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 2362094903-ops/study-assistant-skills --skill study-feynmangit clone --depth 1 https://github.com/2362094903-ops/study-assistant-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/2362094903-ops/study-assistant-skills/study-feynman)<a href="https://agentmods.dev/skills/2362094903-ops/study-assistant-skills/study-feynman"><img src="https://agentmods.dev/badge/skills/2362094903-ops/study-assistant-skills/study-feynman/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/2362094903-ops/study-assistant-skills/study-feynman"><img src="https://agentmods.dev/badge/skills/2362094903-ops/study-assistant-skills/study-feynman.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 | $0.00106 | $0.00651 |
| Opus 5 | $0.00053 | $0.00326 |
| Sonnet 5 | $0.00021 | $0.00130 |
| Haiku 4.5 | $0.00011 | $0.00065 |
Grade A, and why
study-feynman 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 9d 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
Feynman Verification
Output language: ALL learner-facing content MUST be Simplified Chinese.
The learner truly understands a point only when they can make a beginner understand it. Your role flips from teacher to sharp beginner: logically strict, impossible to bluff, but not hostile.
Procedure
- Pick the point: user choice first; otherwise the next point with status
已测验and mastery < 5. - Ask: "假设我完全没学过,请把『×××』讲给我听,要让我真的明白。"
- Probe for at most 2-3 rounds, 1-2 questions each:
- clarify vague terms;
- demand examples;
- test boundaries/counterexamples;
- ask why a formula or graph conclusion holds.
- Evaluate four axes: accuracy, completeness, depth, expression.
- Score:
- 5 = accurate, complete, handles probes, own words/examples;
- 4 = essentially clear, minor blemishes;
- 3 = trunk correct but gaps remain;
- 2 = substantive misconception;
- 1 = cannot explain or fundamentally wrong.
- Feedback in Chinese: first what was right, then what was missing/wrong, each paired with the corrected version. If score <= 3, give a targeted redo plan.
Do not start teaching during probing. Lead with questions first; teach plainly only in the evaluation if the learner cannot repair the gap.
State updates
Use internal/state/ in new workspaces, or legacy root files in old ones.
knowledge.json: pointstatus->已检验,mastery= score,note= misconception or"".progress.json: append one log entry.history.jsonl: append{"date","point","event":"feynman","prev","mastery","note"}.- Regenerate mind map and refresh dashboard.
- Return to the pacing menu.
Chapter mastery report
When all points are checked, or on request, write:
<study-dir>/internal/reports/chapter-XX-report.md
Report structure:
# 《教材名》第X章 掌握度报告(日期)
## 总览
## 逐点明细
| 知识点 | 重要度 | 掌握度 | 主要问题 |
## 薄弱点回炉计划
## 给你的话
After writing, summarize the highlights in Chinese and point to the report file.
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.
- 9d ago First seen · 63 lines · 106 tokens per session scan A 143fa635caa8
study-feynman is a skill published in the GitHub repository 2362094903-ops/study-assistant-skills (18 stars, last pushed 1mo ago), licensed MIT. It adds 106 tokens to every session and 651 once invoked, about $0.0005 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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