Experiment idea planner
Skill StaryMoon/ai-researcher-skills/skills/experiment-idea-planner
Practical skill pack for AI research agents: paper briefing, experiment planning, rebuttal drafting, radar maintenance, and GitHub upkeep.
npx -y skills add StaryMoon/ai-researcher-skills --skill experiment-idea-plannerAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.5 KB, as published. Nobody here has run it
Experiment Idea Planner
Purpose
Turn a paper idea, codebase, or research hunch into a ranked experiment plan with concrete gates. The plan should help a researcher avoid wasting GPU time on weak branches.
When To Use
Use this skill when the user asks what to try next, how to improve a model, how to reproduce a paper, how to compare branches, or how to design a short research sprint.
Inputs
- Current hypothesis or paper idea.
- Local code path and current baseline.
- Available compute budget.
- Metric, dataset, or validation split.
- Prior failed attempts if available.
Workflow
- Restate the target metric and baseline. If they are absent, ask for them or define a minimal proxy.
- Convert broad ideas into small experiment candidates.
- Rank candidates by expected value, cost, and interpretability.
- For each candidate, define implementation scope, config knobs, required files, and expected failure mode.
- Add a gate: minimal metric improvement, qualitative signal, runtime ceiling, or ablation sanity check.
- Add a stop rule for weak branches.
- Produce the first command or file-edit route when the codebase is available.
Output
BaselineRanked Experiment QueueQuick GatesStop RulesFirst Implementation StepWhat Would Be Publishable
Quality Gates
- Do not propose expensive full runs before a quick gate.
- Keep comparisons on the same validation slice.
- Avoid adding complexity without a measurable reason.
- If the baseline is unverified, make baseline verification experiment 0.