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 AgiWish/hermes-skills-zh --skill performance-review-zhgit clone --depth 1 https://github.com/AgiWish/hermes-skills-zhWrote 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/agiwish/hermes-skills-zh/performance-review-zh)<a href="https://agentmods.dev/skills/agiwish/hermes-skills-zh/performance-review-zh"><img src="https://agentmods.dev/badge/skills/agiwish/hermes-skills-zh/performance-review-zh/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/agiwish/hermes-skills-zh/performance-review-zh"><img src="https://agentmods.dev/badge/skills/agiwish/hermes-skills-zh/performance-review-zh.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 | $0.00050 | $0.00677 |
| Opus 5 | $0.00025 | $0.00338 |
| Sonnet 5 | $0.00010 | $0.00135 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
performance-review-zh 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.
What it actually says
绩效复盘 (performance-review-zh)
When to Use
- "帮我写绩效自评"、"季度/年度绩效怎么写"
- "我这季度做了 X，帮我整理成绩效"
- 用户提到「OKR 完成情况」、「绩效季」
/performance-review-zh [工作内容]
Quick Reference
/performance-review-zh [工作成果描述]
可选：
--tone=积极 # 突出亮点（默认）
--tone=客观 # 如实陈述，不夸大
--period=Q3 # 指定考核周期
--format=STAR # 严格 STAR 格式
Procedure
-
收集素材 如用户只提供了流水账，引导补充：
- 「这件事的背景是什么？」（Situation）
- 「你具体做了什么？」（Action）
- 「结果怎么样，有数据吗？」（Result）
-
STAR 结构生成
【[项目/工作名称]】 背景（Situation）： [1-2句话描述工作背景和挑战] 任务（Task）： [我负责的具体目标] 行动（Action）： · [具体做了什么1] · [具体做了什么2] · [解决了什么关键问题] 结果（Result）： · [量化成果，如：完成率 X%、节省 Y 小时] · [定性成果，如：获得好评、推动了某决策] -
整体自评总结（300字以内）
本[季度/年度]，我主要负责[领域]，重点完成了[2-3项]。 [1句话说明最大亮点和价值]。 不足方面，[1句话客观说明待提升项]。 下一阶段，我计划[1-2句话说明改进方向]。
Pitfalls
- 结果部分优先用数字，没有数据就用「显著」「有效」等副词代替，不要捏造数字
- 不足部分要写（只夸自己会显得不真实），但要控制篇幅
- 避免「积极推进」「大力支持」等空洞官话
Verification
- 每个项目是否有 STAR 四要素
- 结果是否有量化或可验证的描述
- 不足部分是否简洁且有改进方向
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 · 82 lines · 50 tokens per session scan A aa5c2b562264
performance-review-zh is a skill published in the GitHub repository AgiWish/hermes-skills-zh (5 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 677 once invoked, about $0.0003 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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