• Principle #10: Match the form to the failure — classify the baseline failure before writing a rule; prohibitions bulletproof discipline failures but measurably backfire on shaping failures (obra/superpowers wording tests + Guardrails polarity data). Plus: no nuance clauses, exemption clauses don't scope.
• Critique-before-verdict judges — all three eval agents (grader, comparator, bineval) now write the detailed evidence critique BEFORE committing to the 1/0 verdict, with a borderline few-shot example in each (Hamel Husain's judge methodology).
• Threshold-blind judging — the BinEval judge no longer computes the overall score or the GATE; the orchestrator aggregates. A judge that knows the bar is biased toward it.
• Automatic cross-family judge calibration — a second judge from a different model family answers the same bank; stable disagreement flags a badly worded question, not a dispute. Self-preference-bias guard on final acceptance.
• Variance discipline — improvements on non-critical questions count only when they reproduce in 2 consecutive runs; the 3-iteration cap now carries its STICK rationale.
• references/pressure-testing.md — micro-test protocol (no-guidance control, 5+ reps, variance as a metric) + pressure scenarios for discipline skills (7 pressure types, forced A/B/C choice, rationalization tables).
• Pushy description formula — [What] + Use when [4-5 phrasings] + "even if they don't explicitly say '<canonical term>'" + Do NOT use for [...], deduped to a single canonical home in Principle #2.
• Question bank v1.1 — 5 new questions: pushy triggers, nuance clauses, directive reference loading, time-rot language, redundant-content (E:A:R).
• Self-hosted proof — this release was produced by Conductor evaluating and improving itself: 3 gated iterations, dual-family judges (Claude + GPT via codex), all critical questions passing.

• Held-out gate for body edits — Mode 2 IMPROVE now splits evals into train/held-out (scripts/split_evals.py, deterministic, stratified by optional evals[].category). Lessons and edits come from TRAIN only; a candidate is accepted iff no held-out assertion regresses (flip-confirmation re-run for noise), train pass-rate strictly improves, and no new critical failure. Methodology borrowed from microsoft/SkillOpt.
• Edit budget — at most 3 atomic edits per iteration (one edit = one lesson, labeled); no wholesale rewrites, so gate rejections stay attributable.
• Case transitions — assertion-level diff parent→candidate (improved / regressed / persistent-fail / stable-success) recorded as an additive transitions block in benchmark.json.
• Refactor — split_eval_set generalized into utils.split_evals(stratify_key=...); run_loop.py (Mode 5 OPTIMIZE) delegates to it, split unchanged bit-for-bit.

• BinEval evaluation — replaces the old 5-axis 1-10 scoring with atomic binary yes/no questions across 5 dimensions (Discovery, Clarity, Structure, Robustness, Completeness). Each answer carries grounding evidence; the pass criterion is a gate on critical questions, not an opaque number. Adapted from "Ask, Don't Judge" (arXiv 2606.27226).
• Deterministic + LLM split — eval_skill.py --json emits structural checks as binary question records; an evaluator agent answers the judgment questions with evidence and a self-update loop feeds failing questions back into edits.
• 9 authoring principles — a universal canon (pre-flight, no-process-in-description, MOC, fresh-practitioner author, TWI "why", blind-agent test, inline checklists, one-term-per-concept, cut-the-fat) in references/sop-practices.md, applied to every skill.
• Dual-channel install — one repo, one source of truth, installable via skills.sh and the Claude Code plugin marketplace.

• references/sop-practices.md — 80 years of Standard Operating Procedure wisdom applied to skill authoring. Inline checklists at risk-points, pre-flight checks, programmatic validation, exception handling patterns. Use for procedural skills (client intake, onboarding, reporting, escalation)
• scripts/test_smoke.py — fast safety net for skill-conductor scripts themselves. Verifies critical scripts execute on known-good skills, fail on known-bad, produce expected output shapes. Run: uv run scripts/test_smoke.py
• Updated eval agents (grader, comparator, analyzer) with refined rubrics
• Improved package_skill.py, eval_skill.py, and schema validation
• Updated patterns.md and schemas.md with tighter definitions

Anthropic updated their skill-creator with serious eval infrastructure. We took the best of it:

From Anthropic's skill-creator:

• 3 specialized agents: grader (assertion checking + claim extraction), comparator (blind A/B testing), analyzer (post-hoc root cause analysis)
• Parallel eval execution with isolated contexts (no cross-contamination)
• Automated description optimization with train/test split (60/40)
• Benchmark tracking: pass rate, tokens, time with variance analysis
• HTML eval viewer with qualitative + quantitative tabs

What Conductor adds on top:

• Architecture before code. 5 patterns (Sequential, Iterative, Context-Aware, Domain Intelligence, Multi-MCP) with selection criteria. Pick wrong = rewrite everything later
• Degrees of freedom. Low (deterministic scripts) → Medium (pseudocode) → High (free text). Match freedom to risk tolerance
• TDD RED before writing. Verify the agent fails WITHOUT the skill first. If it already handles the task — you don't need a skill
• Quality scoring with a gate (now BinEval — see v1.0.0). Numbers and evidence, not a "vibe check"
• Skill categorization. Capability uplift (teaching something new) vs Encoded preference (sequencing known abilities). Different skills need different testing strategies