Academic Experiments
SkillDev toolsAudit, 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Academic Experiments skill
What this skill tells your AI
The instructions your AI receives, as published by joshua-zyy/academic-paper-writer in skills/academic-experiments/SKILL.md and read by ahel’s review.
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 declaresalways_loadfiles,axes, andreferences.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.mddefines non-negotiable rules, evidence type semantics, failure degradation, and scope.red-lines.mddefines absolute prohibitions. Do not negotiate these.output-contract.mddefines deliverables per mode and claim-readiness classification.anti-patterns.mddefines known failure modes and their correct alternatives.
- Detect the mode using the manifest's
modeaxis:experiment-evidence-pass,evidence-inventory-only, orminimal-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'sreferences.on_demandcondition 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 |
Signals
- GitHub stars
- 110
- Forks
- 1
- Last commit
- Jun 2026
Advanced
- Catalog kind
- skill
- Gateway key
academic-experiments- Source
- github.com/joshua-zyy/academic-paper-writer