Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add WhatIfWeDigDeeper/agent-skills --skill peer-reviewgit clone --depth 1 https://github.com/WhatIfWeDigDeeper/agent-skillsWrote 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/whatifwedigdeeper/agent-skills/peer-review)<a href="https://agentmods.dev/skills/whatifwedigdeeper/agent-skills/peer-review"><img src="https://agentmods.dev/badge/skills/whatifwedigdeeper/agent-skills/peer-review/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/whatifwedigdeeper/agent-skills/peer-review"><img src="https://agentmods.dev/badge/skills/whatifwedigdeeper/agent-skills/peer-review.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 | $0.00163 | $0.08876 |
| Opus 5 | $0.00081 | $0.04438 |
| Sonnet 5 | $0.00033 | $0.01775 |
| Haiku 4.5 | $0.00016 | $0.00888 |
Grade D, and why
peer-review scanned grade D with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- **Stdin transport for external CLIs (gemini, codex)** — for gemini and codex the *untrusted* prompt content (the diff, PR body, and file contents) is sent via stdin, never on argv, so it is not exposed via `ps` / `/pro Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
**Cleanup of `$PROMPT_FILE` and `$WORKDIR` is explicit, not via `trap`.** A `trap 'rm -f "$PROMPT_FILE"' EXIT` fires when the bash subshell exits. Within the single-Bash-call execution required above, the subshell wraps How it starts
The opening of the file, as written. The whole thing — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Peer Review
Get a fresh-context review of in-progress work — staged changes, a branch diff, a PR, or a set of files — without accumulated session assumptions. Returns severity-grouped findings the author can apply or skip.
Arguments
The text following the skill invocation is available as $ARGUMENTS (in Claude Code: /peer-review [target] [options]).
If $ARGUMENTS is help, --help, -h, or ?, print usage and exit:
Usage: /peer-review [target] [--model MODEL] [--focus TOPIC]
Targets (pick one):
(none) Auto-detect: staged, unstaged, or prompt [staged/unstaged/all] if both exist
--staged Staged changes only — skip auto-detection (git diff --staged)
--pr N PR #N diff + description
--branch NAME Branch diff vs default branch
path/to/file-or-dir Specific files or directory
Options:
--model MODEL Reviewer model (default: self — use the current assistant)
`self` means the assistant spawns a fresh instance of itself as reviewer
Explicit Claude models: any claude-* value (internal path — assistant selects model natively)
External CLIs: copilot[:submodel], codex[:submodel], gemini[:submodel]
copilot — npm install -g @github/copilot-cli (or VS Code extension)
codex — npm install -g @openai/codex
gemini — npm install -g @google/gemini-cli
--focus TOPIC Narrow emphasis (e.g. security, consistency, evals)
Does NOT suppress critical findings outside the focus area
Parse $ARGUMENTS left-to-right:
- Strip
--staged→ set target type to staged (explicit-staged flag = true; staged-only, no auto-detection) - Strip
--pr N→ set target type to PR, store N as$PR - Strip
--branch NAME→ set target type to branch, store NAME as$BRANCH - Strip
--model MODEL→ store model override - Strip
--focus TOPIC→ store TOPIC as$FOCUS - Remaining token (if any) → treat as a file/dir path target
What ships with it
4 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.
- 11d ago First seen · 382 lines · 163 tokens per session scan D feae96ec39bf
peer-review is a skill published in the GitHub repository WhatIfWeDigDeeper/agent-skills (2 stars, last pushed 18d ago), licensed MIT. It adds 163 tokens to every session and 8,876 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it D with 2 findings (asks for root, recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
review-work
Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.
critical-code-reviewer
Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
one-way-door
Flags irreversible decisions before commit. Use for data models, infra, auth boundaries, API contracts, event schemas, CI/CD.
map-codebase
Deep architecture report that fans out parallel inspections across different aspects of the codebase (structure, tech stack, APIs, patterns, data flow, dependencies, testing) and synthesizes findings into a comprehensive document at .turbo/codebase-map.md and .turbo/codebase-map.html. Use when the user asks to "map…
reply-to-pr-threads
Draft, confirm, and post replies to GitHub PR review threads. Handles per-category reply formatting, re-fetches thread resolution state so auto-resolved threads are skipped, and posts via GraphQL. Use when the user asks to "reply to PR threads", "post PR thread replies", or "draft PR reply messages".
answer-reviewer-questions
For each reviewer question on a PR, recall implementation reasoning and compose a raw answer. Use when the user asks to "answer reviewer questions", "draft answers to PR questions", or "explain reviewer questions".