simplicio-autoresearch

---

name: simplicio-autoresearch description: Evolutionary optimize-by-metric loop — mutate a target, evaluate against fixed criteria, KEEP if the score improves (commit) or REVERT if it doesn't (git checkout), repeat, plateau-break after N stagnated runs. Adapts Karpathy's autoresearch pattern (and the ECC autoresearch-agent) with mandatory yool guardrails (§11 caps), git-isolated branch discipline, an anti-Goodhart eval order (correctness gate FIRST, score SECOND, labeled tokenizer), a local-first mutation ladder, and a simplicio.savings-event/v1 receipt per run. Use when the user says "optimize this by ", "autoresearch loop", "evolutionary optimize", "mutate/eval/keep-revert", or asks to shrink tokens/latency/bundle size/improve pass-rate against a fixed, measurable eval. Worker: scripts/autoresearch.py.

simplicio-autoresearch — evolutionary optimize-by-metric loop

Credit: Andrej Karpathy's autoresearch pattern (https://github.com/balukosuri/Andrej-Karpathy-s-Autoresearch-As-a-Universal-Skill) and the ECC bundle's autoresearch-agent, adapted into a first-class simplicio skill. The upstream loop is NOT installed raw — it conflicts with this ecosystem's rules (unbounded iteration, no git isolation discipline, single-metric hill-climbing). This skill fixes all three. The mechanical bookkeeping (caps, git actions, journal, plateau math, receipt) is a deterministic, model-free worker (scripts/autoresearch.py) — it never mutates the target itself. You (the LLM driving this skill) propose the mutation; the worker enforces the contract around it.

yool guardrails (§11 — MANDATORY, not optional)

Register this skill's runtime as an agent with a hard cap on iterations and budget BEFORE the first mutation — an uncapped loop is a review-blocker, per spec:

### simplicio-autoresearch

- yool_id: `agent.dev.autoresearch`
- authority: dev
- lane: background
- agent_terms:
    cpu_quota_pct: 60
    disk_quota_mb: 100
    timeout_s: 300
    max_iterations: <N>          # MANDATORY — set at `init`, never omitted
    max_token_budget: <N>        # MANDATORY — set at `init`, never omitted

scripts/autoresearch.py init refuses to start (exit 2) without both --max-iterations and --max-token-budget as positive integers — the cap is enforced mechanically, not by convention. record refuses (exit 12) any iteration number beyond the frozen max_iterations.

When to use / when NOT to use

Fit requires: a concrete target file (or small set of files), a command that can score it, and a correctness gate that is genuinely binary.

Good fit (pilots)	Why
mapper — TOON encoder heuristics	eval = round-trip fixtures (correctness) + measured tokens on real artifacts (score)
dev-cli — prompt template	eval = the A/B bench pass-rate, fixed validation set
runtime — local model-ladder tuning	eval = ladder success-rate / latency on a fixed task set

Low fit — do NOT use	Why
sprint (planning code vs its own pytest suite)	the eval IS the test suite the code is graded against — the loop would learn to game its own gate (Goodhart)
an agent already in production	"cache is sacred" — mutating a live, cached agent config outside an offline branch risks correctness regressions no one is watching for
marketing copy with no fixed judge	a wobbling human/LLM judge with no binary criteria is not a gate, it's noise — the loop would hill-climb on judge mood, not quality

Contract — inputs you must have BEFORE calling init

• Target: one file (or a tight set) that the mutation touches.
• Eval command: prints {"gate": "pass"|"fail", "score": <number>} (JSON anywhere in stdout/ stderr, or gate: pass / score: 7 lines as a fallback) — see parse_eval_output in the worker. The eval includes the repo's OWN gate (lint + tests for the target's language/repo) — the loop must never commit red.
• Binary correctness criteria FIRST — round-trip lossless / fixtures / the target repo's test suite. This is the gate; it decides keep vs revert before the score is even consulted.
• A labeled metric SECOND — the score being optimized (tokens via a NAMED tokenizer, pass- rate, latency, bundle size). An unlabeled/unrotulado metric is not a valid score — 5 divergent token estimators disagree by ~30%; hill-climbing on an unlabeled number is a random walk. Pass --tokenizer-id <name> at init whenever the score is a token count.
• Fixed validation set (3-5 items), unchanged for the whole run — mutating criteria mid-run invalidates every prior keep/revert decision.
• Caps: --max-iterations, --max-token-budget (mandatory), optionally --cpu-quota-pct (default 60), --disk-quota-mb (default 100), --timeout-s (default 300), --plateau-n (default 5).

Step 1 — init (git isolation, baseline)

python3 scripts/autoresearch.py init --target <path> --eval "<eval command>" \
    --max-iterations 20 --max-token-budget 50000 [--tokenizer-id cl100k-estimate] \
    [--branch <name>] [--direction max|min] [--margin 0.0] [--plateau-n 5]

Refuses to start on main/master — it resolves the target's git root, checks out (creating if needed) an isolated autoresearch/<slug> branch, records the pre-mutation HEAD, and runs the eval command ONCE to capture the baseline gate + score. Print the store= path it returns; every later command needs --store <that path> (or export SIMPLICIO_AUTORESEARCH_STORE=<that path>).

Step 2 — the mutate/eval/keep-revert loop (repeat until cap or promise)

For each iteration N (1..max_iterations):

1. Mutate — apply ONE change to the target. Prefer the simplicio-runtime local ladder (qwen 64→600) for the mutation itself; a paid remote model is stage 5 ONLY, opt-in via --remote/--allow-remote with recorded escalation evidence. An optimizer that burns frontier tokens indefinitely contradicts the point of the exercise.
2. Record — let the worker run the eval and decide:

   python3 scripts/autoresearch.py record --iteration <N> --store <store> \
       --note "<what you changed>" --mutation-summary "<one-line>"
   

   Gate-first, always: a fail gate is always revert, no matter how good the score looks (anti-Goodhart). Only once the gate passes does the score decide keep vs revert, by --direction (max default; min for tokens/latency/size) beyond --margin. record performs the matching scoped git action itself — git add <target> && git commit on keep, git checkout -- <target> on revert — never touching anything outside the target file(s). hooks/action_gate.py stays active underneath: a mutation is a file edit, never a destructive op.
3. Plateau check every iteration (cheap, no LLM):

   python3 scripts/autoresearch.py plateau --store <store> --exit-code
   

   Exit 10 = --plateau-n (default 5) consecutive reverts in a row. Do a full rewrite of the target from scratch (not another small nudge), then acknowledge it so the streak resets:

   python3 scripts/autoresearch.py record --iteration <N> --store <store> --plateau-break \
       --note "plateau-break: full rewrite"
   

4. Health-check — every 10th record call prints a HEALTH-CHECK| reminder line: pause and re-validate the binary criteria + validation set haven't quietly drifted before continuing.

Step 3 — finish (squash + receipt)

python3 scripts/autoresearch.py finish --store <store> --message "<Conventional Commit subject>"

Squashes every kept commit since the branch's start HEAD into ONE commit (the whole run is a SINGLE task for the DoD — the diff + the journal is the evidence, not N micro-tasks). Refuses (BLOCKED, exit 12) if the final kept state's gate is not pass — a run can never "finish" on a losing state. Writes receipt.json (simplicio.savings-event/v1):

{
  "schema": "simplicio.savings-event/v1",
  "source": "autoresearch",
  "yool_id": "agent.dev.autoresearch",
  "baseline": {"gate": "pass", "score": 118.0},
  "actual": {"gate": "pass", "score": 71.0},
  "proof": {"kind": "autoresearch-eval-log", "path": ".../journal.jsonl"},
  "tokenizer": "cl100k-estimate",
  "iterations": 14, "kept": 4, "reverted": 9, "plateau_breaks": 1
}

baseline/actual are the values the eval command ITSELF produced, never hand-typed. finish also leaves the isolated branch checked out with the squashed commit — open the PR against it per the normal simplicio-tasks flow; do not merge to main from inside this skill.

Other verbs

python3 scripts/autoresearch.py eval --store <store>       # run the eval now, print gate/score
python3 scripts/autoresearch.py status --store <store>     # config + plateau streak + journal tail
python3 scripts/autoresearch.py selftest                   # pure decision math, no git/network

Non-negotiables

• Never run on main/master — init refuses; the worker mechanically enforces isolation.
• Never keep a failing gate, regardless of score — correctness before optimization, always.
• Never omit --max-iterations/--max-token-budget — a cap-less loop is blocked, not "faster".
• Never hand-type baseline/actual numbers in the receipt — they come from the eval command's own output, captured by the worker.
• Never squash-finish on a losing state — finish refuses when the winner's gate isn't pass.