Claude Cursor Skill

feature-radar-learn

Extract reusable patterns, architectural decisions, and pitfalls from completed work into .feature-radar/specs/. Captures the "why" behind choices so future sessions build on past experience. MUST use this skill when the user reflects on what worked or didn't, wants to record a d

LLM Mart · 0 points · 0 views 0 listing impressions 0 install-command copies
Virus-scanned Reviewed automatically before listing.

Full trust report

Download runkids-my-skills-feature-radar_feature-radar-learn-7f33dbc.zip · 1 KB
Part of runkids/my-skills — 13 skills

Install

skills CLI npx skills add https://github.com/runkids/my-skills/tree/main/feature-radar/feature-radar-learn
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install runkids-my-skills@llmmart
Git git clone https://github.com/runkids/my-skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole runkids/my-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Extract Learnings

Capture reusable knowledge from completed work into .feature-radar/specs/.

Deep Read

Behavioral Directives

Workflow

  1. Identify the source — ask the user what was just completed (feature, bug fix, refactor, investigation)

  2. Analyze the work — review recent commits, changed files, and implementation decisions

  3. Extract knowledge — classify each reusable piece into exactly one category, and state the classification in your output:

    • Pattern: recurring solution worth replicating (e.g., "three-tier config merge")
    • Decision: architectural choice with rationale (e.g., "YAML over JSON because...")
    • Pitfall: mistake or dead end to avoid
    • Technique: implementation approach that worked well
  4. Write to specs — create or append to .feature-radar/specs/{topic}.md

  5. Checkpoint — State what was written and ask: "I've written to specs/{topic}.md (). Does this look correct, or should I adjust anything?" Wait for user confirmation before proceeding.

  6. Update base.md — increment the specs count in Tracking Summary

File Format

Use the format defined in ../feature-radar/references/SPEC.md § 3.4 (specs/{topic}.md).

Guidelines

  • One topic per file. If the learning spans multiple topics, create multiple files.
  • Name files by the pattern, not by the feature that produced it.
    • Good: yaml-config-merge.md, symlink-vs-copy-tradeoffs.md
    • Bad: audit-feature-learnings.md, v2-refactor-notes.md
  • Append to existing files when the new learning extends a known topic.
  • Keep it concise — future readers need the insight, not the full story.

Example Output

→ Created specs/symlink-vs-copy-tradeoffs.md (Decision)
→ Updated base.md: specs 2 → 3

Completion Summary

Follow the template in ../feature-radar/references/DIRECTIVES.md, with skill name "Learn Complete".

Files (my-skills)
  • SKILL.md 2.8 KB
    ---
    name: feature-radar-learn
    description: |
      Extract reusable patterns, architectural decisions, and pitfalls from completed work
      into .feature-radar/specs/. Captures the "why" behind choices so future sessions build
      on past experience. MUST use this skill when the user reflects on what worked or didn't,
      wants to record a decision or pattern for future use, or hit a dead end worth documenting.
      Use when the user asks to remember, document, or extract lessons from recent work.
      Do NOT use for recording external observations — that's feature-radar-ref's job.
      Do NOT use for archiving completed features — that's feature-radar-archive's job.
    ---
    
    # Extract Learnings
    
    Capture reusable knowledge from completed work into `.feature-radar/specs/`.
    
    ## Deep Read
    
    <HARD-GATE>
    Read and follow `../feature-radar/references/DEEP-READ.md` — complete all 6 steps before proceeding.
    </HARD-GATE>
    
    ## Behavioral Directives
    
    <HARD-GATE>
    Read and follow `../feature-radar/references/DIRECTIVES.md`.
    </HARD-GATE>
    
    ## Workflow
    
    1. **Identify the source** — ask the user what was just completed (feature, bug fix, refactor, investigation)
    2. **Analyze the work** — review recent commits, changed files, and implementation decisions
    3. **Extract knowledge** — classify each reusable piece into exactly one category, and state the classification in your output:
       - **Pattern**: recurring solution worth replicating (e.g., "three-tier config merge")
       - **Decision**: architectural choice with rationale (e.g., "YAML over JSON because...")
       - **Pitfall**: mistake or dead end to avoid
       - **Technique**: implementation approach that worked well
    
    4. **Write to specs** — create or append to `.feature-radar/specs/{topic}.md`
    5. **Checkpoint** — State what was written and ask: "I've written to `specs/{topic}.md` ({classification type}). Does this look correct, or should I adjust anything?" Wait for user confirmation before proceeding.
    6. **Update base.md** — increment the specs count in Tracking Summary
    
    ## File Format
    
    Use the format defined in `../feature-radar/references/SPEC.md` § 3.4 (`specs/{topic}.md`).
    
    ## Guidelines
    
    - One topic per file. If the learning spans multiple topics, create multiple files.
    - Name files by the pattern, not by the feature that produced it.
      - Good: `yaml-config-merge.md`, `symlink-vs-copy-tradeoffs.md`
      - Bad: `audit-feature-learnings.md`, `v2-refactor-notes.md`
    - Append to existing files when the new learning extends a known topic.
    - Keep it concise — future readers need the insight, not the full story.
    
    ## Example Output
    
    ```
    → Created specs/symlink-vs-copy-tradeoffs.md (Decision)
    → Updated base.md: specs 2 → 3
    ```
    
    ## Completion Summary
    
    Follow the template in `../feature-radar/references/DIRECTIVES.md`, with skill name "Learn Complete".
    

Comments (0)

Sign in to join the conversation.

No comments yet.

Reviews (0)

No reviews yet.

Related