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.
npx skills add https://github.com/joshua-zyy/academic-paper-writer --skill 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。
manifest.yaml. It declares always_load files, axes, and references.on_demand.always_load. These are the skill's binding rules — not reference material.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.mode axis: experiment-evidence-pass, evidence-inventory-only, or minimal-reproducible-run. Align evidence type semantics to ../shared/core/evidence-policy.md.references/ only when the manifest's references.on_demand condition is satisfied.| 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) |
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.
| 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 |
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Take joshua-zyy/academic-experiments from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.