Scikit Learn
zLanqing/codex-claude-academic-skills
Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.
summarize machine learning experiment logs in chinese when the input includes training logs, eval results, hyperparameter changes, user notes, or multiple runs and the user needs a grounded…
Install Experiment Log Summarizer
$ npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install chtc66/academic-skills experiment-log-summarizer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/chtc66/academic-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/experiment-log-summarizer .claude/skills/experiment-log-summarizer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "experiment-log-summarizer" agent skill from https://github.com/chtc66/academic-skills/tree/main/experiment-log-summarizer into .claude/skills/experiment-log-summarizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experiment-log-summarizer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/chtc66/academic-skills/tree/main/experiment-log-summarizerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install chtc66/academic-skills experiment-log-summarizer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/chtc66/academic-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/experiment-log-summarizer .agents/skills/experiment-log-summarizer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "experiment-log-summarizer" agent skill from https://github.com/chtc66/academic-skills/tree/main/experiment-log-summarizer into .agents/skills/experiment-log-summarizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experiment-log-summarizer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install chtc66/academic-skills experiment-log-summarizer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/chtc66/academic-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/experiment-log-summarizer .cursor/skills/experiment-log-summarizer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "experiment-log-summarizer" agent skill from https://github.com/chtc66/academic-skills/tree/main/experiment-log-summarizer into .cursor/skills/experiment-log-summarizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experiment-log-summarizer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/chtc66/academic-skills.git --path experiment-log-summarizer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install chtc66/academic-skills experiment-log-summarizer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/chtc66/academic-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/experiment-log-summarizer .gemini/skills/experiment-log-summarizer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "experiment-log-summarizer" agent skill from https://github.com/chtc66/academic-skills/tree/main/experiment-log-summarizer into .gemini/skills/experiment-log-summarizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experiment-log-summarizer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install chtc66/academic-skills experiment-log-summarizerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/chtc66/academic-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/experiment-log-summarizer .github/skills/experiment-log-summarizer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "experiment-log-summarizer" agent skill from https://github.com/chtc66/academic-skills/tree/main/experiment-log-summarizer into .github/skills/experiment-log-summarizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experiment-log-summarizer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install chtc66/academic-skills experiment-log-summarizer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/chtc66/academic-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/experiment-log-summarizer .opencode/skills/experiment-log-summarizer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "experiment-log-summarizer" agent skill from https://github.com/chtc66/academic-skills/tree/main/experiment-log-summarizer into .opencode/skills/experiment-log-summarizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experiment-log-summarizer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
experiment-log-summarizer- GitHub stars
- 360
- Token cost
- ~281 tokens
- SKILL.md length
- 47 words
- Files
- 4 (incl. references)
- Skills in repo
- 8
- Repo updated
- First seen
- Licence
- MIT
At a glance
summarize machine learning experiment logs in chinese when the input includes training logs, eval results, hyperparameter changes, user notes, or multiple runs and the user needs a grounded…
- Works in 4 steps: 先把输入按实验轮次、配置、结果和备注拆开。 → 参考 references/experiment_template.md… → 在需要失败归因或误差分析时，参考… → …
- Tasks that involve Internal communications
- SKILL.md covers 工作流, 输入处理规则, 输出规则 and 证据与表述约束, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Experiment Log Summarizer is an agent skill from chtc66/academic-skills. summarize machine learning experiment logs in chinese when the input includes training logs, eval results, hyperparameter changes, user notes, or multiple runs and the user needs a grounded experiment summary, error analysis, best configuration recap, or a weekly update ready abstract.
Its SKILL.md is about 280 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `agents/openai.yaml`, `references/error_analysis_template.md` and `references/experiment_template.md`).
It sits in Data & Analytics, covering Internal communications and Machine learning. The repository describes itself as: Academic workflow skills for paper reading, survey writing, experiment summarization, rebuttal drafting, and weekly lab updates. The licence is MIT.
When your agent uses it
- Tasks that involve Internal communications
- Tasks that involve Machine learning
Example prompts
- “/experiment-log-summarizer”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 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。
What it can do on your machine
Read from SKILL.md and the folder at commit 126e235. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Experiment Log Summarizer loads about 281 tokens when it runs, and up to ~653 if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 47 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from chtc66/academic-skills at commit 126e235, republished under its MIT licence (© chtc66). 47 words, ~281 tokens.
Download SKILL.mdSave it as
.claude/skills/experiment-log-summarizer/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- experiment-log-summarizer
- description
- summarize machine learning experiment logs in chinese when the input includes training logs, eval results, hyperparameter changes, user notes, or multiple runs and the user needs a grounded experiment summary, error analysis, best configuration recap, or a weekly update ready abstract.
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。
© chtc66, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in experiment-log-summarizer of chtc66/academic-skills.
- SKILL.md
- agents/openai.yaml
- references/error_analysis_template.md
- references/experiment_template.md
Open the folder on GitHubat commit 126e235
Compare with similar skills
Experiment Log Summarizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Experiment Log Summarizer this skillchtc66/academic-skills | 360 | — | ~281 | Automated safety check: Pass | MIT | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.6k | 17 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 6 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill | 188 | — | ~4k | Automated safety check: Pass | MIT | |
| Retention Analysisliangdabiao/claude-data-analysis-ultra-main | 290 | 1 repos | ~1.3k | Automated safety check: Notes | None | |
| Geomlitalo-goncalves/geoML | 109 | — | ~4.2k | Automated safety check: Pass | GPL-3.0 |
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Related
Categories
Questions about Experiment Log Summarizer
What does Experiment Log Summarizer do?
summarize machine learning experiment logs in chinese when the input includes training logs, eval results, hyperparameter changes, user notes, or multiple runs and the user needs a grounded…. Experiment Log Summarizer is an agent skill from chtc66/academic-skills. summarize machine learning experiment logs in chinese when the input includes training logs, eval results, hyperparameter changes, user notes, or multiple runs and the user needs a grounded experiment summary, error analysis, best configuration recap, or a weekly update ready abstract.
When should I use Experiment Log Summarizer?
Experiment Log Summarizer fits situations like: tasks that involve Internal communications; tasks that involve Machine learning.
How do I install Experiment Log Summarizer in Claude Code?
Run `npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a claude-code`. Or copy the skill folder (experiment-log-summarizer in chtc66/academic-skills) into .claude/skills/experiment-log-summarizer in your project. Claude Code loads it when a task matches its description.
How do I install Experiment Log Summarizer in Codex?
Run `npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a codex`. Or copy the skill folder (experiment-log-summarizer in chtc66/academic-skills) into .agents/skills/experiment-log-summarizer in your project. Codex loads it when a task matches its description.
Can I use Experiment Log Summarizer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add chtc66/academic-skills --skill experiment-log-summarizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/experiment-log-summarizer, .gemini/skills/experiment-log-summarizer, .github/skills/experiment-log-summarizer and .opencode/skills/experiment-log-summarizer in your project.
What does Experiment Log Summarizer need to run?
SKILL.md names no scripts, command-line tools or credentials: Experiment Log Summarizer is instructions for the agent only.
Does Experiment Log Summarizer access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Experiment Log Summarizer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Experiment Log Summarizer use?
Experiment Log Summarizer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Experiment Log Summarizer use?
About 281 tokens (SKILL.md is roughly 1.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 372 tokens, read only when the agent opens those files.
What are the alternatives to Experiment Log Summarizer?
Skills that share tags, products or a category with Experiment Log Summarizer: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars) and Retention Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Experiment Log Summarizer?
chtc66 (a GitHub user) maintains it in chtc66/academic-skills, which has 360 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on April 5, 2026.
Source: chtc66/academic-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.