Run AI-powered code review through Alibaba's open-code-review (`ocr`) CLI instead of hand-reviewing diffs. Use when the user asks to review code, review a PR/MR, review staged/unstaged/untracked changes, review a single commit, compare two branches, or scan whole files for bugs, security issues, performance problems, and quality concerns. Picks the lightest invocation (workspace review, branch range, single commit, or full-file scan), passes business context via `--background`, classifies findings by priority, and optionally applies fixes. Triggers on: open code review, ocr review, ocr scan, alibaba code review, ai code review cli, review my changes, review this pr, review this commit, review this branch, scan repository for issues.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Run AI-powered code review through Alibaba's open-code-review (`ocr`) CLI instead of hand-reviewing diffs. Use when the user asks to review code, review a PR/MR, review staged/unstaged/untracked changes, review a single commit, compare two branches, or scan whole files for bugs, security issues, performance problems, and quality concerns. Picks the lightest invocation (workspace review, branch range, single commit, or full-file scan), passes business context via `--background`, classifies findings by priority, and optionally applies fixes. Triggers on: open code review, ocr review, ocr scan, alibaba code review, ai code review cli, review my changes, review this pr, review this commit, review this branch, scan repository for issues.
allowed-tools
Bash Read Write Edit Grep Glob WebFetch
license
Apache-2.0
compatibility
Requires the `ocr` CLI (install via `npm install -g @alibaba-group/open-code-review` or a GitHub release binary; macOS, Linux, Windows). Requires a configured LLM provider (Anthropic- or OpenAI-compatible) before the first review. Plugin-installable via `npx skills add`, the Claude Code plugin marketplace, or the Codex plugin marketplace. Routes evidence-first human review judgment to `code-review`, Git mechanics to `git-workflow`, and live-failure reproduction to `debugging`.
Open Code Review — routing-first AI review via the ocr CLI
Keyword: open-code-review · ocr review · ocr scan
OpenCodeReview (OCR) is an open-source
AI code-review CLI from Alibaba. It reads Git diffs (or whole files) and produces
structured, line-level review comments through a configured LLM.
This skill is the operator front door for the ocr binary: it decides the lightest
invocation that answers the user's question, supplies business context, classifies the
findings, and only then applies fixes. It does not re-implement human review judgment —
that lives in the neighboring code-review skill.
When to use this skill
The user wants an automated review of working-copy changes, a commit, a branch range, or a PR/MR
The user wants to scan whole files (no diff) to audit an unfamiliar or pre-migration codebase
The user wants the review wired into a coding agent (Claude Code / Codex slash command) or CI/CD
The user needs help configuring the ocr LLM provider or testing connectivity
The user wants OCR comments classified by priority and selectively auto-fixed
If ocr llm test fails, the user must configure a provider. Never invent or hardcode an
API key — stop and ask the user for credentials, then use one path:
# Interactive (recommended for humans)
ocr config provider
ocr config model
# Non-interactive (CI/scripts) — built-in provider
ocr config set provider anthropic
ocr config set providers.anthropic.api_key <api-key>
ocr config set providers.anthropic.model claude-sonnet-4-6
# Environment variables (highest priority — best for CI)export OCR_LLM_URL=https://api.anthropic.com/v1/messages
export OCR_LLM_TOKEN=<api-key>
export OCR_LLM_MODEL=claude-opus-4-6
export OCR_USE_ANTHROPIC=true
Read the commit messages, branch name, or the user's stated intent and distill one short
requirement sentence. Pass it through --background (-b) — OCR uses it to judge whether
the change implements the requirement, not just whether the code parses.
Step 3: Choose the lightest invocation that fits
Pick exactly one primary mode:
User intent
Command
Review the working copy (staged + unstaged + untracked)
The wrapper scripts default to --audience agent, which suppresses progress UI and emits
only the final summary — the right mode when an agent (not a human terminal) consumes output.
# Workspace review with context
bash scripts/run-review.sh -b "Adding rate limiting to the login API"# Branch range, JSON output for parsing, lower concurrency to dodge rate limits
bash scripts/run-review.sh --from origin/main --to feature-x --format json --concurrency 4
# Single commit; --background auto-fills from the commit message when omitted
bash scripts/run-review.sh --commit abc123
# Full-file scan of a subdirectory, capped spend, skipping generated/test files
bash scripts/run-scan.sh --path internal --exclude '**/*_test.go,**/generated/**' --max-tokens-budget 500000
OCR returns comments with path, content, start_line/end_line (both 0 = positioning
failed), optional suggestion_code, existing_code, and thinking. Group them by priority:
High — obvious bugs, security issues, clear mistakes, or well-founded fixes with a precise proposal
Medium — reasonable but context-dependent concerns, style/perf suggestions, manual-fix items
Low — likely false positives, missing context, nitpicks — discard silently unless the user wants everything
Report High and Medium grouped by level; cite path:line for each.
Step 5: Fix only with intent and verification
If the user explicitly asked to "review and fix", apply High/Medium fixes directly when safe and well-defined
If the user only asked to "review", ask before changing any code
For complex fixes needing manual work, describe what must be done instead of guessing
Always re-run the relevant tests/build and confirm fixes with the user before committing
When asked to review, return a compact, priority-grouped brief:
## Code Review Results**Mode**: workspace | commit <sha> | <base>..<head> | scan
**Files reviewed**: N
**Issues**: X high / Y medium
### High priority-**`path/to/file.go:42`** — what is wrong
> Recommendation: how to fix
### Medium priority-**`path/to/other.ts:88`** — concern + context
### Decision- ran <command>; fixes applied | fixes proposed (awaiting approval) | route-out to <skill>
Examples
Example 1: "Review my changes"
Run bash scripts/run-review.sh -b "<context>" (workspace mode), then classify and report.
Example 2: "Review this PR against main"
Run bash scripts/run-review.sh --from origin/main --to <branch> -b "<context>".
Example 3: "Audit this unfamiliar repo for issues"
Use bash scripts/run-scan.sh --preview first to see the file list and token estimate, then scan.
Example 4: "Review commit abc123 and fix the high-confidence bugs"
Run bash scripts/run-review.sh --commit abc123, apply only High findings with precise fixes, verify, confirm.
