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 chtc66/academic-skills --skill experiment-log-summarizergit clone --depth 1 https://github.com/chtc66/academic-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/chtc66/academic-skills/experiment-log-summarizer)<a href="https://agentmods.dev/skills/chtc66/academic-skills/experiment-log-summarizer"><img src="https://agentmods.dev/badge/skills/chtc66/academic-skills/experiment-log-summarizer/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/chtc66/academic-skills/experiment-log-summarizer"><img src="https://agentmods.dev/badge/skills/chtc66/academic-skills/experiment-log-summarizer.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 | $0.00058 | $0.00501 |
| Opus 5 | $0.00029 | $0.00251 |
| Sonnet 5 | $0.00012 | $0.00100 |
| Haiku 4.5 | $0.00006 | $0.00050 |
Grade A, and why
experiment-log-summarizer 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.
What it actually says
Experiment Log Summarizer
用这个 skill 整理实验日志、参数改动、训练结果和失败记录，输出中文实验总结。重点是区分证据与猜测，并把分散实验整理成可复盘的研究记录。
工作流
- 先把输入按实验轮次、配置、结果和备注拆开。
- 参考
references/experiment_template.md汇总主要结论。 - 在需要失败归因或误差分析时，参考
references/error_analysis_template.md。 - 输出完整实验总结，并附周报版摘要。
输入处理规则
- 接收训练日志、eval 结果、参数表、用户备注和多轮实验对比。
- 如果日志不完整，优先整理可确认事实，再列缺口。
- 如果同一实验有多次重复运行，优先总结稳定趋势，不要被单次波动误导。
输出规则
- 默认输出：
- 本次实验目标
- 做了哪些改动
- 结果变化
- 可能原因
- 当前最佳配置
- 失败实验总结
- 下一步建议
- 周报版摘要
- “结果变化”只写有数字、日志或明确记录支撑的内容。
- “可能原因”必须明确标为推测，不要伪装成已验证结论。
证据与表述约束
- 明确区分：
证据：日志、指标、配置表、用户明确说明推测：对涨跌原因的解释、潜在 bug 假设、过拟合猜测
- 不要把失败实验简化成“无效”，要指出失败是因为假设错误、实现问题、数据问题还是评测问题。
何时读引用文件
- 始终读取
references/experiment_template.md。 - 在需要拆失败原因、错误模式或后续验证动作时读取
references/error_analysis_template.md。
What ships with it
3 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.
- 10d ago First seen · 48 lines · 58 tokens per session scan A 985cf86125b8
experiment-log-summarizer is a skill published in the GitHub repository chtc66/academic-skills (348 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 501 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 2026-08-30.
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