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baby-sit

Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

88%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a tight, command-level operational playbook: every step is executable, validation checkpoints and feedback loops are explicit, and the failure taxonomy makes triage unambiguous. Its only weaknesses are mild redundancy between the start/stop and stop-conditions sections and inline cloud/local branching that could be split out for a leaner core file.

DimensionReasoningScore

Conciseness

The body is dense and imperative with no explanations of concepts Claude already knows, but there is redundancy: the "Stop conditions" section restates blocking conditions already covered in "Start or stop" steps 5–8 (e.g. "report that no watch is needed" vs. "end local monitoring when checks become non-failing"). It is above the midpoint because nearly every token is operational; not a 5 because the overlapping stop/report logic could be consolidated.

4 / 5

Actionability

Fully executable guidance throughout: exact commands with flags ("gh pr checks --json name,bucket,state,workflow,link", "gh run view <run-id> --log-failed", "gh run rerun <run-id> --failed"), a named tool with actions ("manage_baby_sit" with action "start"/"stop"/"record_retry"), and concrete parameter lists ("passing the canonical PR URL, verified head SHA, failed check name, concise evidence, and GitHub check URL"). Nothing is pseudocode or vague.

5 / 5

Workflow Clarity

Multi-step processes are clearly sequenced with explicit validation checkpoints: "Confirm the failure belongs to the current head SHA", "Re-fetch the PR and complete check set", retry limits ("allow at most three flaky reruns for the current head"), and error-recovery feedback loops ("If GitHub denies the operation, stop and report"; rerun then "return to the bounded foreground watch"). The failure taxonomy (deterministic/flaky/ambiguous/external) acts as a decision checklist, matching the anchor 5 example's validate-fix-retry structure.

5 / 5

Progressive Disclosure

No bundle files exist, and the single SKILL.md is organized under clear, well-sequenced headers (Inputs, Start or stop, Failure diagnosis, Flaky rerun, Stop conditions) with no buried references. It is not a 5 because at ~78 lines the cloud-specific tool mechanics and local/desktop branching are inlined in the main flow, so the operational core could be slightly leaner with peripheral detail (e.g. webhook/fallback behavior) split into a reference file.

4 / 5

Total

18

/

20

Passed

Description

75%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A specific, distinctive, third-person description that clearly states concrete capabilities in the GitHub CI domain. Its main weakness is the absence of explicit trigger guidance — there is no "Use when..." clause telling Claude when the skill applies, which caps completeness and leaves keyword coverage slightly short of comprehensive.

Suggestions

Append an explicit trigger clause, e.g. "Use when the user asks to watch, unblock, or fix CI on a pull request, or mentions flaky GitHub Actions jobs, failing checks, or a red build."

Include common user phrasings as trigger synonyms — "build", "checks", "failing tests", "watch CI" — to broaden natural keyword coverage.

Mention the stop/continue invocation surface (e.g. "or when a failure wakeup continues an active watch") so the when-condition covers the skill's full usage, though keep it concise.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — "Monitor a GitHub pull request until CI is green", "diagnose failures", and "rerun only evidence-backed flaky GitHub Actions jobs" — with a clearly named domain. Coverage is comprehensive for the actions claimed; it is not score 4 because there are no minor gaps among the capabilities it states.

5 / 5

Completeness

The "what" is clear and specific, but there is no explicit "Use when..." clause or equivalent trigger guidance in the description; the when is only weakly implied by the imperative verbs. Per the judging guidelines, a missing 'Use when' clause caps completeness at 3, and it is not a 2 because the 'what' is fully articulated.

3 / 5

Trigger Term Quality

Good keyword coverage with natural terms users would say: "GitHub pull request", "CI", "GitHub Actions", "flaky", "rerun". It falls short of anchor 5 because common variations like "build", "checks", "failing tests", or "watch CI" are missing.

4 / 5

Distinctiveness Conflict Risk

It occupies a clear niche — CI baby-sitting on GitHub pull requests with evidence-gated flaky job reruns — with distinctive triggers like "evidence-backed flaky GitHub Actions jobs". Conflict risk with generic CI/CD or code-review skills is minimal; it is not a 4 because the GitHub-specific, flaky-rerun framing leaves little overlap even with closely related skills.

5 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

langchain-ai/open-swe

Path

openswe/bundled_skills/baby-sit/SKILL.md

Reviewed

Table of Contents

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