/shinka-inspect
Load top-performing Shinka programs into agent context using `shinka.utils.load_programs_to_df`, and emit a compact Markdown bundle for iteration planning.
$ npx -y skills add sakanaai/shinkaevolve --skill shinka-inspect --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/shinka-inspect
Context preview
The summary Claude sees to decide when to auto-load this skill.
Load top-performing Shinka programs into agent context using `shinka.utils.load_programs_to_df`, and emit a compact Markdown bundle for iteration planning.
SKILL.md
shinka-inspect.SKILL.mdname: shinka-inspect
description: Load top-performing Shinka programs into agent context using `shinka.utils.load_programs_to_df`, and emit a compact Markdown bundle for iteration planning.
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.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>
2. Generate context bundle
python skills/shinka-inspect/scripts/inspect_best_programs.py \
--results-dir <results_dir> \
--k 5
3. 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
4. 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).
Read more
name: shinka-inspect description: Load top-performing Shinka programs into agent context using `shinka.utils.load_programs_to_df`, and emit a compact Markdown bundle for iteration planning.
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.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>
2. Generate context bundle
python skills/shinka-inspect/scripts/inspect_best_programs.py \ --results-dir <results_dir> \ --k 5
3. 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
4. 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).
ShinkaEvolve: Towards Open-Ended and Sample-Efficient Program Evolution 🧬
Other skills on shinkaevolve.
- /shinka-convert
Convert an existing codebase in the current working directory into a ShinkaEvolve task directory by snapshotting the relevant code, adding evolve blocks, and generating `evaluate.py` plus Shinka runner/config files. Use when the user wants to optimize existing code with Shinka
Open skill - /shinka-run
Run existing ShinkaEvolve tasks with the `shinka_run` CLI from a task directory (`evaluate.py` + `initial.<ext>`). Use when an agent needs to launch async evolution runs quickly with required `--results_dir`, generation count, and strict namespaced keyword overrides.
Open skill - /shinka-setup
Create ShinkaEvolve task scaffolds from a target directory and task description, producing `evaluate.py` and `initial.<ext>` (multi-language). Use when asked to set up new ShinkaEvolve tasks, evaluation harnesses, or baseline programs for ShinkaEvolve.
Open skill

