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 | $0.00019 | $0.02000 |
| Opus 5 | $0.00010 | $0.01000 |
| Sonnet 5 | $0.00004 | $0.00400 |
| Haiku 4.5 | $0.00002 | $0.00200 |
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 today.
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 — 96 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.
Phase 1: Fetch & Categorize (Deterministic)
Run the triage command to fetch, normalize, deduplicate, and categorize all bot comments:
pnpm totem triage-pr $ARGUMENTS
This outputs a categorized inbox grouped by blast radius (Security → Architecture → Convention → Nits) with cross-bot deduplication already applied. The heavy lifting is done in TypeScript — no LLM math needed.
STOP HERE. Present the output to the user and wait for them to specify actions. Do NOT proceed to Phase 2 until the user replies.
Phase 2: Execute Actions (Bulk Support)
The user may type individual IDs (e.g., fix 4, 11) OR use bulk actions:
fix all securitydefer all nitsextract all architecture
fix <numbers | category>
Mark items as will-fix. No API calls — just acknowledge. The user will make code changes next.
defer <numbers | category> [ticket]
Auto-reply on the PR acknowledging the deferral:
- CodeRabbit items: Reply inline to each thread with "Tracked in #NNN" or "Deferred — not blocking for this PR."
- GCA items: DO NOT reply inline. Batch ALL GCA responses into ONE issue comment:
@gemini-code-assistfollowed by a numbered list addressing each finding. Usegh pr comment $ARGUMENTS --body-file -and pipe the comment body via stdin. - ghcq items:
github-code-quality[bot]has no known @-listener (attested: no in-org tag attempt has drawn a response and none is documented — mmnto-ai/totem#2626) — do not tag it; treat its dispositions as audit-trail-only. - SARIF items: No reply needed (our own tool).
nit <numbers | category>
Same as defer but reply text is "Acknowledged — nit / by design."
extract <numbers | category>
For each selected finding, generate a lesson and call mcp__totem-dev__add_lesson (or equivalent):
- Use the bot's finding as the lesson body
- Add relevant tags from the file path and finding category
- The lesson will automatically get
lifecycle: nurserytreatment
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.
- today Changed · +2 lines 3a3d05bdc75e
- yesterday Changed · +14 lines 1bdbf2339441
- 6d ago Changed 123fd927ee84
- 10d 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 (17 stars, last pushed today), licensed Apache-2.0. It adds 19 tokens to every session and 2,000 once invoked, about $0.0001 per session on Opus 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.
Other skills, from other repositories
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
printing-press-output-review
Internal sub-skill: agentic review of a printed CLI's sampled command output for plausibility issues that rule-based checks can't encode (substring-match relevance, format bugs, silent source drops, ranking failures). Invoked via the Skill tool by the main printing-press skill at Phase 4.85 and printing-press-polish…
review
Validate plans, execution, or PRs against wish criteria — returns SHIP / FIX-FIRST / BLOCKED with severity-tagged gaps.
review
Adversarial fresh-context review of an increment before it ships. Every finding cites path:line and is re-verified. Use when saying "review", "grill this", or "critique the implementation".
genie-orca-review
Independent, read-only review of a group, a wish, or a PR on Orca — SHIP / FIX-FIRST / BLOCKED with severity-tagged findings. Council and retro are this skill with a different input.
argot-check
Score your working changes with argot — flag code foreign to this repo's own patterns (unfamiliar dependencies, APIs, constructs), functions the repo already has, code filed in the wrong place, imports that break the repo's layering, and tests weakened, disabled, or deleted alongside a production change — before…