Open Code Review Setup

Lifecycle operation: detect → install → configure → verify. For PR metadata, agent labels, and the token-spend semantics of rule.json, use ai-agent-pr-metadata — this skill only installs and validates the plumbing.

1. Detect

ls .opencodereview/ .github/workflows/ 2>/dev/null | grep -i -E 'ocr|open-code-review'
gh variable list --repo <owner>/<repo> | grep OCR_
gh secret list  --repo <owner>/<repo> | grep OCR_LLM_AUTH_TOKEN

• All present → audit only: confirm the action is pinned to a SHA, the maintainer gate matches the repo owner, and the model var is still wanted. Do not clobber local customizations.
• Anything missing → install/configure the missing pieces below.

2. Install

Branch per git workflow (feature/issue-N-open-code-review, PR to the default integration branch — never commit to main/staging directly).

1. Copy templates/open-code-review.yml and templates/ocr-manual-review.yml into .github/workflows/.
2. Set the maintainer gate in both files (github.event.pull_request.user.login == '<maintainer>' / github.event.comment.user.login == '<maintainer>') to the account that opens PRs (the bot account whose token creates PRs, if agents open them).
3. Copy templates/rule.json to .opencodereview/rule.json and adapt exclude to the stack:
   • JS/TS: node_modules/**, dist/**, pnpm-lock.yaml, package-lock.json
   • PHP/Laravel: vendor/**, storage/**, decide explicitly on tests/** (built-in excludes do not cover *Test.php)
   • Static/binary assets (images, fonts, brand files): exclude their directories
   • Keep **/*.md, **/*.json, **/*.lock unless docs/schema review is wanted
4. Pin alibaba/open-code-review to the current release SHA (never a floating tag).

3. Configure

Repo variables (non-secret):

gh variable set OCR_LLM_URL              --repo <owner>/<repo> --body "https://openrouter.ai/api/v1"
gh variable set OCR_LLM_MODEL_FREE       --repo <owner>/<repo> --body "nvidia/nemotron-3-super-120b-a12b:free"
gh variable set OCR_LLM_MODEL_FALLBACK   --repo <owner>/<repo> --body "<paid-model-id>"
gh variable set OCR_USE_ANTHROPIC        --repo <owner>/<repo> --body "false"

• Default free model: nvidia/nemotron-3-super-120b-a12b:free (OpenRouter free tier, 262k ctx). Override per repo only when asked; verify any model ID against https://openrouter.ai/api/v1/models first.
• Fallback model: use a known-working paid model already proven for this deployment context if one exists, otherwise a cheap general-purpose paid model — verify against https://openrouter.ai/api/v1/models first, same as the free model. The preflight probes OCR_LLM_MODEL_FREE first and falls back to OCR_LLM_MODEL_FALLBACK on any non-2xx response (quota exhaustion, outage, etc.).
• Secret: OCR_LLM_AUTH_TOKEN = OpenRouter key. Pipe it from the local secrets store straight into gh secret set — never print, log, or commit the value:

grep -m1 '^OPENROUTER_API_KEY=' <env-file> | cut -d= -f2- | tr -d '\r' | gh secret set OCR_LLM_AUTH_TOKEN --repo <owner>/<repo>

• No key available, or preflight returns 401 → stop and use hitl-blocker; do not retry automation.

4. Verify

1. Open the setup PR. The pull_request trigger runs the workflow from the PR head, so the PR reviews itself — watch gh run list.
2. Preflight must pass (HTTP 2xx). 401 = bad key (→ step 3 HITL); other codes = check OCR_LLM_URL/model ID.
3. Confirm the review comment lands on the PR and ends with the Review metadata footer per ai-agent-pr-metadata.
4. Compare tool_calls/input_tokens in the workflow log's === OCR result === block after tuning rule.json to confirm spend dropped.
5. ocr rules check --rule .opencodereview/rule.json <path> verifies a path matches before relying on it (no LLM call).
6. Confirm both probe branches are exercised at least once: the free-model probe should succeed under normal conditions; to test the fallback branch deliberately, temporarily set OCR_LLM_MODEL_FREE to an invalid model ID via gh variable set in a throwaway test, confirm the workflow logs "Free model unavailable ... falling back to paid model", then revert the variable to its real value.
7. The preflight probe only proves the model is reachable (1-token reply) — it does not prove a real review will finish within llm_timeout. A free-tier model can pass the probe and still time out mid-review on a large diff, and since fallback only triggers on a failed probe (not a failed review), a slow-but-reachable free model gets no fallback. llm_timeout: '300' is the template default and a starting point, not a guarantee — raise it further per repo if reviews are timing out on large diffs.

Guardrails

• Never print, commit, or comment secret values; public artifacts must use Credential details: [redacted].
• The manual /ocr-review workflow must stay gated to the maintainer — an open comment trigger lets anyone spend LLM budget.
• Never check out or execute PR-supplied code in OCR workflows; the action diffs content remotely.
• Existing rule.json exclusions are deliberate cost controls — treat removals as spend increases, not cleanups.