Coding-agent skills for pull-request work and orchestration: an adversarial multi-pass PR reviewer, PR description and title writing, answering review comments, CI failure triage and PR nursing, a DAG orchestrator that delegates to cheaper subagents, and file-channel coordination between agents. Supports GitHub and GitLab.
95%
Does it follow best practices?
Impact
98%
1.88xAverage score across 10 eval scenarios
Low
Low-risk findings worth noting
Topic file for running pr-reviewer from inside OpenAI Codex (CLI / app). Not yet exercised end to end in staged mode; the dispatch shape below matches the collaboration.spawn_agent schema observed in one Codex session (2026-09). Read your Codex's actual tool schema and model list before dispatching.
Codex has shell access in its sandboxed workspace and is competent with gh and glab. Network requirement: Steps 2 and 4 need outbound network. If the sandbox is offline, the user must check out the PR head beforehand and the skill can only run the "current branch" path, verifying HEAD == headRefOid against metadata the user pastes in. Codex must not invent metadata it could not fetch. The default workspace-write sandbox may not allow $HOME/.cache; Step 3's temp-dir fallback covers that.
Same aliases as the-mister's Codex file.
| Tier | model | reasoning_effort | Used for |
|---|---|---|---|
| Hard | gpt-5.6-sol | high | assessor; escalated reviewer |
| Medium | gpt-5.6-terra | medium | reviewers A, B, C by default |
| Easy | gpt-5.6-luna | low | summary |
Codex takes effort as a dispatch parameter, so "high effort" is reasoning_effort: "high" rather than prompt text. Record the model and effort you passed.
spawn_agent takes task_name, message, fork_turns, model, reasoning_effort. Three calls for the reviewers, then wait_agent repeatedly until all three have returned (it returns on the first completion); only then spawn the assessor:
{
"task_name": "pr_reviewer_a_correctness",
"message": "<reviewer prompt from references/passes.md, fully instantiated>",
"fork_turns": "none",
"model": "gpt-5.6-terra",
"reasoning_effort": "medium"
}fork_turns must be "none": a model override requires a non-full-history fork, and a clean slate is what keeps one reviewer's context out of another. Reviewers must not be able to see each other's results; do not fork from a turn that already contains one.
If the sandbox has no spawn_agent, fall back to single mode and record mode: single (fallback: <reason>) in findings.md so the report says so.
Codex CLI has no /loop primitive at the time of writing (codex#15679; broader scheduling at codex#25466). Use one of:
/pr-reviewer <num> --rounds=3 and keep invoking it until findings.md records converged: true for the current revision."while loop that breaks on the convergence marker (see the awk check in generic.md). Always pass --rounds=N.Step 11 of the skill. If the sandbox is offline, print the report and let the user post it; do not simulate posting.