Borrowing it
Nothing to install: this file belongs to mmnto-ai/totem. 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/mmnto-ai/totem/main/.agents/skills/review-reply/SKILL.mdgit clone --depth 1 https://github.com/mmnto-ai/totemWrote 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/mmnto-ai/totem/review-reply)<a href="https://agentmods.dev/skills/mmnto-ai/totem/review-reply"><img src="https://agentmods.dev/badge/skills/mmnto-ai/totem/review-reply/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/mmnto-ai/totem/review-reply"><img src="https://agentmods.dev/badge/skills/mmnto-ai/totem/review-reply.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Prompt Injection · line 6 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00029 | $0.03475 |
| Opus 5.5 | $0.00012 | $0.01390 |
| Sonnet 5.5 | $0.00006 | $0.00695 |
| Haiku 4.5 | $0.00003 | $0.00347 |
Grade A, and why
review-reply 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Triage PR review comments from all bots for PR $ARGUMENTS.
Before Phase 1: confirm each invoked bot's review is on the head
A review trigger is the operator's to post, and what a bot does with it is not ours to control — so before triaging, confirm every invoked bot's review against the review object for THIS head sha, or the bot's summary comment for that sha, never a green commit status on the head: CodeRabbit's status settles green on every push head whether or not it reviewed that head (Review completed on the sha it reviewed, Review skipped on every push head it did not), and a PENDING status means still reviewing; Greptile's green Greptile Review check run does mark the sha it reviewed; GCA posted neither a status nor a check run on any GCA-reviewed sha measured so far. The reads, in order — the head sha; every review with the sha it was submitted against; the sha each Greptile summary comment names as its Last reviewed commit; the sha each CodeRabbit summary comment names as covered in its final_review_risk_coverage marker — so both halves compare a printed sha against a printed head, never an improvised one:
gh pr view $ARGUMENTS --json headRefOid --jq .headRefOid
gh api --paginate "repos/{owner}/{repo}/pulls/$ARGUMENTS/reviews" --jq '.[] | [.user.login, .commit_id, .state, .submitted_at] | join(" ")'
gh api --paginate "repos/{owner}/{repo}/issues/$ARGUMENTS/comments" --jq '.[] | select(.user.login == "greptile-apps[bot]") | .body | capture("Last reviewed commit:.*?/commit/(?<sha>[0-9a-f]{40})") | .sha'
gh api --paginate "repos/{owner}/{repo}/issues/$ARGUMENTS/comments" --jq '.[] | select(.user.login == "coderabbitai[bot]") | .body | capture("coveredCommitId.:.(?<sha>[0-9a-f]{40})") | .sha'
A summary-comment verdict is read from the PR's issue comments and matched to the head by the Last reviewed commit sha its own body names — never by the comment's timestamp, which an in-place re-review does not advance. A chat reply or silence with no review to confirm is not a pass under the cadence of one external pass per chosen bot, so the pass is a standalone re-trigger, posted by the operator on the same terms — that bot's first pass, not a re-invoke, which the cadence reserves for risky rework with the reason recorded in the round comment. Before merging on the other reviewers, either wait one acknowledgement window (about 12 minutes from the trigger) or merge and record the late acknowledgement as one line on the PR thread naming the bot and the time it acknowledged.
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 · +10 lines · +10 tokens per session b2a8f7dc917f
- 1mo ago Changed · +2 lines 192e0fc9863c
- 1mo ago Changed · +13 lines c97052e1afb1
- 1mo ago Changed · +6 lines ccdb9e0e0ae2
- 1mo ago Changed · +2 lines 3a3d05bdc75e
- 1mo ago Changed · +14 lines 1bdbf2339441
- 1mo ago Changed 123fd927ee84
- 1mo ago First seen · 80 lines · 19 tokens per session scan A 33ef1baf444c
review-reply is a skill published in the GitHub repository mmnto-ai/totem (18 stars, last pushed today), licensed Apache-2.0. It adds 29 tokens to every session and 3,475 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 2026-08-30.
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