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 Dong90/oh-my-taiyiforge --skill taiyi-healthgit clone --depth 1 https://github.com/Dong90/oh-my-taiyiforgeWrote 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/dong90/oh-my-taiyiforge/taiyi-health)<a href="https://agentmods.dev/skills/dong90/oh-my-taiyiforge/taiyi-health"><img src="https://agentmods.dev/badge/skills/dong90/oh-my-taiyiforge/taiyi-health/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/dong90/oh-my-taiyiforge/taiyi-health"><img src="https://agentmods.dev/badge/skills/dong90/oh-my-taiyiforge/taiyi-health.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 | $0.00023 | $0.00259 |
| Opus 5.5 | $0.00009 | $0.00104 |
| Sonnet 5.5 | $0.00005 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
taiyi-health 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 1mo 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
taiyi-health — 健康巡检
场景
周期性/里程碑前/接手陌生项目/重构决策前
步骤
1. 生产代码6维抽样(R1-R6)
抽5个最近改动模块按6维打分
2. 冗余巡检
重复代码块(grep>=5行)/未用导出/死代码/未用依赖
3. 测试6维抽样(T1-T6)
抽5个测试文件
4. 综合分
🔴-10/🟡-3/🟢-1。第二次跑须与上次对比
5. 反哺
🔴→health-fix CHANGE | 🟡→CONTEXT技术债 | 🟢→LESSONS | 未用依赖→禁动清单
禁止
- 直接改代码
- 开多个fix CHANGE(所有Critical合并1个)
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.
- 1mo ago First seen · 32 lines · 23 tokens per session scan A 71efabfa534b
taiyi-health is a skill published in the GitHub repository Dong90/oh-my-taiyiforge (859 stars, last pushed 16d ago), licensed MIT. It adds 23 tokens to every session and 259 once invoked, about $0.0001 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
build-recovery
Owns the build wait and build failure. Narrates the generation honestly while it runs, and when it errors, names the failure plainly with a real recovery path instead of a generic "oops". Loads when generation is long or returns an error. The single highest-risk moment for the "it's actually building" belief.
pn-session-retro
Run a blameless retrospective on an agent transcript. Classify mistakes under a 5-code taxonomy, emit a structured report at docs/refs/retros/, and recommend concrete hardening. Use after a session that felt off, before a quarterly audit, or via the /pn-retro command. v1 is manual-only; cross-session pattern counters…
pn-api-probe
Fetch live API, version, and enum facts from the target runtime or stack before planning. Use in Phase 0 of pn-design and pn-build when the stack may have changed since training data.
pn-systematic-debugging
Root cause analysis with triage mode — Phase 0 feedback loop, investigate first, one question max, then isolate, hypothesize, confirm. Outputs TDD fix plan with RED-GREEN cycles; optional GitHub issue via GitHub MCP. Use when debugging a failure or bug.
ml-debug
Use when something is failing in ML/AI work — OOM, NaN, divergence, crashes, bad throughput, wrong outputs, dependency conflicts.
pn-discipline-philosophy
Defines engineering discipline: test-first, root-cause before fix, minimal change, measure-before-optimize, review/second look, evidence over guess. Use when implementing features, debugging, planning, or establishing development practices. Aligns with TDD, systematic debugging, and RCA practices (current).