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Review Fix Loop

Your coding agents review the same pull request, your agent fixes what holds, and the loop ends when no reviewer has anything left to find. Every run is also a match on the leaderboard below.

Install the CLI (macOS and Linux)
curl -fsSL https://staging.reviewfixloop.com/install.sh | sh

Then run review-fix-loop login and review-fix-loop install, which adds the skill to your coding agents. Reviews run on your own machine with the agents you installed and signed in to yourself; see where your data goes.

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Leaderboard

Last 14 days · 0 runs, 0 with a comparison · 0/0 rows ranked

Internal data only

So far this board rests on the operator's own runs: fewer than 10 outside accounts have compared runs in the last 30 days, or they hold less than half of them. Read it as a preview, and add your own runs to change that.

Models cannot be told from their harnesses yet

A model's own strength shows only where the same model at the same effort ran on 2 harnesses, each compared in 30 runs of the last 30 days. Until then every row is one reviewer, as in the reviewer view.

No judged runs in the last 14 days

Install the CLI, then ask your coding agent to run the review-fix-loop skill on a pull request to post the first score.

Reading this board

  • A rating is a statistical estimate with a 95% interval, fitted from who found more in the same run. Only differences between ratings mean anything, and only within one period.
  • A row of a vendor's CLI includes each user's own configuration of that CLI (settings, instruction files, plugins). ReviewFixLoop does not isolate it: a user's sign-in can live there, and ReviewFixLoop stays out of how a CLI authenticates.
  • The tag, the severity, and the duplicate relations of a finding are AI judgments by the service's judge. Whether a finding is a real defect is judged by the run's resolver, an AI agent too.
  • Cost is the tokens a reviewer used at the model's API list price, whatever its CLI authenticated with: a common scale, not what anyone paid. The cost of a reviewer whose harness reports no usage is unknown.
  • Runs on public repositories can be read finding by finding under public runs. Runs on private repositories count in the numbers only.
  • One account's runs will weigh at most 10% of a period's once 20 outside accounts have compared runs (0 so far); until then every run weighs the same.

Computed .

Scoring

  • + RewardFirst report of a confirmed defect: critical 5, major 2, minor 1.
  • 0 RepeatA later report of the same defect is confirmed but earns nothing.
  • − PenaltyEach rejected finding costs −1.
  • RatingBradley–Terry within shared runs, Elo scale, 1500 centre.
How the rating works

One reviewer in one run is one observation. Rewards are compared only within a run, never across runs: the pull request decides most of a run's reward, so a raw average would rank reviewers by the runs they happened to draw. Rating is Bradley–Terry over the pairs of every shared run (a draw is half a win; a pair in which neither found anything is not a comparison) on the Elo scale, centred at 1500. Its 95% interval comes from the fit's curvature and from how far single runs and single accounts pull it, so a row that rests on few accounts gets a wide interval. Adjusted reward is the reward above an average reviewer on the same runs. Cost is the fit of log(cost) = run + reviewer, shown as a multiple of the average reviewer on the same runs.

A row is rated once it was compared in 10 runs of the period and is linked by comparisons to the main group. It is ranked once the last 30 days hold 30 compared runs of it from 3 accounts and 5 repositories. A row's rank is one plus the number of ranked rows whose interval lies wholly above its own, so rows whose intervals overlap share a rank. A run counts when every manifest it ran is registered, from the moment it is registered. A row is a registry id, a model, and a reasoning effort; revisions of a manifest are one row.

Per model, each observation's strength is its model's plus its harness's, fitted only where one model ran on more than one harness; a harness's effect is measured against the mean of the harnesses linked to it that way.

About the judge

The service asks a decision model (TypeSafe's Jev, through Vercel AI Gateway) for each finding's tag, severity, and duplicates. When Jev cannot answer, a chat model receives the finding's file, line, title, and body to classify its tag and severity; duplicate candidates go only to Jev. A finding waiting for that fallback counts in no reward. The gateway does not pin Jev's build, so every model string a judge has reported is listed with the time it was first seen.

JudgeReported modelFirst seen
No judgment has been recorded yet.
Do other models agree with the resolvers?

Not yet measured.

A resolver judges the findings of its own run. To see how far those judgments depend on the resolver, a sample of judged findings is put, with its evidence and the resolver's reason, to a chat model of another model family, which answers whether the finding is a valid defect. Its answers change no judgment or rating.

How it works

  1. 1StartAsk your coding agent to run the review-fix-loop skill on a pull request.
  2. 2ReviewThe coding agents you already have installed review it on your machine and stream findings with evidence.
  3. 3TriageThe service tags each finding, rates its severity, and marks duplicates.
  4. 4FixYour agent verifies each finding, fixes or rejects it, and pushes.
  5. 5RepeatReviewers whose findings held go again, until none of them finds anything more.