How do I install the Academic Experiments skill?

Run npx skillmds@latest add joshua-zyy/academic-experiments in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Academic Experiments safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Academic Experiments?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Academic Experiments free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Academic Experiments?

joshua-zyy (@joshua-zyy) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Academic Experiments by joshua-zyy — Agent Skill

Name: Academic Experiments
Author: 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.

joshua-zyy Updated Sep 17, 2026

File contents

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

Read manifest.yaml. It declares always_load files, axes, and references.on_demand.
Read every file listed under always_load. These are the skill's binding rules — not reference material.
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.
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.
Echo the selected mode to the user before executing.
Reach for references/ only when the manifest's references.on_demand condition is satisfied.

Modes

Mode	Use when
`experiment-evidence-pass`	Full audit: inventory + run + record + risk analysis
`evidence-inventory-only`	Inventory existing artifacts only, no execution
`minimal-reproducible-run`	Execute 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

Input	Mode	Priority	Behavior
`repo_path` + no run mode	experiment-evidence-pass	2 (path trigger)	Full audit: inventory → env → minimal run → risk
`repo_path` + "inspect only"	evidence-inventory-only	1 (explicit)	Inventory only, no commands
`repo_path` + specific command	minimal-reproducible-run	1 (explicit)	Verify env → execute → record
No `repo_path`	—	3 (no input)	Ask path, or auto-detect entry files

Scenario	Recommended
Just auditing/reproducing evidence	This skill (standalone)
Writing results into paper prose	academic-paper-writer orchestrator
Draft results need verification	This skill → academic-reviser

---name: academic-experimentsdescription: 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 Protocol1. 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| Mode | Use when ||---|---|| `experiment-evidence-pass` | Full audit: inventory + run + record + risk analysis || `evidence-inventory-only` | Inventory existing artifacts only, no execution || `minimal-reproducible-run` | Execute 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| Input | Mode | Priority | Behavior ||---|---|---|---|| `repo_path` + no run mode | experiment-evidence-pass | 2 (path trigger) | Full audit: inventory → env → minimal run → risk || `repo_path` + "inspect only" | evidence-inventory-only | 1 (explicit) | Inventory only, no commands || `repo_path` + specific command | minimal-reproducible-run | 1 (explicit) | Verify env → execute → record || No `repo_path` | — | 3 (no input) | Ask path, or auto-detect entry files || Scenario | Recommended ||---|---|| Just auditing/reproducing evidence | This skill (standalone) || Writing results into paper prose | academic-paper-writer orchestrator || Draft results need verification | This skill → academic-reviser |

Frequently asked questions

Academic Experiments

File contents

Academic Experiments

Router Protocol

Modes

Agent Dispatch

Independent Use

Related skills

Frequently asked questions

How do I install the Academic Experiments skill?

What does the Academic Experiments skill do?

Is Academic Experiments safe to use?

Which AI agents work with Academic Experiments?

Is Academic Experiments free to use?

Who published Academic Experiments?