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 WrongStack/WrongStack --skill auto-reviewgit clone --depth 1 https://github.com/WrongStack/WrongStackWrote 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/wrongstack/wrongstack/auto-review)<a href="https://agentmods.dev/skills/wrongstack/wrongstack/auto-review"><img src="https://agentmods.dev/badge/skills/wrongstack/wrongstack/auto-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/wrongstack/wrongstack/auto-review"><img src="https://agentmods.dev/badge/skills/wrongstack/wrongstack/auto-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00067 | $0.01774 |
| Opus 5.5 | $0.00027 | $0.00710 |
| Sonnet 5.5 | $0.00013 | $0.00355 |
| Haiku 4.5 | $0.00007 | $0.00177 |
Grade A, and why
auto-review 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 5d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Review — Built-in Plugin
Selection card
- Task: Operate the built-in automatic review plugin.
- Start: Identify the scope and obtain an executable before-proof or review evidence.
- Finish: apply the acceptance checks below; report observed results and unresolved constraints.
Overview
The wstack-auto-review plugin (built into @wrongstack/core) detects
every git-tracked file change during a session and automatically dispatches
a review subagent after a trailing file-quiet window. It extends the Chimera
review pipeline with mid-session reviews while leaving any work still
waiting at session.ended to the post-session Chimera path.
iteration.completed → git diff → trailing quiet window → chimera.review_needed event
↓
Director spawns review subagent
(provider/model from config)
↓
Severity-ranked report → store + mailbox
↓
chimera.review_complete event
↓
chimera.report_available notification
↓
stop (with cascadeOn set: follow-up fix agents)
Status
This is a built-in plugin (packages/core/src/plugins/auto-review-plugin.ts),
NOT a skill-based watcher. It is loaded automatically and enabled by default.
Optional overrides in your config:
{
"extensions": {
"wstack-auto-review": {
"provider": "deepseek",
"model": "deepseek-chat",
"fallbackProfile": "reliable",
"modelSelection": "round-robin",
"debounceMs": 15000,
"maxFilesPerBatch": 15
}
}
}
Requirements
--directorflag (Director mode) — the subagent spawning pipeline (execution.ts) requires the Director to be active. Without it, review events are silently skipped.gitavailable in the session working directory.
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.
- 5d ago Changed 9ad36bb925d9
- 6d ago Changed · +21 lines 1f3e7db9baf3
- 19d ago Changed · -1 lines 507a499cd238
- 29d ago Changed · -40 lines cc30685c25e1
- 1mo ago First seen · 180 lines · 67 tokens per session scan A bbf69307eda4
auto-review is a skill published in the GitHub repository WrongStack/WrongStack (376 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 1,774 once invoked, about $0.0003 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.
Other skills, from other repositories
code-reviewer
Staff-engineer-level code review delivering 10 prioritized actionable findings across architecture, security, performance, and maintainability.
pull-request-review
Review a pull request or branch diff like a staff engineer. Verify it does what the issue asks, find correctness and security defects with file and line evidence, check tests and compatibility, and deliver prioritized, actionable comments. Use for PR review, branch review, and "review my changes".
verify
Answer "is this shippable / done?" with evidence before a commit or PR — fires on "verify", "is this done", "ready to ship?", pre-commit or pre-PR. Part of the Agentsmith harness; runs the project's verify phases and never claims "passing" without showing the output (R5).
meta-reviewing-ai-reviewing
AI integration review patterns. Use when reviewing model API calls, prompt construction, LLM output handling, RAG pipelines, and tool-calling code. Covers prompt-injection call-chain tracing, output validation, token budgets, retry/timeout handling, streaming, and key/PII exposure.
meta-reviewing-api-reviewing
Backend code review patterns. Use when reviewing API routes, database operations, auth middleware, and server utilities. Covers injection, boundary validation, authorization coverage, secret/PII exposure, error leakage, and query patterns.
meta-reviewing-infra-reviewing
Infrastructure code review patterns. Use when reviewing CI/CD workflows, Dockerfiles, deployment configs, and IaC. Covers supply-chain pinning, secret exposure, container hygiene, least-privilege permissions, and deployment safety.