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 yusenthebot/stanford-ai-agent-skill --skill skillgit clone --depth 1 https://github.com/yusenthebot/stanford-ai-agent-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/yusenthebot/stanford-ai-agent-skill/skill)<a href="https://agentmods.dev/skills/yusenthebot/stanford-ai-agent-skill/skill"><img src="https://agentmods.dev/badge/skills/yusenthebot/stanford-ai-agent-skill/skill/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/yusenthebot/stanford-ai-agent-skill/skill"><img src="https://agentmods.dev/badge/skills/yusenthebot/stanford-ai-agent-skill/skill.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 | $0.00134 | $0.01719 |
| Opus 5 | $0.00067 | $0.00860 |
| Sonnet 5 | $0.00027 | $0.00344 |
| Haiku 4.5 | $0.00013 | $0.00172 |
Grade A, and why
stanford-ai-agent-skill 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 10d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stanford AI Agent Skill
把 Stanford CS329A《Self-Improving AI Agents》的稳定方法，作为一个可选择性加载的双语合集使用。这个入口负责判断当前问题属于哪种决策，再读取对应 reference；不要把 7 个模块一次性全部塞入上下文。
Use the stable methods from Stanford CS329A, Self-Improving AI Agents, as one selectively loaded bilingual collection. Identify the decision, then read the matching reference. Never load all seven modules by default.
语言路由 / Language routing
- 用户主要用中文提问时，读取
references/下的中文模块并用中文回答。 - When the user asks mainly in English, read the matching file under
references/en/and answer in English. - If the prompt is mixed, follow the explicitly requested output language. Otherwise use the language of the decision question.
- 术语、代码、命令、文件名、指标名和课程英文引文保持原样，不要为了翻译改写技术标识。
使用流程
- 用一句话写出当前要做的决策，不是宽泛主题。
- 按路由表选择一个主模块；只有决策确实跨界时才加载第二个模块。
- 读取对应 reference，并严格执行其中的 E（Execution）与 B（Boundary）。
- 输出决策、证据、风险、判停条件和下一步；引用课程依据时附官方视频与时间戳。
- 涉及部署权限、训练数据回写或不可逆外部动作时，不因 benchmark 提升自动放行。
English equivalent: state the decision in one sentence; choose one primary module and at most one secondary module when genuinely needed; execute its E steps and respect its B boundaries; return the decision, evidence, risks, stop conditions, and next action; never use a benchmark gain alone to authorize deployment, training-data writeback, or irreversible external action.
路由表
| 当前决策 | 加载 reference |
|---|---|
| 这是不是一个 agent？最小闭环缺了什么？ | 01-agent-minimum-loop.md |
| 推理时预算应该花在采样、修订、检索还是验证？ | 02-verification-constrained-inference-budget.md |
| reward、verifier 或 LLM-as-judge 是否可信？ | 03-verification-signal-and-judge-audit.md |
| 多步任务如何处理依赖、并行、回滚和副作用？ | 04-dependency-reversibility-execution-control.md |
| 生成轨迹是否应该进入 SFT/RL？ | 05-verified-trajectory-training-gate.md |
| 缺失证据如何驱动检索、抽取、综合与引用？ | 06-evidence-gap-research.md |
| agent 是否达到某一部署范围或权限级别？ | 07-agent-production-readiness-eval.md |
What ships with it
19 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.
- INDEX.en.md 2.2 KB
- INDEX.md 2.2 KB
- references/01-agent-minimum-loop.md 8.4 KB
- references/02-verification-constrained-inference-budget.md 9.0 KB
- references/03-verification-signal-and-judge-audit.md 7.0 KB
- references/04-dependency-reversibility-execution-control.md 7.0 KB
- references/05-verified-trajectory-training-gate.md 7.7 KB
- references/06-evidence-gap-research.md 12 KB
- references/07-agent-production-readiness-eval.md 13 KB
- references/en/01-agent-minimum-loop.md 4.7 KB
- references/en/02-verification-constrained-inference-budget.md 4.7 KB
- references/en/03-verification-signal-and-judge-audit.md 4.2 KB
- references/en/04-dependency-reversibility-execution-control.md 4.6 KB
- references/en/05-verified-trajectory-training-gate.md 4.9 KB
- references/en/06-evidence-gap-research.md 4.8 KB
- references/en/07-agent-production-readiness-eval.md 5.2 KB
- tests/test-prompts.json 9.4 KB
- tests/test-results.en.md 2.1 KB
- tests/test-results.md 3.0 KB
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.
- 10d ago First seen · 90 lines · 134 tokens per session scan A 9ddf976901f4
stanford-ai-agent-skill is a skill published in the GitHub repository yusenthebot/stanford-ai-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 134 tokens to every session and 1,719 once invoked, about $0.0007 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.
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