Shinka Inspect Skill

When to Use

• 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

• You still need to scaffold a task (

  shinka-setup

  )
• You need to run evolution batches (

  shinka-run

  )

What it does

• Uses

  shinka.utils.load_programs_to_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:

  <results_dir>/shinka_inspect_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).