skill-conductor

A skill that creates, evaluates, and improves other skills. Meta-level.

Architecture-first skill lifecycle: design → build → test → evaluate → package.

Most skill tools jump straight to "write SKILL.md." Conductor makes you choose the architecture first — because rewriting a wrong pattern costs more than writing it right.

Install

# skills.sh — installs into ~/.claude/skills
npx skills add smixs/skill-conductor

# Claude Code plugin
/plugin marketplace add smixs/skill-conductor
/plugin install skill-conductor@smixs

<strong>v3.0.0 — BinEval scoring, English canon, dual-channel install</strong>

• 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.

<strong>v3: SOP practices + smoke tests</strong>

• 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

<strong>v2: Anthropic's eval engine meets architecture-first design</strong>

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

Synthesized from

1. Anthropic Skill Creator — eval infrastructure, grader/comparator/analyzer agents, benchmark pipeline
2. The Complete Guide to Building Skills for Claude — architecture patterns, success metrics
3. Superpowers / writing-skills by Jesse Vincent — TDD approach, the "description trap" discovery
4. Skills Best Practices by Minko Gechev — three-stage LLM validation, eval methodology

Methodology foundations

The 9 authoring principles and the BinEval scoring draw on established procedure-writing and evaluation research:

• Standard Operating Procedures: A Writing Guide — Richard Stup, Penn State Extension. Format selection, hierarchical vs. flowchart procedures.
• Procedure Writing: Principles and Practices — Wieringa, Moore & Barnes (Battelle Press, 1998). Imperative steps, removing modal weasel-words.
• Toyota TWI (Training Within Industry) — the "Job Instruction" method: step → key point → why; the 5 Whys root-cause practice.
• McDonald's Operations Manual — the canonical 600+ page SOP system; checklists at the point of use.
• Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement — the BinEval method behind Conductor's evaluation.

6 Modes

Mode	What it does
CREATE	Architecture selection → TDD baseline → scaffold → write → verify → refactor
IMPROVE	Diagnose → eval loop → self-update loop (failing questions → targeted edits) → iterate
VALIDATE	Structural checks + trigger testing + BinEval scoring
REVIEW	Pass/fail quality gate for third-party skills before you install them
OPTIMIZE	Auto-tune the description for accurate triggering with a train/test split
PACKAGE	Validate structure + package as .skill for distribution

Architecture patterns

Choose before writing a single line:

Pattern	Use when
Sequential workflow	Clear step-by-step process
Iterative refinement	Output improves with cycles
Context-aware selection	Same goal, different tools by context
Domain intelligence	Specialized knowledge beyond tool access
Multi-MCP coordination	Workflow spans multiple services

Eval infrastructure

                    ┌─────────┐
                    │  SKILL  │
                    └────┬────┘
                         │
              ┌──────────┼──────────┐
              │          │          │
         ┌────▼────┐ ┌──▼───┐ ┌───▼────┐
         │ Grader  │ │ A/B  │ │Analyzer│
         │         │ │Blind │ │        │
         │assertions│ │compare│ │root    │
         │+ claims │ │      │ │cause   │
         └─────────┘ └──────┘ └────────┘
              │          │          │
              └──────────┼──────────┘
                         │
                   ┌─────▼─────┐
                   │ Benchmark │
                   │ mean±std  │
                   └───────────┘

Quality is scored with BinEval: binary yes/no questions per dimension, each answered with evidence; the skill passes when every critical question answers yes — not when a scalar clears a threshold.

Installation layout

skills/
└── skill-conductor/
    ├── SKILL.md
    ├── agents/
    │   ├── grader.md
    │   ├── comparator.md
    │   ├── analyzer.md
    │   └── bineval.md
    ├── eval-viewer/
    │   ├── generate_review.py
    │   └── viewer.html
    ├── references/
    │   ├── patterns.md
    │   ├── schemas.md
    │   ├── sop-practices.md
    │   ├── bineval-method.md
    │   ├── quality-questions.md
    │   └── runtime-setup.md
    ├── assets/
    │   └── eval_review.html
    └── scripts/
        ├── init_skill.py
        ├── eval_skill.py
        ├── run_eval.py
        ├── run_loop.py
        ├── improve_description.py
        ├── aggregate_benchmark.py
        ├── generate_report.py
        ├── package_skill.py
        ├── quick_validate.py
        ├── test_smoke.py
        └── utils.py

Claude Code: the install commands above drop it into .claude/skills/. Auto-activates when the agent detects a skill-building task.

Key discovery

Never put process steps in the skill description. If your description says "exports assets, generates specs, creates tasks" — the model follows the description and skips the body. Tested experimentally.

# ✅ Good
description: Analyze design files for developer handoff. Use when user uploads .fig files.

# ❌ Bad - model follows this and ignores SKILL.md body
description: Exports Figma assets, generates specs, creates Linear tasks, posts to Slack.

License

MIT — see LICENSE.