experiment-suite
Use when the user has a research question and needs a complete experiment package — design document, runnable code, results (measured or simulated with honest provenance), publication-grade figures, structured report. Single-stage, no Python runtime.
Profile →npx skills add ai4s-research/ai4s-skills --skill experiment-suite --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Experiment Suite ## Overview End-to-end experiment package builder. **Single stage, full quality from the start.** The agent (Claude Code / Cursor / Aider / Codex / …) writes everything directly using its own tools (Write, Bash, WebFetch, …). This skill contains procedure + reference playbooks + figure-example scripts — no Python runtime, no LLM SDK. The substantive work is decomposed into reference playbooks under `references/`: | Reference | Topic | |---|---| | `references/00-incremental-execution.md` | how to do this without losing work: batches, persistence, resume — **read first** | | `references/01-design-depth.md` | what a real experiment design contains (motivation → hypothesis → datasets → baselines → metrics → ablations → budget) | | `references/01a-data-contract.md` | runtime dataset binding: source, access route, version, split, and reuse boundary | | `references/02-code-quality.md` | code-skeleton standards — runnable `model.py`, `data.py`, `train.py`, `evaluate.py` | | `references/03-results-protocol.md` | `results.json` schema; `measured` / `simulated` / `illustrative` provenance | | `references/04-publication-figures.md` | publication-grade charts, multi-panel lay
- Overview
- When to Use
- When NOT to Use
- Workflow
- Step 1 — Understand the question and operating mode
- Step 2 — Set up the run directory
- Step 3 — Build the package (REQUIRED — this is the whole job)
- Step 4 — Deliver
- Cross-skill data flow (path convention)
- Important rules
mkdir -p "$RUN/experiment" "$RUN/figures" ln -sfn "$TS" "output/experiment-suite/$SLUG/latest"
What does the experiment-suite skill do?
Use when the user has a research question and needs a complete experiment package — design document, runnable code, results (measured or simulated with honest provenance), publication-grade figures, structured report. Single-stage, no Python runtime.
How do I install it?
Run `npx skills add ai4s-research/ai4s-skills --skill experiment-suite --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ai4s-research/ai4s-skills, a repository with 163 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.