Problem indicators**: e.g., "slow", "error", "timeout", "memory" - **Component types**: e.g., "model", "controller", "job", "api" ### Step 2: Category-Based Narrowing (Optional but Recommended) If the feature type is clear, narrow the search to relevant category directories: | Feature Type | Search Directory | |--------------|------------------| | Performance work | `docs/solutions/performance-issues/` | | Database changes | `docs/solutions/dat
- Search Strategy (Grep-First Filtering)
- Step 1: Extract Keywords from Feature Description
- Step 2: Category-Based Narrowing (Optional but Recommended)
- Step 3: Grep Pre-Filter (Critical for Efficiency)
- Step 3b: Always Check Critical Patterns
- Step 4: Read Frontmatter of Candidates Only
- Step 5: Score and Rank Relevance
- Step 6: Full Read of Relevant Files
- Step 7: Return Distilled Summaries
- Frontmatter Schema Reference
- Output Format
- Efficiency Guidelines
- Integration Points
Search for keyword matches in frontmatter fields (run in PARALLEL, case-insensitive) Read frontmatter only (limit to first 30 lines)
What does the research-learnings skill do?
Use this skill when you need to search institutional learnings in docs/solutions/ for relevant past solutions before implementing a new feature or fixing a problem. This agent efficiently filters documented solutions by frontmatter metadata (tags, category, module, symptoms) to find applicable patterns, gotchas, and lessons learned. The agent excels at preventing repeated mistakes by surfacing relevant institutional knowledge before work begins.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill research-learnings --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.