totem: Skill for Claude Code

.agents/skills/review-reply/SKILL.md

review-reply is a skill for Claude Code from mmnto-ai/totem. It costs 29 tokens per session (3,475 once invoked), scanned A, original, Apache-2.0.

A workflow for collecting, grouping, and handling automated pull-request review comments from multiple bots.

In plain words
What is it for?
Use it to triage bot comments on a pull request, mark selected items for fixing, defer them, or extract architecture-related work.
Why use it?
It removes duplicate comments and organizes feedback by impact, so you can decide what to fix or defer in one place.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: installed under .agents/ (shared by several agents).

This is mmnto-ai/totem's own configuration. It tells Claude Code how to work on totem itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything totem configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/mmnto-ai/totem/main/.agents/skills/review-reply/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/mmnto-ai/totem

Made for: Claude Code.

Wrote 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.

agentmods badge for review-reply

README.md
[![agentmods](https://agentmods.dev/badge/skills/mmnto-ai/totem/review-reply/github.svg)](https://agentmods.dev/skills/mmnto-ai/totem/review-reply)
Your own site
<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.

agentmods 80×15 button for review-reply

Your own site · 80×15
<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>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,475 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash b2a8f7dc917f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

.agents/skills/review-reply/SKILL.md · 127 lines

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.

Read the full file on GitHub · 127 lines

Changes

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.

  1. 1mo ago Changed · +10 lines · +10 tokens per session b2a8f7dc917f
  2. 1mo ago Changed · +2 lines 192e0fc9863c
  3. 1mo ago Changed · +13 lines c97052e1afb1
  4. 1mo ago Changed · +6 lines ccdb9e0e0ae2
  5. 1mo ago Changed · +2 lines 3a3d05bdc75e
  6. 1mo ago Changed · +14 lines 1bdbf2339441
  7. 1mo ago Changed 123fd927ee84
  8. 1mo ago First seen · 80 lines · 19 tokens per session scan A 33ef1baf444c

Subscribe to this mod's changes

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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