are-generate

SkillDocs & knowledge

Generate AI-friendly documentation for the entire codebase

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the are-generate skill

What this skill tells your AI

The instructions your AI receives, as published by geoloeg-ist/agents-reverse-engineer in .claude/skills/are-generate/SKILL.md and read by ahel’s review.

Generate comprehensive documentation for this codebase using agents-reverse-engineer.

Steps

  1. Read version: Read .claude/ARE-VERSION → store as $VERSION. Show the user: agents-reverse-engineer v$VERSION

  2. Run the generate command in the background using run_in_background: true:

    npx agents-reverse-engineer@$VERSION generate --backend claude $ARGUMENTS
    
  3. Monitor progress by polling the latest progress log:

    • Wait ~15 seconds (use sleep 15 in Bash), then use Glob to find the latest .agents-reverse-engineer/progress-*.log file, and Read it (use the offset parameter to read only the last ~20 lines for long files)
    • Show the user a brief progress update (e.g. "32/96 files analyzed, ~12m remaining")
    • Check whether the background task has completed using TaskOutput with block: false
    • Repeat until the background task finishes
    • Important: Keep polling even if no progress log exists yet (the command takes a few seconds to start writing)
  4. On completion, read the full background task output and summarize:

    • Number of files analyzed and any failures
    • Number of directories documented
    • Any inconsistency warnings from the quality report

This executes a two-phase pipeline:

  1. File Analysis (concurrent): Discovers files, applies filters, then analyzes each source file via AI and writes .sum summary files with YAML frontmatter (content_hash, file_type, purpose, public_interface, dependencies, patterns).

  2. Directory Aggregation (sequential): Generates AGENTS.md per directory in post-order traversal (deepest first, so child summaries feed into parents), and writes CLAUDE.md pointers.

Options:

  • --dry-run: Preview the plan without making AI calls
  • --eval: Namespace output by backend.model for side-by-side comparison (e.g., file.ts.claude.haiku.sum, AGENTS.claude.haiku.md)
  • --concurrency N: Control number of parallel AI calls (default: auto)
  • --fail-fast: Stop on first file analysis failure
  • --debug: Show AI prompts and backend details
  • --trace: Enable concurrency tracing to .agents-reverse-engineer/traces/

Signals

GitHub stars
20
Forks
5
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
are-generate
Source
github.com/geoloeg-ist/agents-reverse-engineer