Shinka Inspect Skill

Extract the strongest programs from a Shinka run and package them into a context file that coding agents can load directly.

When to Use

Use this skill when:

• A run already produced a results directory and SQLite database
• You want to inspect top-performing programs before launching the next batch
• You want a compact context artifact instead of manually browsing the DB

Do not use this skill when:

• You still need to scaffold a task (shinka-setup)
• You need to run evolution batches (shinka-run)

What it does

• Uses shinka.utils.loadprogramsto_df to read program records
• Ranks programs by combined_score
• Selects top-k correct programs (fallback to top-k overall if no correct rows)
• Writes one Markdown bundle with metadata, ranking table, feedback, and code snippets

Workflow

1. Confirm run artifacts exist

ls -la <results_dir>

1. Generate context bundle

python skills/shinka-inspect/scripts/inspect_best_programs.py \
  --results-dir <results_dir> \
  --k 5

1. Optional tuning knobs

python skills/shinka-inspect/scripts/inspect_best_programs.py \
  --results-dir <results_dir> \
  --k 8 \
  --max-code-chars 5000 \
  --min-generation 10 \
  --out <results_dir>/inspect/top_programs.md

1. Load output into agent context

• Default output path: <resultsdir>/shinkainspect_context.md
• Use it as the context artifact for next-step mutation planning

CLI Arguments

• --results-dir: Path to run directory (or direct DB file path)
• --k: Number of programs to include (default 5)
• --out: Output markdown path (default under results dir)
• --max-code-chars: Per-program code truncation cap (default 4000)
• --min-generation: Optional lower bound on generation
• --include-feedback / --no-include-feedback: Include text_feedback blocks

Notes

• Ranking metric is combined_score.
• If no correct rows exist, script falls back to top-score rows and labels fallback in output.
• Script is read-only for run artifacts (writes only the markdown bundle).