Skill Gauntlet

Skill Gauntlet — regression-guarded A/B evaluation

Adopt a candidate only when it beats the champion on value dimensions and does not regress on guard dimensions. Totals and preference votes measure added value; conjunctive guards protect existing value. Candidates that win every blind forced choice can still be correctly rejected — that is the harness working, not failing.

Three inviolable rules:

1. Pre-register everything before running any cell — the adoption gate, the cells, the instruments, the comparison set. No goalpost movement after results arrive. If a bar turns out to be mis-set, report both readings; the pre-registered text decides.
2. Blind everything scoreable. Judges, auditors, and executors never see version names, the sealed mapping, or planted-flaw keys. Only the coordinator holds the mapping.
3. The champion is frozen. Never modify the champion artifact during a round, not even "harmless" fixes. Hash it (md5) before and after.

The workflow

Phase 0 — Scope and design

• Define the champion (current shipped artifact) and the candidate (exact diff from champion; verify by diff that nothing else changed).
• Design briefs: 2–4 realistic tasks hard enough that the skill should matter, in fresh domains the skill has never seen. For skills with detection claims, plant 3–4 flaws per brief and write the key matrix (see references/key-verification.md).
• Design fixtures (see references/fixtures.md): a framing-invariance pair (same facts, hyped vs skeptical register) and a negative fixture (a task too small for the skill — the highest-information single cell; every evolution round ratchets toward more machinery, so price restraint).
• Freeze the rubric: 7–9 dimensions split into value dims (where the candidate claims gains) and guard dims (where regression is fatal). Anchor every odd rung. See references/judge-panel.md.

Phase 1 — Pre-register the gate

Write the adoption rule into the results file BEFORE any execution. Conjunctive template (adapt dims; full template in references/protocol.md):

Candidate passes iff ALL of: (1) ≥ champion on every guard dim, on every instrument; (2) value-dim margins ≥ pre-set floors; (3) key detection ≥ relative-to-champion AND ≥ 90% absolute floor; (4) framing invariant; (5) negative fixture behaves; (6) instrument checks pass (controls gap, inter-judge agreement). Anything less → champion ships unchanged, candidate's deltas ship only as a research note.

Add the symmetric-loss clause: on failure the candidate branch is discarded; its deltas return to an inventory with failure data attached. Never "fix and re-judge the same run" — that is goalpost movement.

Phase 2 — Execute cells blind

Spawn one executor subagent per cell. Each executor gets only: the skill path (champion or candidate), the brief verbatim, an output path. No testing context, no keys, no version names. Cells: candidate × briefs × N runs (N≥2 for variance), champion × same, plus fixtures for both, plus 1 generic control per brief (no skill at all) — controls calibrate the discrimination floor.

Phase 3 — Blind and leak-scan

Copy raw outputs to anonymized codes via scripts/normalize.py (mapping JSON → blind codes; runs a leak regex scan; inspect hits manually, never auto-fix). Codes and judge panel assignments go in a coordinator-only sealed mapping file.

Phase 4 — Score with three instrument classes

Run all three in parallel; they are blind to each other and to the mapping.

• Judge panels (2–3 persona-diverse judges, crossed design: every brief scored by ≥2 judges, every judge sees both arms mixed): dimension-major scoring, forced choice per brief, suspected-generic call. Prompt patterns and persona design: references/judge-panel.md.
• Cross-check auditor: re-scores the guard dims mechanically (rule-text applied literally), runs the framing and restraint gates. When judges and auditor disagree on a guard dim, the conservative reading governs. See references/auditor.md.
• Key verifier: grades planted-flaw detection (✓/~/✗ with evidence pointers, hard-grader rule: torn → lower grade). See references/key-verification.md.

Pool judges with scripts/scorecard.py (judges must emit a machine-readable scores block — required by the panel prompt).

Phase 5 — Apply the gate exactly

Compute margins per dimension, check every conjunctive condition, write the verdict. On PASS: repackage/reinstall the candidate, write the round report. On FAIL: champion ships unchanged; write the research note capturing validated wins, failure autopsies, and the next candidate's specified fixes. Either outcome is a deliverable.

Hard-won operating rules (each cost a round to learn)

• Dimension-major scoring (score dim 1 across all reports, then dim 2…) is what makes inter-judge agreement reach ~100% cell-level within-1pt. Do not let judges score report-by-report.
• The negative fixture is the highest-information cell. Skills drift toward ceremony; both champion and candidate once ran full machinery on a trivial task. Keep one in every round.
• Absolute 100% detection bars are usually unattainable on freshly planted hard keys — pre-register relative-to-champion plus a 90% floor instead, and say so before running.
• Instruments will disagree. Judges grade holistically; the auditor applies rule text mechanically. Both readings are real. Pre-commit to the conservative one for adoption decisions.
• Prose bans do not stop template-driven leaks. If the report template is organized as numbered stages, executors will cross-reference "Stage 4" no matter how strongly the skill forbids it. Enforce with a mechanical pre-delivery grep gate, or de-ordinal the template.
• A compliance failure is not a design failure — log which one you saw. A candidate whose mechanism works but whose self-check missed a case needs a checklist fix, not a re-mine.
• Do not chase a specific key. If every arm misses the same planted flaw the same way across builds, document it as a hard cell; tuning the skill to that cell teaches to the test and corrupts the harness.
• Margins are not decision-grade without uncertainty. With the small N these rounds run, pre-register an INCONCLUSIVE verdict (collect more cells) alongside PASS/FAIL, and treat any margin inside the noise band as "not yet evidence." Details in references/protocol.md.
• Guard dims include operational ones when the artifact ships to production — cost, latency, output-schema compatibility. A candidate that triples cost or breaks downstream consumers must fail the gate even when quality dims pass.
• Delete subagents after their results arrive; never spawn judges who can see each other's scores.
• Normalize format tells in the blinder. When arms differ in template format (numbered vs unnumbered headers), every judge can identify the arm at a glance. Strip the tell from ALL blind copies (normalize.py --strip-h2-ordinals) while leaving prose leaks intact — those are auditor evidence.
• A fixture is burned the moment its answer enters any candidate's text. Quoting the expected fixture answer inside a skill (even as an example) means restraint on that fixture can never be measured again. Rotate fixtures every round; grep candidates for fixture content before running.
• Auditor prompts quote the pre-registered rule text verbatim — never paraphrase. A paraphrase that adds one category ("tag or class names") can charge rubric-required content as violations and split your instruments; score any extra category separately as exploratory, never in the binding number.
• The cheap spread noise-band is direction-blind. A candidate above the champion in every single cell can still read as "inside the band". Wherever cell-level dominance is plausible, pre-register a paired sign test alongside the spread heuristic.
• Blind codes, judge IDs, and rubric dimension names share one namespace across ALL rounds. Keep them disjoint (no D5 code when D5 is a dimension; no J3 code when J3 is a judge).