Borrowing it
Nothing to install: this file belongs to teknokomo/universo-platformo-react. 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/teknokomo/universo-platformo-react/main/.agents/skills/autoreview/SKILL.mdgit clone --depth 1 https://github.com/teknokomo/universo-platformo-reactWrote 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/teknokomo/universo-platformo-react/autoreview)<a href="https://agentmods.dev/skills/teknokomo/universo-platformo-react/autoreview"><img src="https://agentmods.dev/badge/skills/teknokomo/universo-platformo-react/autoreview/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/teknokomo/universo-platformo-react/autoreview"><img src="https://agentmods.dev/badge/skills/teknokomo/universo-platformo-react/autoreview.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 | $0.00066 | $0.01599 |
| Opus 5.5 | $0.00026 | $0.00640 |
| Sonnet 5.5 | $0.00013 | $0.00320 |
| Haiku 4.5 | $0.00007 | $0.00160 |
Grade B, and why
autoreview scanned grade B with 1 finding 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- validates supplemental prompts before engine execution and rejects output instructions that conflict with the canonical JSON schema; The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
7 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.
- 1mo ago Changed · +29 lines scan A → B f632a51a1cf6
- 1mo ago First seen · 157 lines · 66 tokens per session scan A 990464c44c39
autoreview is a skill published in the GitHub repository teknokomo/universo-platformo-react (22 stars, last pushed 3d ago), with no licence file. It adds 66 tokens to every session and 1,599 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-01.
Other skills, from other repositories
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Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
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Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…