Agent skill

parse-ai-analysis

Parse GitHub AI analysis comment sections for architectural alignment, technical feasibility, implementation suggestions, and testing strategy

majiayu000534★ · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill parse-ai-analysis --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Path: skills/analysis/parse-ai-analysis/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Parse AI Analysis ## Purpose Extract structured data from GitHub AI issue analysis comments generated by github-actions bot for use in architecture planning and implementation. ## When to Use - After fetching issue with `fetch-github-issue-analysis` skill - When AI analysis found in issue comments - During conductor Phase 1 (architecture planning) - When extracting specific sections for delegation ## AI Analysis Format The AI analysis comment contains these sections: ```markdown # AI Issue Analysis ## Architectural Alignment [How this issue aligns with current architecture] ## Technical Feasibility [Assessment of implementation difficulty and approach] ## Implementation Suggestions [Specific implementation recommendations] ## Files/Components Affected [List of files that will need changes] ## Testing Strategy [Recommended testing approach] ## Dependencies [Related issues or features] ## Estimated Complexity [Simple/Medium/Complex assessment] ``` ## Instructions ### Step 1: Validate Input ```bash AI_ANALYSIS=$1 # Full AI analysis comment text if [ -z "$AI_ANALYSIS" ]; then echo "❌ Error: No AI analysis provided" exit 1 fi # Check if contains expected heading if ! echo "$AI_ANALYSI

What's inside
Steps it walks through
  1. Purpose
  2. When to Use
  3. AI Analysis Format
  4. Instructions
  5. Step 1: Validate Input
  6. Step 2: Extract Architectural Alignment
  7. Step 3: Extract Technical Feasibility
  8. Step 4: Extract Implementation Suggestions
  9. Step 5: Extract Files/Components Affected
  10. Step 6: Extract Testing Strategy
  11. Step 7: Extract Dependencies
  12. Step 8: Extract Complexity
  13. Step 9: Return Structured Output
  14. Output Format
Ships with 1 file
  • metadata.json
Commands it runs
if [ -z "$AI_ANALYSIS" ]; then
echo "❌ Error: No AI analysis provided"
exit 1
fi
Check if contains expected heading
if ! echo "$AI_ANALYSIS" | grep -q "AI Issue Analysis"; then
echo "⚠️ Warning: Unexpected format - may not be AI analysis"
Extract section between "## Architectural Alignment" and next "##"
sed -n '/## Architectural Alignment/,/^## /p' | \
sed '$d' | \
More from claude-skill-registry
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About this skill
What does the parse-ai-analysis skill do?

Parse GitHub AI analysis comment sections for architectural alignment, technical feasibility, implementation suggestions, and testing strategy

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill parse-ai-analysis --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.

Keep going