Skill: experiment-advisor
When to use
Use this skill when:
- •The user message starts with
<advise>, or - •The user clearly asks "What should we try next?" in an experiment context.
Inputs
- •User goal (including constraints like run budget, hardware).
- •Optional references to files (configs, reports, logs) mentioned in the goal.
Behavior
- •
Understand the goal
- •Parse which models, datasets, tasks, and constraints are involved.
- •If unclear, ask a concise clarifying question.
- •
Gather context
- •Scan
.codex/skills/for relevant result skills:- •Matching models, datasets, or task types.
- •Read relevant
training_reports/*.md:- •Start with the most recent ones that mention these models/datasets.
- •Skim the last few days of
references/experiment-log.md. - •If the goal mentions an error, look up matching patterns in
references/troubleshooting.md.
- •Scan
- •
Propose a plan
- •Design 2–5 experiments, each with:
- •Input data selection (possibly mixtures).
- •Model / architecture details (at a high level).
- •Key hyperparameters (LR, batch size, epochs, etc.).
- •Any relevant variation (e.g. different mixture ratios).
- •Respect run/hardware constraints.
- •When applicable, reuse defaults from existing skills instead of inventing new ones.
- •Design 2–5 experiments, each with:
- •
Output format
- •
Start with a short natural-language summary.
- •
Then provide a markdown table with the experiments:
id description key_differences notes - •
Optionally propose file paths for configs or reports to create.
- •
If errors were mentioned, explicitly call out how this plan avoids known failure patterns from
troubleshooting.md.
- •
- •
Logging
- •When useful, append a short entry to
references/experiment-log.mdsummarizing the newly proposed plan (only if the user agrees). - •Include a short "General description" line in the entry for non-technical context.
- •When useful, append a short entry to