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 agentmods add skills/arctan303/creator.skill/reviewernpx skills add arctan303/creator.skill --skill reviewergit clone --depth 1 https://github.com/arctan303/creator.skillWrote 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/arctan303/creator.skill/reviewer)<a href="https://agentmods.dev/skills/arctan303/creator.skill/reviewer"><img src="https://agentmods.dev/badge/skills/arctan303/creator.skill/reviewer.svg" alt="Measured on agentmods" 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 | $0.00080 | $0.00674 |
| Opus 5 | $0.00040 | $0.00337 |
| Sonnet 5 | $0.00016 | $0.00135 |
| Haiku 4.5 | $0.00008 | $0.00067 |
Grade A, and why
reviewer 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 6d 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.
What it actually says
独立审查员技能 (reviewer)
Purpose
用未参与实现的独立视角发现契约偏离、回归、证据缺口与安全风险,并给出可追溯、会收敛的结论。
Trigger
- R2 变更或产品 Phase 完成后必须审查。
- R1 存在显著残余风险,或用户明确要求聚焦审查。
- R0 通常不触发。
模型容量、网络或宿主失败是执行中断,不是审查结论。
Required context
- 路线、目标、范围、非目标和风险依据。
- 产品文档/Phase,或维护任务契约。
- 当前 diff、变更文件、验证命令、结果、失败/跳过项和残余风险。
- 审查指纹:
契约版本 + diff 标识 + 证据标识。 - 前一轮结论与修复摘要(如果是聚焦复审)。
Workflow
- 先校验 fresh 独立性、路线和风险级别。
- 相同审查指纹已有结论时复用,不创建重复 reviewer。
- Stage 1 核验契约、范围和验收证据;有阻塞问题时直接不通过。
- Stage 2 检查边界、回归、测试质量、安全、隐私、数据和发布风险。
- 一轮内列全可发现问题,实施者批量修复。
- 产生新 diff 后最多进行一次聚焦复审;同一阻塞问题连续两轮存在时停止循环并报告阻塞。
Output
- 审查指纹、路线与风险判定。
- 结论:通过 / 有条件通过 / 不通过 / 无法审查 / 中断。
- 按严重度排列的问题,每项含证据、影响和建议。
- 验收覆盖、残余风险和是否允许聚焦复审。
Stop or escalate
- 缺少 fresh 独立性、契约、diff 或验证证据:输出“无法审查”。
- 模型容量、网络、工具或宿主中断:输出“中断”和恢复检查点,不把它算作审查轮次。
- 同一 diff 已审查:复用结论,不新建 reviewer。
- 新 diff 的一次聚焦复审仍有相同阻塞问题:停止继续复审,交还实施者或用户决策。
- 发现新能力被标为维护或风险被低估:升级路线/风险后再审。
References
执行前读取 stage-contract.md。
What ships with it
2 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.
- 6d ago First seen · 55 lines · 80 tokens per session scan A eba4bda29520
reviewer is a skill published in the GitHub repository arctan303/creator.skill (20 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 674 once invoked, about $0.0004 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
skill-forge
Generate a new skill from a simple prompt, then run an enhancer pass to tighten trigger rules, workflow, and output quality.
edgeone skill scanner
Scan any agent skill for security risks before you install or use it. Powered by Tencent Zhuque Lab A.I.G (AI-Infra-Guard). 100% local static analysis — no file contents or credentials leave your device. Compatible with CodeBuddy, Cursor, Windsurf, Claude Code, OpenClaw and more. Triggers on: 这个 skill 安全吗, skill 安全扫描…
avoid-ai-writing
Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detect-only mode, an edit-in-place mode for files, an optional voice…
trulens-evaluation-workflow
Systematically evaluate your LLM application with TruLens.
remote-compute-modal
Run GPU jobs on the user's own Modal account via host.compute.create('byoc:modal', ...). Covers the create→submit→waitfornotification flow, the computeprovider kernel for env setup, image/volume resolution, and the two approval cards. Load once you've decided to dispatch to Modal.
smart-data-analysis
数据分析员工(Data Analyst Agent)的唯一总入口:凡与数据资产、取数、指标、表/视图、 治理职责、知识网络、统计或分析相关的问题,必须先经本 skill 做编排与路由,再进入找表或问数等子流程。 负责 kn 分域、上下文注入(accountId / date)、多候选 KN 时的 LLM 决策、 问数分支的 SQL 生成;与 smart-search-tables / smart-ask-data / ontology-core 的交接。 当用户提出任何数据类自然语言任务、或需在多条业务 KN 间切换时使用; 所有 ontology CLI 执行均委托 ontology-core 完成,本 skill 不直接执行…