How do I install the Extract Learnings To Issues skill?

Run npx skillmds@latest add vamseeachanta/extract-learnings-to-issues in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Extract Learnings To Issues free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Extract Learnings To Issues?

vamseeachanta (@vamseeachanta) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Extract Learnings To Issues by vamseeachanta

Name: Extract Learnings To Issues
Author: vamseeachanta

File contents

Extract Learnings to Issues

Convert unstructured user thoughts, reflections, and learnings into structured, actionable GitHub issue content that enhances existing issues rather than creating noise.

When to Use

User shares random thoughts/workflow reflections and wants them "captured"
User says "put this into issues" or "filter this to repos"
Post-session learnings that should compound in the repo
User wants their reflections to "straighten the twisted brain"

Pattern

Phase 1: Distill Themes

Parse the unstructured thoughts and extract core themes. Look for:

Problems/gaps identified
Insights about what works (secret sauce)
Proposed improvements or next steps
Metrics or evidence of problems
Connections to existing work

Phase 2: Map to Existing Issues

CRITICAL: Before creating any new issues, search the existing landscape:

cd /mnt/local-analysis/workspace-hub

# Search by label categories
gh issue list --label cat:harness --state open --limit 10
gh issue list --label cat:platform --state open --limit 10
gh issue list --label domain:knowledge --state open --limit 10

# Search by keywords
gh issue search "keyword1 OR keyword2" --state open --limit 10

# View key issues
gh issue view $NUM 2>/dev/null

Map each distilled theme to the most relevant existing issue.

Phase 3: Route as Comments (NOT New Issues)

Write a body file and add as a comment to the existing issue:

cat > /tmp/comment_$NUM.md << 'EOF'
## Context Update (date)

[Distilled insight from user's reflection]

[Specific additions to acceptance criteria]
[Related issues to link]
[Quotes from user for context]
EOF

gh issue comment $NUM --body-file /tmp/comment_$NUM.md

If no existing issue covers a theme, create a focused new issue with clear acceptance criteria and references.

Phase 4: Close Any Unnecessary Issues

If you created issues before realizing they're duplicates:

gh issue comment $NUM --body "Closing as duplicate of #EXISTING - see context added there."
gh issue close $NUM

Key Principles

ROUTE over CREATE: Always prefer enhancing existing issues
Quote the user: Preserve their exact words for context
Be specific: Add actionable acceptance criteria, not just commentary
Cross-link: Connect related issues explicitly
Minimize noise: The goal is signal, not issue proliferation

---name: extract-learnings-to-issuesdescription: Extract unstructured user reflections and learnings, distill core themes, route insights to existing GitHub issues as contextual comments rather than creating duplicates.---# Extract Learnings to IssuesConvert unstructured user thoughts, reflections, and learnings into structured, actionable GitHub issue content that enhances existing issues rather than creating noise.## When to Use- User shares random thoughts/workflow reflections and wants them "captured"- User says "put this into issues" or "filter this to repos"- Post-session learnings that should compound in the repo- User wants their reflections to "straighten the twisted brain"## Pattern### Phase 1: Distill ThemesParse the unstructured thoughts and extract core themes. Look for:- Problems/gaps identified- Insights about what works (secret sauce)- Proposed improvements or next steps- Metrics or evidence of problems- Connections to existing work### Phase 2: Map to Existing IssuesCRITICAL: Before creating any new issues, search the existing landscape:```bashcd /mnt/local-analysis/workspace-hub# Search by label categoriesgh issue list --label cat:harness --state open --limit 10gh issue list --label cat:platform --state open --limit 10gh issue list --label domain:knowledge --state open --limit 10# Search by keywordsgh issue search "keyword1 OR keyword2" --state open --limit 10# View key issuesgh issue view $NUM 2>/dev/null```Map each distilled theme to the most relevant existing issue.### Phase 3: Route as Comments (NOT New Issues)Write a body file and add as a comment to the existing issue:```bashcat > /tmp/comment_$NUM.md << 'EOF'## Context Update (date)[Distilled insight from user's reflection][Specific additions to acceptance criteria][Related issues to link][Quotes from user for context]EOFgh issue comment $NUM --body-file /tmp/comment_$NUM.md```If no existing issue covers a theme, create a focused new issue with clear acceptance criteria and references.### Phase 4: Close Any Unnecessary IssuesIf you created issues before realizing they're duplicates:```bashgh issue comment $NUM --body "Closing as duplicate of #EXISTING - see context added there."gh issue close $NUM```## Key Principles- ROUTE over CREATE: Always prefer enhancing existing issues- Quote the user: Preserve their exact words for context- Be specific: Add actionable acceptance criteria, not just commentary- Cross-link: Connect related issues explicitly- Minimize noise: The goal is signal, not issue proliferation

Frequently asked questions

Extract Learnings To Issues