Panel review loop
SkillDev toolsLets your agent iteratively review and improve a user-facing surface across real-world projects until a review panel has no blocking concerns.
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 Panel review loop skill
About this capability
Iteratively review and improve a Fallow user-facing surface across representative real-world projects until the panel has no blocking concerns.
What this skill tells your AI
The instructions your AI receives, as published by fallow-rs/fallow in .agents/skills/panel-review-loop/SKILL.md and read by ahel’s review.
The real-world corpus is intentionally untracked. Before its first use, follow
the benchmark setup and run
npm --prefix benchmarks run download-fixtures.
- Define the user-visible surface and success criteria.
- Select representative projects from
benchmarks/fixtures/real-world/. - Capture actual output for each project.
- Run
panel-reviewon the evidence, not on an intended answer. - Implement the smallest coherent improvement that addresses consensus.
- Re-run the same corpus and compare behavior.
- Stop when the panel has no blocks and further changes do not materially improve the surface.
Keep the corpus stable across iterations. Preserve output contracts unless the plan explicitly approves a versioned change.
Signals
- GitHub stars
- 4k
- Forks
- 155
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
panel-review-loop- Source
- github.com/fallow-rs/fallow