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Algol

Skill btmoriarty/algol/skills/algol

Govern code review for a project instead of performing it. Triggers when setting up or running Algol, defining or compiling review policy in .algol/policy.toml, routing a change to the right review engine by policy and undo-cost, reconciling findings from /code-review, ultra, gauntlet, or the deterministic collectors into one record, or recording a review decision with reopens-if conditions. Not a reviewer itself.From its SKILL.md

Install
npx -y skills add btmoriarty/algol --skill algol

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 22 days oldThe repository was created 22 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.6 KB, 485 tokens by cl100k_base, as published. Nobody here has run it

Algol

Govern what gets reviewed and record what was decided. Algol reviews nothing itself.

Algol reads a project's versioned review standards, routes each change to the right engine per policy and undo-cost, reconciles every engine's findings into one record without silently upgrading a heuristic to verified, and preserves each human decision with the conditions that would reopen it.

v0.9.0, local scope. See docs/DESIGN.md for the full design and each references/ file for a component's protocol.

The pieces

  • Policy model: versioned, path-scoped standards in .algol/policy.toml, compiled into review instructions, scanner rules, routing criteria, and a catalog. tools/compile_policy.py, references/policy-model.md.
  • Evidence collectors: seclint (security) and brevlint (style and brevity), deterministic, emitting structured evidence rows. tools/seclint.py, tools/brevlint.py, references/collectors.md.
  • policy-review: a model-pass harness that checks a change against the project's own standards, not a general bug hunt. tools/policy_review.py, references/policy-review.md.
  • router: recommends skip, a collector, policy-review, /code-review, or ultra from policy and undo-cost, and escalates to the deep tier on undo-cost. Recommends, never auto-launches. tools/router.py, references/router.md.
  • reconcile and the record: merges findings into one governed record, keeps verified distinct from heuristic, never upgrades silently, carries reopens-if on each disposition. tools/reconcile.py, tools/record.py, references/reconcile-and-record.md.
  • Deep tier: routes to gauntlet and consumes its run record. tools/gauntlet_adapter.py, references/deep-tier.md.
  • Composed axes: testing to evidence-locked-uat, efficiency to applying-formal-rigor. tools/compose_adapter.py, references/composition.md.
  • Hooks: a non-modifying reversibility guard and a collector reporter. tools/hooks.py, references/hooks.md.

The floor

The tool proposes; a human decides and runs the engine. No finding originates in Algol except the deterministic collector rows. A heuristic is never silently upgraded to verified.

What ships with it: 32 files

143.3 KB alongside SKILL.md, 17 of them executable

references/

tools/

Keep looking

Skills are one crate of 326,452. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.