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Blueprint Curate Docs

  • 55 installs
  • 49 repo stars
  • Updated August 4, 2026
  • laurigates/claude-plugins

Helps with ai & agent building tasks.

About

blueprint-curate-docs is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • blueprint-curate-docs
  • AI & Agent Building
  • AI-coding skill

Blueprint Curate Docs by the numbers

  • 55 all-time installs (skills.sh)
  • Ranked #6,846 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/laurigates/claude-plugins --skill blueprint-curate-docs

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Listed on Skillselion
Installs55
repo stars49
Last updatedAugust 4, 2026
Repositorylaurigates/claude-plugins

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

/blueprint:curate-docs

Curate library or project documentation into ai_docs entries optimized for AI agents - concise, actionable, gotcha-aware context that fits in PRPs.

Usage: /blueprint:curate-docs [library-name] or /blueprint:curate-docs project:[pattern-name]

When to Use This Skill

Use this skill when...Use alternative when...
Creating ai_docs for PRP contextReading raw documentation for ad-hoc tasks
Documenting library patterns for reuseOne-time library usage
Building knowledge base for projectGeneral library research

Context

  • ai_docs directory: !find docs/blueprint -maxdepth 1 -name 'ai_docs' -type d
  • Existing library docs: !find docs/blueprint/ai_docs/libraries -name "*.md" -type f
  • Existing project patterns: !find docs/blueprint/ai_docs/project -name "*.md" -type f
  • Library in dependencies: !find . -maxdepth 1 \( -name package.json -o -name pyproject.toml -o -name requirements.txt \) -exec grep -m1 "^$1[\":@=]" {} +

Parameters

Parse $ARGUMENTS:

  • library-name: Name of library to document (e.g., redis, pydantic)
  • Location: docs/blueprint/ai_docs/libraries/[library-name].md
  • OR project:[pattern-name] for project patterns
  • Location: docs/blueprint/ai_docs/project/[pattern-name].md

Execution

Execute complete documentation curation workflow:

Step 1: Determine target and check existing docs

1. Parse argument to determine if library or project pattern 2. Check if ai_docs entry already exists 3. If exists → Ask: Update or create new version? 4. Check project dependencies for library version

Step 2: Research and gather documentation

For libraries:

  • Find official documentation URL
  • Search for specific sections relevant to project use cases
  • Find known issues and gotchas (WebSearch: "{library} common issues", "{library} gotchas")
  • Extract key sections with WebFetch

For project patterns:

  • Search codebase for pattern implementations: grep -r "{pattern}" src/
  • Identify where and how it's used
  • Document conventions and variations
  • Extract real code examples from project

Step 3: Extract key information

1. Use cases: How/why this library/pattern is used in project 2. Common operations: Most frequent uses 3. Patterns we use: Project-specific implementations (with file references) 4. Configuration: How it's configured in this project 5. Gotchas: Version-specific behaviors, common mistakes, performance pitfalls, security considerations

Sources for gotchas: GitHub issues, Stack Overflow, team experience, official docs warnings.

Step 4: Create ai_docs entry

Generate file at appropriate location (see REFERENCE.md):

  • docs/blueprint/ai_docs/libraries/[library-name].md OR
  • docs/blueprint/ai_docs/project/[pattern-name].md

Include all sections from template: Quick Reference, Patterns We Use, Configuration, Gotchas, Testing, Examples.

Keep under 200 lines total.

Step 5: Add code examples

Include copy-paste-ready code snippets from:

  • Project codebase (reference actual files and line numbers)
  • Official documentation examples
  • Stack Overflow solutions
  • Personal implementation experience

Step 6: Update task registry

Update the task registry entry in docs/blueprint/manifest.json:

jq --arg now "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \
  --argjson processed "${ITEMS_PROCESSED:-0}" \
  --argjson created "${ITEMS_CREATED:-0}" \
  '.task_registry["curate-docs"].last_completed_at = $now |
   .task_registry["curate-docs"].last_result = "success" |
   .task_registry["curate-docs"].stats.runs_total = ((.task_registry["curate-docs"].stats.runs_total // 0) + 1) |
   .task_registry["curate-docs"].stats.items_processed = $processed |
   .task_registry["curate-docs"].stats.items_created = $created' \
  docs/blueprint/manifest.json > tmp.json && mv tmp.json docs/blueprint/manifest.json

Step 7: Validate and save

1. Verify entry is < 200 lines 2. Verify all code examples are accurate 3. Verify gotchas include solutions 4. Save file 5. Report completion

Agentic Optimizations

ContextCommand
Check ai_docs exists`test -d docs/blueprint/ai_docs && echo "YES" \
List library docsls docs/blueprint/ai_docs/libraries/ 2>/dev/null
Check library version`grep "{library}" package.json pyproject.toml 2>/dev/null \
Search for patternsUse grep on src/ for project patterns
Fast researchUse WebSearch for common issues instead of fetching docs

---

For ai_docs template, section guidelines, and example entries, see REFERENCE.md.

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