Use when the user wants to evolve an ML model/program through population-based search rather than a single sequential refine loop — a generational evolution where parallel proposers each apply one small SEARCH/REPLACE diff to a parent, scored by a cascade-evaluated training run, and children are kept in a MAP-Elites archive across islands (with migration + checkpointing) so diverse high performers survive. A finite, bounded-parallelism re-creation of AlphaEvolve/OpenEvolve, bent for ML autoresearch. Runs to a fixed compute budget or until interrupted. Not for the sequential single-thread autoresearch loops (one change → measure → keep/revert), and not for verifying a known bug or external claim — this is parallel, diversity-preserving search over a program.
Installation
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Use when the user wants to evolve an ML model/program through population-based search rather than a single sequential refine loop — a generational evolution where parallel proposers each apply one small SEARCH/REPLACE diff to a parent, scored by a cascade-evaluated training run, and children are kept in a MAP-Elites archive across islands (with migration + checkpointing) so diverse high performers survive. A finite, bounded-parallelism re-creation of AlphaEvolve/OpenEvolve, bent for ML autoresearch. Runs to a fixed compute budget or until interrupted. Not for the sequential single-thread autoresearch loops (one change → measure → keep/revert), and not for verifying a known bug or external claim — this is parallel, diversity-preserving search over a program.
A population-based evolutionary loop over a program. The artifact is the editable model code; a
child is one analysis-informed SEARCH/REPLACE diff to a parent, and the feedback signal is a
cascade-evaluated training run (<metric>, smoke→full). Children are placed in a MAP-Elites
archive across islands (complexity × diversity axes), so a child survives by being either better or
more novel, not just better. The discipline this enforces: diversity is preserved, not collapsed —
diverse high performers co-exist instead of one local optimum winning. You are the controller: sample
a parent + inspirations, spawn parallel Mutators to propose and evaluate children, place them, migrate
between islands, checkpoint. Loops to a fixed compute budget or until interrupted.
When to use
Use this for parallel, diversity-preserving search over a model/program where many variants explore at
once and the archive keeps the illuminated frontier. Default to broad island coverage; if quality
stalls, bias selection toward exploiting top elites; if coverage stalls, bias toward empty cells. Not
for the sequential autoresearch loops (one change at a time), and not for fixing a known anomaly.
The cast (both in this folder): roles/Mutator.md produces + cascade-evaluates one child (the
generation step); schemas/result.schema.json is the result a Mutator returns.
Setup
Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it, confirm the
values in one line, and skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is
available, <host> = claude-code) infer a likely value for each binding and present it as the
recommended option; on other hosts (<host> = other) ask each as a quoted plain-text prompt. Then
write loop.run.yaml (format: examples/run.example.yaml) and confirm the values before creating any
other files. <host> also decides execution: Claude Code spawns real Agent Mutators in parallel
(capped at <concurrency>); other hosts degrade to running a generation's children serially (identical
algorithm).
Probe the box first (mandatory — measure, never assume <concurrency>). Record and report:
CPU cores → <cores>: python3 -c "import os; print(os.cpu_count())".
the program being evolved (e.g. model.py, config.yaml); never the harness or data
—
ask explicitly — this is the code that gets mutated; do not default it (multi-select on Claude Code)
<sandbox_root>
where lae/ is created
./sandbox
—
<gate> + <budget>
one full run's size: time/epochs + amount; the FIXED eval budget applied to every program
—
identify the duration key now (e.g. train.epochs) so the controller can override it
<total_budget>
total compute = number of full training runs (or wall-clock minutes); the single cost dial
—
ask
<concurrency>
parallel evaluations C
derived from the probe
CPU-only → max(1, <cores>//4); single GPU/MPS → 1 (ask if more fit <vram>); multi-GPU → <gpu_count> (pin one child/GPU)
num_generations is derived: ceil(<total_budget> / <concurrency>). The cascade is derived
from <budget> (not asked): smoke = ~1 epoch / a small subset, full = <budget>, gate = child's
smoke <metric> ≥ parent's smoke. <budget>/<metric>/eval split are FIXED — never mutation targets
(a child may not "train longer" to look better); changing them means re-running the whole loop.
Advanced (opt-in). Ask one yes/no: "Use defaults for the evolutionary settings, or customize?"
Defaults are faithful to AlphaEvolve/OpenEvolve — use them and ask nothing more. Only on "customize"
ask for each (showing the default as recommended): num_islands (4), num_top (3), num_diverse (2),
num_bins (10), migration_interval (5), diversity_reference_size (10), pop_per_island (40),
seed (42). Axes are fixed: complexity × diversity. See examples/run.example.yaml for the shape.
Print the resolved bindings + the probe + derived num_generations, and do not create files or
launch until the user confirms. Then initialise the sandbox (header rows only; programs/ is created
as children are evaluated):
<sandbox_root>/lae/
├── archive.tsv ← current elites = program database + checkpoint
├── history.tsv ← append-only record of every child
├── leaderboard.md ← rendered UI
└── programs/ ← one self-contained dir per program
The controller (loop)
You maintain num_islands MAP-Elites maps in archive.tsv, the append-only history.tsv, running
per-axis percentile stats, and leaderboard.md. You are the sole writer of all shared logs —
Mutators only return results, so there are no write races. Copy this checklist and tick items off:
GEN 0 — in each island, create the baseline program (a copy of <editable_files>) + optionally a few stochastic variants; cascade-evaluate; place in the archive.
Per generation: build EXACTLY <concurrency> tasks (round-robin island, seeded-rule parent, top num_top + num_diverse most-diverse inspirations); make each child dir by copying the parent program + harness.
Run the C Mutators (spawn-or-degrade), each with roles/Mutator.md, parent code, inspirations, parent artifacts, its child dir, and the smoke/full budgets.
For each returned child: append a history.tsv row; if evaluated, compute its niche → cell and place it in the island map iff <metric> is better (kept=y); record smoke_dropped/crash without placing.
Re-render leaderboard.md; checkpoint (archive.tsv is the checkpoint); print a status line.
Every migration_interval generations: ring-migrate top elites island k → k+1.
Stop at <total_budget> (reserve a little for synthesis), then synthesize the final report.
Niche computation (you do this, from a child's sandbox):
complexity = trainable param count (fallback: total LOC of the editable files + any files
the child added), log10-scaled.
diversity = average normalized edit distance of the program's concatenated code (editable +
added files) to a random sample of diversity_reference_size programs from its island (vs the
baseline if the island is near-empty). Higher = more novel.
Normalize each axis with running ~5th/95th percentiles (not raw min/max, so one outlier can't
collapse the range): scaled = clamp01((v − p5)/(p95 − p5)); bin = min(num_bins−1, int(scaled × num_bins)); cell = (complexity_bin, diversity_bin). Re-bin existing elites when a percentile
shifts enough to move an edge (keep the higher <metric> on collisions; the archive is small).
The Mutator's prompt (the sampler): parent code + inspirations + the parent's rendered artifacts
(<metric>, per-class accuracy, loss curve, stderr) + the instruction to return one SEARCH/REPLACE
diff. Single harness model — no LLM ensemble. The Mutator applies its diff in the child dir,
cascade-evaluates at the FIXED <budget> (the controller injects/caps the duration key on the run
command), and returns a result validated against schemas/result.schema.json:
{"child_id":"g3-i1-a2","parent_id":"g1-i1-a0","approach_summary":"add BatchNorm after conv2","sandbox_path":"<sandbox_root>/lae/programs/g3-i1-a2","status":"evaluated","smoke_metric":0.61,"metric":0.71}
status ∈ {evaluated, smoke_dropped, crash}; metric is null unless evaluated. Mutators
compute nothing about the archive — the controller derives every niche from the sandbox.
Program sandboxes. A parallel population doesn't map onto branches, so every program is a
self-contained, fully-runnable dir <sandbox_root>/lae/programs/<child_id>/; the archive references it
by id. Build each child dir by copying real files (the parent's <editable_files>, then apply the
diff, plus the harness/entrypoint code it imports) and evaluate from inside it
(cd <child_dir> && <entrypoint>). Symlink only large read-only data, never the entrypoint or any
imported .py: Python resolves a symlinked script's __file__ to the link target, so sys.path[0]
becomes the original dir and the child's model.py/dataset.py are silently shadowed by the baselines
— every architecture/data mutation becomes a no-op (tell-tale: identical loss curves across different
"architectures"). Isolation sanity gate: the harness logs the param count / a code fingerprint;
flag any child whose code changed but whose metric/loss curve is identical to its parent's (shadowed),
and fix the sandbox before placing it. The repo working tree is never mutated.
Final synthesis. Report the global-best program + its lae/programs/<id>/ path, the illuminated
complexity×diversity map (coverage + who won each region), per-island bests, and 2–3 notably diverse
runners-up.
Ledger
All three logs live under <sandbox_root>/lae/, tab-separated, never commas in free text. The
controller is the sole writer; resume from archive.tsv + history.tsv if interrupted.
archive.tsv — current elites + checkpoint. Header
island cell metric child_id parent_id sandbox_path complexity diversity:
history.tsv — every child, append-only. Header
gen island parent_id child_id smoke_metric full_metric status kept cell:
gen island parent_id child_id smoke_metric full_metric status kept cell
4 0 g2-i0-a3 g4-i0-a1 0.61 0.71 evaluated y (2,7)
4 1 g2-i1-a0 g4-i1-a2 0.40 - smoke_dropped n -
leaderboard.md — re-rendered each generation: global best + per-island coverage + the archive
ranked by <metric>. Report the best program at stop (not the last), the archive coverage, and a
few diverse runners-up. Leave lae/ untracked.
Constraints
A child works only inside its own lae/programs/<child_id>/ dir — it may edit the copied
<editable_files> and create new files there, but never modify any file outside it (the repo, the
read-only harness, the data, other programs' dirs are ground truth or shared state).
The controller is the sole writer of archive.tsv/history.tsv/leaderboard.md, so parallel
Mutators never race on the logs.
<concurrency> comes from the probe + the user's confirmation — never assume the box; pin one
child per GPU on multi-GPU; if a run OOMs/thrashes, lower C and say so (don't rewrite a child's
config to fit), because the box's limit is real and rewriting the child corrupts the comparison.
<budget> (epochs/time), <metric>, and the eval/test split are FIXED and out-of-bounds for
mutation. The controller injects <budget> on every run, overriding any duration the child set —
so "train longer" / change-the-metric / change-the-test-set can never win. Evolve the
model/optimizer/data pipeline, not the compute or the scoring; comparability across programs depends
on it.
Never symlink the entrypoint or any imported .py into a child dir (it shadows the child's code
via sys.path[0]); copy harness code, symlink only data, and run the isolation sanity gate before
placing a child — a shadowed result is a phantom.
Do not install new packages or modify the evaluation harness — <metric> is ground truth.
Do not pause to ask "should I continue?" Run until <total_budget> (reserving a little for
synthesis) or interrupt; if coverage stalls bias toward empty cells, if quality stalls exploit top
elites. A child that overruns its gate is killed and recorded as crash.
