Borrowing it
Nothing to install: this file belongs to warpdotdev-demos/cloud-factory-demo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/warpdotdev-demos/cloud-factory-demo/main/.agents/skills/improve-review-pr/SKILL.mdgit clone --depth 1 https://github.com/warpdotdev-demos/cloud-factory-demoWrote 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/warpdotdev-demos/cloud-factory-demo/improve-review-pr)<a href="https://agentmods.dev/skills/warpdotdev-demos/cloud-factory-demo/improve-review-pr"><img src="https://agentmods.dev/badge/skills/warpdotdev-demos/cloud-factory-demo/improve-review-pr/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/warpdotdev-demos/cloud-factory-demo/improve-review-pr"><img src="https://agentmods.dev/badge/skills/warpdotdev-demos/cloud-factory-demo/improve-review-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 20 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 87 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 88 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00068 | $0.01488 |
| Opus 5.5 | $0.00027 | $0.00595 |
| Sonnet 5.5 | $0.00014 | $0.00298 |
| Haiku 4.5 | $0.00007 | $0.00149 |
Grade A, and why
improve-review-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 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve Review PR
Run a once-per-day outer loop over the automated code-review stage.
The inner loop is already running: review-pr comments on pull requests throughout the day. This skill is the outer loop: read how humans reacted to those comments, extract durable organizational knowledge, and update the review skill so the next inner-loop runs get better.
Goal
Improve future automated reviews by learning from human validation and correction of previous review-pr comments.
Do not re-review product code. Do not restate one-off PR opinions. Only capture knowledge that should change how the review agent behaves on future PRs.
Inputs
- The current checkout of the repository that owns
.agents/skills/review-pr/SKILL.md - GitHub API access via authenticated
gh - Optional lookback window, default last 24 hours
- Optional
feedback_corpus.jsonproduced by:python3 .agents/skills/improve-review-pr/scripts/collect_review_feedback.py \ --repo OWNER/REPO \ --since-hours 24 \ --output feedback_corpus.json
If feedback_corpus.json is missing, run the collector yourself before analyzing.
Workflow
1. Collect the day's review-agent interactions
Run or read feedback_corpus.json.
The corpus should include, for the lookback window:
- Pull requests that received an automated review from the review agent
- The review agent's top-level review bodies and inline comments
- Human replies to those comments
- Human reactions (for example
+1,eyes,confused,thumbs down) when available - Whether the human accepted a suggestion, dismissed it, edited around it, or explicitly disagreed
- Whether the PR author or another reviewer later fixed the same issue, ignored it, or called it wrong
Identify the review agent by login when possible (github-actions[bot], a bot account, or a configured login). Prefer comments that originated from the review-pr publish path.
2. Score each feedback item
For each human interaction, classify the outcome:
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 · 157 lines · 68 tokens per session scan A e1b4730c9ad1
improve-review-pr is a skill published in the GitHub repository warpdotdev-demos/cloud-factory-demo (405 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 1,488 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 2026-08-30.
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