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 boundsj/agent-skills --skill review-alertsgit clone --depth 1 https://github.com/boundsj/agent-skillsWrote 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/boundsj/agent-skills/review-alerts)<a href="https://agentmods.dev/skills/boundsj/agent-skills/review-alerts"><img src="https://agentmods.dev/badge/skills/boundsj/agent-skills/review-alerts/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/boundsj/agent-skills/review-alerts"><img src="https://agentmods.dev/badge/skills/boundsj/agent-skills/review-alerts.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 | $0.00080 | $0.01347 |
| Opus 5.5 | $0.00032 | $0.00539 |
| Sonnet 5.5 | $0.00016 | $0.00269 |
| Haiku 4.5 | $0.00008 | $0.00135 |
Grade A, and why
review-alerts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Alerts
Show a prioritized summary of PRs waiting for the caller's review. Never starts an actual review.
Prerequisites
This skill expects:
ghCLI installed and authenticatedjqavailable onPATH
Step 1: Validate Inputs
This skill requires two parameters: repo (GitHub owner/name) and user (GitHub login).
If either is missing, ask the user:
I need two things to check your reviews:
- **repo**: GitHub repository (e.g., `owner/repo`)
- **user**: Your GitHub username (e.g., `octocat`)
Which repo and user should I check?
And stop until the user provides them. Do NOT auto-detect from git context or gh api user.
Step 2: Discover PRs
Run the bundled discovery script with explicit flags:
PR_JSON=$("<skill-dir>/find-my-reviews.sh" --detailed --repo "$REPO" --user "$USER")
Where <skill-dir> is the directory containing this SKILL.md file.
If the script returns an empty array ([]), respond with:
Nothing to review right now. Enjoy your day!
And stop.
Step 3: Assess Priority and Review Complexity
Priority
For each PR in the JSON, assign a priority level:
High (any of):
- Label contains: urgent, hotfix, blocking, critical, P0, P1, high-priority, expedite
- Base branch matches:
release/*,hotfix/*,production - Age 7+ days
Medium (any of):
- Age 3-7 days
- Moderate size with activity
Low:
- Created less than 3 days ago, no urgency signals
Sort: High first, then Medium, then Low. Within same priority, oldest first.
Review Complexity
For each PR, assess review complexity:
Low: changed_files <= 3 AND (additions + deletions) < 50 AND no security-sensitive paths High: changed_files > 15 OR (additions + deletions) > 500 OR touches auth/crypto/security paths Medium: everything else
Security-sensitive path patterns: auth/, crypto/, keychain/, Token, Password, Secret, security/, credential
Step 4: Format the Report as ASCII Table
What ships with it
1 file 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.
- 2mo ago First seen · 144 lines · 80 tokens per session scan A 99e2f7a8abf2
review-alerts is a skill published in the GitHub repository boundsj/agent-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 80 tokens to every session and 1,347 once invoked, about $0.0003 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 2026-08-31.
review-alerts compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| review-alertsboundsj/agent-skills | Skill | 2 | 4mo ago | MIT | A | 80 | Claude Code, Codex |
| prYeachan-Heo/oh-my-claudecode | Skill | 41k | today | MIT | A | 47 | Claude Code |
| merge-batchdiegosouzapw/OmniRoute | Skill | 83k | 6d ago | MIT | A | 46 | Claude Code |
| otto-pr-review-loopomnigent-ai/omnigent | Skill | 11k | today | Apache-2.0 | A | 17 | Claude Code, Codex |
| engram-branch-prGentleman-Programming/engram | Skill | 7.8k | today | MIT | A | 37 | Claude Code, Codex |
| github-contributordaymade/claude-code-skills | Skill | 1.5k | today | MIT | A | 105 | Claude Code, Codex |
| revdiffumputun/revdiff | Skill | 947 | 2d ago | MIT | A | 250 | Claude Code |