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 skills add yeaight7/agent-powerups --skill pr-review-ci-loopgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yeaight7/agent-powerups/pr-review-ci-loop)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/pr-review-ci-loop"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/pr-review-ci-loop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/pr-review-ci-loop"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/pr-review-ci-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00028 | $0.00829 |
| Opus 5.5 | $0.00011 | $0.00332 |
| Sonnet 5.5 | $0.00006 | $0.00166 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
pr-review-ci-loop 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Combine PR review and CI failure triage into one human-controlled loop. Review findings and CI logs are inputs; they are not permission for autonomous remote actions.
When to Use
- A PR needs both technical review and CI failure triage.
- The user wants a single inspect -> summarize -> patch -> recheck cycle.
- Local fixes depend on accurate PR diff and CI log context.
Do not use when the user only asked for a review, only asked for CI diagnosis, or has not approved edits.
Core Rules
- Remote writes such as push, comment, and request re-review are always opt-in.
- Local changes must be grounded in specific review findings or failing CI checks.
- Prefer GitHub-first flows; treat other CI providers as adaptations.
- Do not apply speculative fixes.
Inputs
- PR number, branch, or URL
- Repository remote and base branch
- CI provider access, preferably
ghfor GitHub-hosted PRs - User approval boundary for local edits and remote writes
Workflow
1. Read PR metadata and diff
gh pr view <pr> --json number,title,author,baseRefName,headRefName,mergeStateStatus,reviewDecision,statusCheckRollup
gh pr diff <pr> --stat
gh pr diff <pr>
Record file count, risky areas, generated files, and public API changes.
2. Identify review risks
Classify findings by severity:
| Category | What to check |
|---|---|
| Correctness | Logic errors, off-by-one, wrong assumptions |
| Tests | Missing coverage for changed behavior, broken assertions |
| Public API | Breaking changes to exports, signatures, or contracts |
| Security | Injection risks, exposed secrets, auth bypass |
| Migration / release risk | Schema changes, feature flags, rollback difficulty |
3. Inspect CI failures
gh pr checks <pr>
gh run view <run-id> --log-failed
Extract the first failing assertion or error line. Categorize each failure as code error, flaky, environment, or config.
4. Produce one readout
REVIEW FINDINGS:
[blocking] <description> - <file:line or section>
[non-blocking] <description>
CI FAILURES:
<check name>: <first error line>
Likely cause: <code error / flaky / env / config>
LIKELY FIXES:
1. <specific change>
2. <specific change>
VALIDATION PLAN:
- Run: <command>
- Expected: <outcome>
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.
- 1mo ago First seen · 123 lines · 28 tokens per session scan A e42cdd1bfa42
pr-review-ci-loop is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 16d ago), licensed Apache-2.0. It adds 28 tokens to every session and 829 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-14.
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kodama-constraints
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hqe
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adk-review
Reviews the uncommitted changes in an adk-python working tree and reports correctness, design, public-API stability, test, sample and documentation gaps as a prioritized findings report, fixing them only when asked. Use when the user asks to review local changes, wants a self-review before opening a pull request, asks…