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

1. Understand the goal

   • Parse which models, datasets, tasks, and constraints are involved.
   • If unclear, ask a concise clarifying question.

2. 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

     .

3. 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.

4. 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

     .

5. Logging

   • When useful, append a short entry to

     references/experiment-log.md

     summarizing the newly proposed plan (only if the user agrees).
   • Include a short "General description" line in the entry for non-technical context.