Agent skill

Academic Experiments

by joshua-zyy in joshua-zyy/academic-paper-writer

Audit, run, or verify experimental evidence for CS/AI/ML papers.

MITAuto-check passedResearch & Science

Install Academic Experiments

skills CLI
$ npx skills add joshua-zyy/academic-paper-writer --skill academic-experiments -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install joshua-zyy/academic-paper-writer academic-experiments --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/joshua-zyy/academic-paper-writer.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/academic-experiments .claude/skills/academic-experiments && rm -rf skills-src

Use ~/.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/

Facts

Skill name
academic-experiments
GitHub stars
115
Token cost
~754 tokens
SKILL.md length
250 words
Files
11 (incl. scripts, references)
Skills in repo
8
Repo updated
First seen
Licence
MIT

At a glance

Audit, run, or verify experimental evidence for CS/AI/ML papers.

  • Works in 6 steps: Read manifest.yaml. It declares… → Read every file listed under… → Apply the loaded material as constraints → …
  • : checking if experiment results are reproducible
  • SKILL.md covers Router Protocol, Modes, Agent Dispatch and Independent Use
  • Runs Python scripts from its folder

What it does

Academic Experiments is an agent skill from joshua-zyy/academic-paper-writer. Audit, run, or verify experimental evidence for CS/AI/ML papers. Produces Evidence Inventory with evidencetype annotations (newlyrun/preexistingartifact/userclaim) and Protocol Risk assessments. Use when: checking if experiment results are reproducible, auditing existing experiment artifacts, running minimal reproducible commands, evaluating checkpoints without full retraining, documenting protocol risks like data leakage or missing baselines. Triggers on: 复核实验, run experiments, 实验结果, experiment evidence, verify…

Its SKILL.md is about 750 tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts and reference files (for example `agents/experiment_agent.md`, `manifest.yaml` and `references/evidence-inventory.md`).

It sits in Research & Science, covering Scientific writing. The repository describes itself as: 面向 CS / AI / ML 领域的证据驱动、分节推进的论文写作 Agent Skill。 The licence is MIT.

When your agent uses it

  • : checking if experiment results are reproducible
  • Auditing existing experiment artifacts
  • Running minimal reproducible commands
  • Evaluating checkpoints without full retraining

Example prompts

  • “/academic-experiments”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Read manifest.yaml. It declares always_load files, axes, and references.on_demand.
  2. Read every file listed under always_load. These are the skill's binding rules — not reference material.
  3. Apply the loaded material as constraints
  4. Detect the mode using the manifest's mode axis: experiment-evidence-pass, evidence-inventory-only, or minimal-reproducible-run. Align…
  5. Echo the selected mode to the user before executing.
  6. Reach for references/ only when the manifest's references.on_demand condition is satisfied.

What it can do on your machine

Read from SKILL.md and the folder at commit 6fceefd. 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

    Ships 1 file in scripts/ (Python), which the agent can run.

    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

Academic Experiments loads about 754 tokens when it runs, and up to ~2k if it reads all its reference files. Until then it costs about 160 tokens; SKILL.md has 250 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~160
When it runs · the whole SKILL.md, loaded when a task matches
~754
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from joshua-zyy/academic-paper-writer at commit 6fceefd, republished under its MIT licence (© joshua-zyy). 250 words, ~754 tokens.

Download SKILL.mdSave it as .claude/skills/academic-experiments/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
academic-experiments
description
Audit, run, or verify experimental evidence for CS/AI/ML papers. Produces Evidence Inventory with evidence_type annotations (newly_run/preexisting_artifact/user_claim) and Protocol Risk assessments. Use when: checking if experiment results are reproducible, auditing existing experiment artifacts, running minimal reproducible commands, evaluating checkpoints without full retraining, documenting protocol risks like data leakage or missing baselines. Triggers on: 复核实验, run experiments, 实验结果, experiment evidence, verify results, 实验验证, evidence inventory, protocol risk, 跑实验, check results, reproduce experiments, 实验审计.

Academic Experiments

将此 skill 视为"实验取证代理",目标是建立最短且可信的证据链,而不是尽量多跑实验。

Router Protocol

  1. Read manifest.yaml. It declares always_load files, axes, and references.on_demand.
  2. Read every file listed under always_load. These are the skill's binding rules — not reference material.
  3. Apply the loaded material as constraints:
    • stance.md defines non-negotiable rules, evidence type semantics, failure degradation, and scope.
    • red-lines.md defines absolute prohibitions. Do not negotiate these.
    • output-contract.md defines deliverables per mode and claim-readiness classification.
    • anti-patterns.md defines known failure modes and their correct alternatives.
  4. Detect the mode using the manifest's mode axis: experiment-evidence-pass, evidence-inventory-only, or minimal-reproducible-run. Align evidence type semantics to ../shared/core/evidence-policy.md.
  5. Echo the selected mode to the user before executing.
  6. Reach for references/ only when the manifest's references.on_demand condition is satisfied.

Modes

ModeUse when
experiment-evidence-passFull audit: inventory + run + record + risk analysis
evidence-inventory-onlyInventory existing artifacts only, no execution
minimal-reproducible-runExecute minimal reproducible command (e.g. eval existing checkpoint)

Agent Dispatch

agents/experiment_agent.md is dispatched by academic-paper-writer orchestrator at Step 4. The agent may run experiments but must not modify project source code or data files, nor write paper prose independently.

Independent Use

InputModePriorityBehavior
repo_path + no run modeexperiment-evidence-pass2 (path trigger)Full audit: inventory → env → minimal run → risk
repo_path + "inspect only"evidence-inventory-only1 (explicit)Inventory only, no commands
repo_path + specific commandminimal-reproducible-run1 (explicit)Verify env → execute → record
No repo_path—3 (no input)Ask path, or auto-detect entry files
ScenarioRecommended
Just auditing/reproducing evidenceThis skill (standalone)
Writing results into paper proseacademic-paper-writer orchestrator
Draft results need verificationThis skill → academic-reviser

© joshua-zyy, 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 10 other files (scripts, references) in skills/academic-experiments of joshua-zyy/academic-paper-writer.

  • SKILL.md
  • agents/experiment_agent.md
  • manifest.yaml
  • references/evidence-inventory.md
  • references/protocol-risks.md
  • references/run-strategy.md
  • scripts/evidence_scanner.py
  • static/core/anti-patterns.md
  • static/core/output-contract.md
  • static/core/red-lines.md
  • static/core/stance.md

Open the folder on GitHubat commit 6fceefd

Compare with similar skills

Academic Experiments 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.

Academic Experiments compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Academic Experiments this skilljoshua-zyy/academic-paper-writer115—~754Automated safety check: PassMIT
Nature-Style Scientific FiguresYuan1z0825/nature-skills47k—~2.9kAutomated safety check: PassApache-2.0
Citation Verification GuideGalaxy-Dawn/claude-scholar5.7k2 repos~1.9kAutomated safety check: PassMIT
Citation ManagementK-Dense-AI/claude-scientific-writer2.4k2 repos~3.9kAutomated safety check: NotesMIT
Academic Paper Composerlishix520/academic-paper-skills1.4k2 repos~6.3kAutomated safety check: PassMIT
Academic Paper Writing PipelineImbad0202/academic-research-skills51k—~16kAutomated safety check: PassCustom licence

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Questions about Academic Experiments

What does Academic Experiments do?

Audit, run, or verify experimental evidence for CS/AI/ML papers. Academic Experiments is an agent skill from joshua-zyy/academic-paper-writer. Audit, run, or verify experimental evidence for CS/AI/ML papers.

When should I use Academic Experiments?

Academic Experiments fits situations like: : checking if experiment results are reproducible; auditing existing experiment artifacts; running minimal reproducible commands; evaluating checkpoints without full retraining.

How do I install Academic Experiments in Claude Code?

Run `npx skills add joshua-zyy/academic-paper-writer --skill academic-experiments -a claude-code`. Or copy the skill folder (skills/academic-experiments in joshua-zyy/academic-paper-writer) into .claude/skills/academic-experiments in your project. Claude Code loads it when a task matches its description.

How do I install Academic Experiments in Codex?

Run `npx skills add joshua-zyy/academic-paper-writer --skill academic-experiments -a codex`. Or copy the skill folder (skills/academic-experiments in joshua-zyy/academic-paper-writer) into .agents/skills/academic-experiments in your project. Codex loads it when a task matches its description.

Can I use Academic Experiments 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 joshua-zyy/academic-paper-writer --skill academic-experiments -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/academic-experiments, .gemini/skills/academic-experiments, .github/skills/academic-experiments and .opencode/skills/academic-experiments in your project.

What does Academic Experiments need to run?

Going by SKILL.md and its folder, Academic Experiments needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Academic Experiments 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 Academic Experiments 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Academic Experiments use?

Academic Experiments 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 Academic Experiments use?

About 754 tokens (SKILL.md is roughly 3k 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 1.2k tokens, read only when the agent opens those files.

What are the alternatives to Academic Experiments?

Skills that share tags, products or a category with Academic Experiments: Nature-Style Scientific Figures (Yuan1z0825/nature-skills, 47k stars), Citation Verification Guide (Galaxy-Dawn/claude-scholar, 5.7k stars), Citation Management (K-Dense-AI/claude-scientific-writer, 2.4k stars) and Academic Paper Composer (lishix520/academic-paper-skills, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Academic Experiments?

joshua-zyy (a GitHub user) maintains it in joshua-zyy/academic-paper-writer, which has 115 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on October 5, 2026.

Source: joshua-zyy/academic-paper-writer on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.