OCR Review-to-Approval Loop

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/spencermarx/open-code-review/ocr-review-loop

Any agent

npx skills add spencermarx/open-code-review --skill ocr-review-loop

Clone the repo

git clone --depth 1 https://github.com/spencermarx/open-code-review

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/spencermarx/open-code-review/ocr-review-loop.svg)](https://agentmods.dev/skills/spencermarx/open-code-review/ocr-review-loop)

Your own site

<a href="https://agentmods.dev/skills/spencermarx/open-code-review/ocr-review-loop"><img src="https://agentmods.dev/badge/skills/spencermarx/open-code-review/ocr-review-loop.svg" alt="Measured on agentmods" height="20"></a>

Per session 178 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,723 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin unknown No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00178	$0.01723
Opus 5	$0.00089	$0.00861
Sonnet 5	$0.00036	$0.00345
Haiku 4.5	$0.00018	$0.00172

Measured today against content hash a23cd80ee010, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

OCR Review-to-Approval Loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/ocr-review-loop/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

What This Skill Does

Orchestrates an autonomous review → address → re-review loop on a pull request, using OCR's existing multi-agent review pipeline, until the review reaches an APPROVE verdict — then runs one final address pass for leftover suggestions. Every review and every address round is posted to the GitHub PR as a comment, producing a transparent, reviewable audit trail.

It is a thin orchestrator over two skills it does not reimplement:

/ocr:review → .ocr/commands/review.md (the 8-phase multi-agent review that emits a verdict)
/ocr:address → .ocr/commands/address.md (corroborate feedback against code, then implement)

When to Use

"Review and address in a loop until it's approved."
"Iterate /ocr:review and /ocr:address on this PR."
"Auto review-and-fix PR #N to a clean approval."

When NOT to Use (use the underlying skills directly)

A single review with no auto-fixing → /ocr:review.
Addressing one existing review's feedback → /ocr:address.
There is no PR / not on a feature branch (this skill commits and posts).

Prerequisites

On a non-default git branch with an open GitHub PR (the loop commits + pushes each round).
gh CLI installed and authenticated (for posting; skip with --no-post).
OCR set up in the repo (.ocr/ exists, ocr CLI available). Run /ocr:doctor if unsure.
A green-ish working tree: any uncommitted changes will become part of what is reviewed.

Arguments

/ocr-review-loop [pr] [--team <spec>] [--max-rounds N] [--no-final-suggestions] [--no-post]

pr (optional): PR number. Default: the open PR for the current branch.
--team <spec> (optional): reviewer team override (reviewer-id:count,...), forwarded to /ocr:review. Default: the project's default team (ocr team resolve).
--max-rounds N (optional): safety cap on review rounds. Default: 5. The loop never runs forever.
--no-final-suggestions (optional): stop at APPROVE; skip the final suggestions pass.
--no-post (optional): run locally; do not post to GitHub. Present the trail in-chat instead.

Read the full file on GitHub · 117 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

today First seen · 117 lines · 178 tokens per session scan A a23cd80ee010

Subscribe to this mod's changes

OCR Review-to-Approval Loop is a skill published in the GitHub repository spencermarx/open-code-review (352 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 178 tokens to every session and 1,723 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.