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.
npx agentmods add skills/openai/codex/babysit-prnpx skills add openai/codex --skill babysit-prgit clone --depth 1 https://github.com/openai/codexWhat 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 | $0.00114 | $0.03602 |
| Opus 5 | $0.00057 | $0.01801 |
| Sonnet 5 | $0.00023 | $0.00720 |
| Haiku 4.5 | $0.00011 | $0.00360 |
Grade A, and why
babysit-pr 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 yesterday.
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.
Copies of this mod
7 near-identical copies found in the catalogue:
- babysit-pr — 100% identical, 0 lines differ
- babysit-pr — 100% identical, 0 lines differ
- babysit-pr — 100% identical, 0 lines differ
- babysit-pr — 100% identical, 0 lines differ
- babysit-pr — 100% identical, 0 lines differ
- babysit-pr — 100% identical, 0 lines differ
- babysit-pr — 92% identical, 42 lines differ
How it starts
The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Babysitter
Objective
Babysit a PR persistently until one of these terminal outcomes occurs:
- The PR is merged or closed.
- A situation requires user help (for example CI infrastructure issues, repeated flaky failures after retry budget is exhausted, permission problems, or ambiguity that cannot be resolved safely).
- Optional handoff milestone: the PR is currently green + mergeable + review-clean. Treat this as a progress state, not a watcher stop, so late-arriving review comments are still surfaced promptly while the PR remains open.
Do not stop merely because a single snapshot returns idle while checks are still pending.
Inputs
Accept any of the following:
- No PR argument: infer the PR from the current branch (
--pr auto) - PR number
- PR URL
Core Workflow
- When the user asks to "monitor"/"watch"/"babysit" a PR, start with the watcher's continuous mode (
--watch) unless you are intentionally doing a one-shot diagnostic snapshot. - Run the watcher script to snapshot PR/review/CI state (or consume each streamed snapshot from
--watch). - Inspect the
actionslist in the JSON response. - If
diagnose_ci_failureis present, inspect failed run logs and classify the failure. - If the failure is likely caused by the current branch, patch code locally, commit, and push. Do not patch random flaky tests, CI infrastructure, dependency outages, runner issues, or other failures that are unrelated to the branch.
- If
process_review_commentis present, inspect surfaced published review items and decide whether to address them. - If a review item is actionable and correct, patch code locally, commit, push, and then resolve the associated review thread only when allowed by the GitHub state mutation policy below.
- Do not post replies to human-authored review comments/threads unless the user explicitly confirms the exact response. If a human review item is non-actionable, already addressed, or not valid, surface the item and recommended response to the user instead of replying on GitHub.
- If the failure is likely flaky/unrelated and
retry_failed_checksis present, rerun failed jobs with--retry-failed-now. - If both actionable review feedback and
retry_failed_checksare present, prioritize review feedback first; a new commit will retrigger CI, so avoid rerunning flaky checks on the old SHA unless you intentionally defer the review change. - On every loop, look for newly surfaced review feedback before acting on CI failures or mergeability state, then verify mergeability / merge-conflict status (for example via
gh pr view) alongside CI. - After any push or rerun action, immediately return to step 1 and continue polling on the updated SHA/state.
- If you had been using
--watchbefore pausing to patch/commit/push, relaunch--watchyourself in the same turn immediately after the push (do not wait for the user to re-invoke the skill). - Repeat polling until
stop_pr_closedappears or a user-help-required blocker is reached. A green + review-clean + mergeable PR is a progress milestone, not a reason to stop the watcher while the PR is still open. - Maintain terminal/session ownership: while babysitting is active, keep consuming watcher output in the same turn; do not leave a detached
--watchprocess running and then end the turn as if monitoring were complete.
What ships with it
5 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.
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.
- yesterday First seen · 224 lines · 114 tokens per session scan A 8e4b149a345e
babysit-pr is a skill published in the GitHub repository openai/codex (120,598 stars, last pushed today), licensed Apache-2.0. It adds 114 tokens to every session and 3,602 once invoked, about $0.0006 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 2026-08-30.
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