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Academic Experiments

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joshua-zyy
academic-experiments

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.

Overview

Publisherjoshua-zyy
Repositoryacademic-paper-writer
Skill nameacademic-experiments
Stars
115
Forks
1
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by joshua-zyy on GitHub. Read the source before you install it.

Installation

Install the Academic Experiments AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/joshua-zyy/academic-paper-writer/tree/main/skills/academic-experiments
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/joshua-zyy/academic-paper-writer.git /tmp/academic-paper-writer
mkdir -p .claude/skills
cp -r /tmp/academic-paper-writer/skills/academic-experiments .claude/skills/academic-experiments
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Experiments in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Academic Experiments on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Academic Experiments is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Academic Experiments AI skill do?

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, repro...

Why use Academic Experiments on TypingMind?

Because you install it once and use it with any model. Academic Experiments is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Academic Experiments in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/joshua-zyy/academic-paper-writer/tree/main/skills/academic-experiments. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Academic Experiments?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Academic Experiments?

As many as you like. As long as a model supports skills, you can use Academic Experiments with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Academic Experiments AI skill free?

Yes. It is published on GitHub by joshua-zyy under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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