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 PANGKAIFENG/ai-product-manager-skills --skill skill-reviewergit clone --depth 1 https://github.com/PANGKAIFENG/ai-product-manager-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/pangkaifeng/ai-product-manager-skills/skill-reviewer)<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/skill-reviewer"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/skill-reviewer/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/pangkaifeng/ai-product-manager-skills/skill-reviewer"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/skill-reviewer.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 | $0.00116 | $0.01953 |
| Opus 5 | $0.00058 | $0.00977 |
| Sonnet 5 | $0.00023 | $0.00391 |
| Haiku 4.5 | $0.00012 | $0.00195 |
Grade A, and why
skill-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 12d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill 评审器（skill-reviewer）
中文速查
- 中文名：Skill 评审器 / Skill 审计
- 英文稳定名：
skill-reviewer - 你可以这样叫我：
帮我 review 这个 Skill、检查这个 SKILL.md、这个能力适不适合做成 Skill、优化 Skill 触发描述 - 适合：评审 Skill 质量、触发边界、输入输出、工具边界、资源组织、评估设计和团队复用风险
- 不适合：直接创建新 Skill；创建或导入新 Skill 优先用
team-skill-creator
Overview
使用这个 Skill 做团队级 Skill 评审。把 Skill 当作可复用能力单元，而不是一段长提示词。从目标倒推：触发契约、输入输出契约、工作流门槛、工具边界、随包资源、context budget、评估闭环和治理方式都必须服务于目标结果。
Workflow
- 定位待评审对象。优先使用包含
SKILL.md的 Skill 目录；如果用户只提供文本，就评审文本并说明目录/资源检查不可用。 - 在给最终建议前建立评审上下文：目标用户、业务/领域目标、预期触发语、运行环境、所需工具、输入输出、失败成本、复用频率和当前痛点。
- 如果缺少关键上下文，最多问 5 个具体问题。不要因可选上下文阻塞；如果用户要求快速评审，就在明确假设下继续。
- 当本地 Skill 目录或
SKILL.md路径可用时，运行确定性检查：
python3 <this-skill>/scripts/check_skill.py /path/to/skill-or-SKILL.md
- 边检查边建立 Evidence Summary：读了哪些文件、跑了哪些命令、确定性发现、直接观察、推断判断和假设。
- 涉及团队交付、评分、非平凡优化、context budget 或跨生态元数据时，读取
references/team-skill-rubric.md。 - 涉及行业级评审、跨 harness 资产或最佳实践问题时，读取
references/industry-skill-patterns.md。 - 只有在需要验证
SKILL.md声明时，才深入检查references/、scripts/、assets/、eval 文件或示例 prompt。 - 输出按优先级排序的问题和具体修复方案。用户要求优化时，优先给可执行 patch、重写段落、脚本建议或 eval 清单，而不是只评论。
Context Intake
Confirm the following before final review, or write them as assumptions:
- Purpose: what repeatable task the Skill supports, and why it should be a Skill rather than a prompt, tool, workflow, plugin, or app.
- Users: who invokes it, how they naturally ask, and whether it is personal, team, or production-grade.
- Trigger examples: 3-5 requests that should trigger it and 2-3 that should not.
- Inputs and outputs: required user input, discoverable environment context, final deliverable, output format, and Definition of Done.
- Execution boundary: tools, scripts, APIs, permissions, side effects, sandbox constraints, confirmation requirements, and fallback behavior.
- Evaluation: success criteria, smoke prompts, non-trigger prompts, deterministic checks, capability evals, regression traces, grader type, and release requirements.
Only ask questions that materially affect the review. If the artifact already answers a question, do not ask it again.
What ships with it
5 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.
- 12d ago First seen · 140 lines · 116 tokens per session scan A 301fcda3b6a1
skill-reviewer is a skill published in the GitHub repository PANGKAIFENG/ai-product-manager-skills (11 stars, last pushed 13d ago), licensed MIT. It adds 116 tokens to every session and 1,953 once invoked, about $0.0006 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 2026-08-31.
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