are-implement
SkillDocs & knowledgeExecute implementation with and without ARE documentation (experimental)
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the are-implement skill
What this skill tells your AI
The instructions your AI receives, as published by geoloeg-ist/agents-reverse-engineer in .claude/skills/are-implement/SKILL.md and read by ahel’s review.
Execute the implementation phase with and without ARE documentation to measure impact.
Steps
-
Read version: Read
.claude/ARE-VERSION→ store as$VERSION. Show the user:agents-reverse-engineer v$VERSION -
Run the implement command:
npx agents-reverse-engineer@$VERSION implement --backend claude $ARGUMENTSNote: When
--plan-id <id>is provided, the task description is loaded from the stored plan — no need to pass it explicitly. -
Wait for completion — the command runs two sequential AI implementation sessions (without docs, then with docs) and outputs a comparison table.
-
On completion, summarize the comparison results:
- Implementation metrics (files created/modified, lines added/deleted)
- Test results (if
--run-testswas used) - Build and lint results (if
--run-build/--run-lintwas used) - Cost and latency comparison
- Quality evaluation scores (if
--evalwas used)
This re-uses branches created by /are-plan and executes the implementation in both worktrees:
- With ARE docs: Full documentation available during implementation
- Without ARE docs: Source code only (ARE artifacts stripped)
The comparison measures how much ARE documentation improves implementation quality.
Options:
--eval: Run AI quality evaluator on both implementations--eval-model <name>: Model for the evaluator (default: same as --model)--model <name>: AI model to use for implementation--task-slug <slug>: Reference existing plan by slug (default: auto-generate from task)--plan-id <id>: Reference existing plan by ID (shown byare planon completion)--run-tests: Execute test suite and include results in metrics--run-build: Execute build and verify success--run-lint: Run linter and include error/warning counts--dry-run: Show what would happen without executing--list: List all saved implementation comparisons--show <id>: View a previous comparison by date--force: Overwrite existing branches for the same task--debug: Show verbose AI execution details
Signals
- GitHub stars
- 20
- Forks
- 5
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
are-implement- Source
- github.com/geoloeg-ist/agents-reverse-engineer