| name | skill-conductor |
| description | Create, edit, evaluate, and package agent skills. Use when building a new skill from scratch, improving an existing skill, running evals to test a skill, benchmarking skill performance, optimizing a skill's description for better triggering, reviewing third-party skills for quality, or packaging skills for distribution. Not for using skills or general coding tasks.
|
Skill Conductor
Full lifecycle management for agent skills: draft → test → review → improve → repeat.
One skill to rule them all — from architecture to packaging. The core loop is always the same: write something, test it, see what fails, fix it, test again.
Runtime requirements (pre-flight)
Before any mode that touches scripts (CREATE, IMPROVE, VALIDATE, OPTIMIZE, PACKAGE), run the pre-flight block → references/runtime-setup.md (checks uv, sets UV_BIN/SKILL_CONDUCTOR_DIR, verifies LLM access). If uv is absent, stop and tell the user.
How to communicate
Read context cues. If the user is a skill author iterating on their own work, be direct and technical. If they're new to skills, explain the why behind each step — not just what to do, but why it matters. Default to conversational, not robotic.
- Explain trade-offs when there's a real choice to make
- Use concrete examples over abstract rules
- When something fails, explain the root cause, not just the fix
- Imperative voice in instructions: "Extract the data", not "You should extract"
Modes
Detect mode from context. If ambiguous, ask.
| Mode | When | What happens |
|---|
| 1. CREATE | "build a skill", "new skill for..." | Full lifecycle: intent → architecture → scaffold → write → test |
| 2. IMPROVE | "fix this skill", "it doesn't trigger" | Diagnose → eval loop → self-update loop → iterate |
| 3. VALIDATE | "test this skill", "run evals" | Structural checks + trigger testing + BinEval scoring |
| 4. REVIEW | "review this skill", third-party assessment | 11-point quality gate, quick and focused |
| 5. OPTIMIZE | "improve triggering", "description optimization" | Automated description optimization with train/test split |
| 6. PACKAGE | "package for distribution" | Validate + bundle into .skill file |
Mode 1: CREATE
Step 1: Capture Intent
Before writing anything, extract 2–3 concrete scenarios.
Ask:
- "What specific task should this skill handle?"
- "What would a user say to trigger it?"
- "What should NOT trigger it?"
Don't move on until you have a clear picture of what the skill does, for whom, and when. This prevents the most common failure: a skill that does something but triggers for the wrong things.
Step 2: Baseline (TDD RED)
Before writing the skill, verify the agent fails without it:
- Take one scenario from Step 1
- Run it in a clean session without the skill
- Document what went wrong — what the agent guessed, what it missed
If the agent already handles it perfectly, the skill is unnecessary. This sounds obvious, but it's the most skipped step and the most valuable one.
Step 3: Architecture
Choose a primary pattern from references/patterns.md (can combine):
| 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 |
Choose degrees of freedom — this determines how much control vs. flexibility the skill gives the agent:
| Freedom | When | Example |
|---|
| Low (scripts) | fragile, error-prone, must be exact | PDF rotation, API calls |
| Medium (pseudocode) | preferred pattern exists, some variation ok | data processing |
| High (text) | multiple valid approaches, judgment needed | design decisions |
Golden rule: read references/sop-practices.md before authoring or reviewing ANY skill. It holds the canonical 9 authoring principles (universal): pre-flight, no-process-in-description, MOC (SKILL.md = map, not prose), fresh-practitioner author, TWI "why", blind-agent test, inline checklists, one-term-per-concept, cut-the-fat (env/keys OUT of SKILL.md). For procedural skills (business process with branching: request, quote, onboarding, escalation) the same file also has the deep SOP methodology — format selection, 7-step process, procedural checklist.
Step 4: Scaffold
uv run scripts/init_skill.py <skill-name> --path <output-dir> [--resources scripts,references,assets]
Or create manually:
skill-name/
├── SKILL.md # required — the brain
├── scripts/ # deterministic operations (executed, not loaded)
├── references/ # detailed docs (loaded on demand)
└── assets/ # templates, images for output (never loaded)
Step 5: Write SKILL.md
Frontmatter
---
name: kebab-case-name
description: >
[Purpose in one sentence]. Use when [triggers].
Do NOT use for [negative triggers].
---
The description is the single most important line. It determines whether the skill gets triggered at all. Rules:
name: lowercase, digits, hyphens only. No consecutive hyphens. Matches folder name. Max 64 charsdescription: max 1024 chars. No angle brackets. No process/workflow steps- Start with purpose, then "Use when...", then "Do NOT use for..."
- Don't put workflow in the description — tested: when the description lists process steps, the agent follows it and skips the body entirely
description: Analyze Figma design files for developer handoff. Use when user uploads .fig files or asks for "design specs". Do NOT use for Sketch or Adobe XD.
description: Exports Figma assets, generates specs, creates Linear tasks, posts to Slack.
Body structure
# Skill Name
## Overview
What this enables. 1-2 sentences. Core principle.
## [Main sections]
Step-by-step with numbered sequences.
Concrete templates over prose.
Imperative voice throughout.
## Common Mistakes
What goes wrong + how to fix.
## Troubleshooting (if applicable)
Error: [message] → Cause: [why] → Fix: [how]
Writing rules
- One term per concept. Pick "template" and stick with it — not template/boilerplate/scaffold (Principle 8)
- SKILL.md = map, not prose. Body is a table-of-contents pointing to references; detail lives there (Principle 3)
- No secrets/env in SKILL.md. No keys, passwords, tokens, env values, or user-absolute paths (
/home/<user>, /Users/<user>) — reference them, never inline (Principle 9a)
- Progressive disclosure. SKILL.md = brain (<500 lines). References = details. One level deep
- Token budget. Frequently loaded: <200 words. Standard: <500 lines. Heavy: move to references/
- No junk files. No README, CHANGELOG inside the skill
- Scripts: bundle when same code rewritten repeatedly, or operation is fragile. Must return descriptive stdout/stderr on failure
- Imperative voice. Use "Extract the data", not "you should extract" or capitalized "MUST/NEVER" — explanation > rule (see
references/sop-practices.md Principle 5, TWI)
Step 6: Test Cases & Eval Loop
This is the critical step — most failures hide here. Treat it as three sub-phases.
6a. Pre-flight (before spawning anything)
If any item fails — fix before proceeding. A missing workspace dir mid-run loses outputs.
6b. Run loop (do all in one turn)
| What | Key move | Why |
|---|
| Spawn with-skill runs | One subagent per eval, skill active, save outputs to iteration-N/<eval-name>/with_skill/ | Parallel = same wall time as one run |
| Spawn baseline runs in the same turn | Same prompt, no skill (or old version snapshot for IMPROVE), save to without_skill/ or old_skill/ | If you wait, baselines drift in time and aren't comparable |
| Draft assertions while runs execute | Pull verifiable statements from eval prompts | Don't waste the 5–15 min of subagent time |
| Capture timing on each notification | Save total_tokens, duration_ms to timing.json immediately | Notification is the only source — process per-arrival, don't batch |
6c. Post-run checklist
The last bullet is the trap. If you skip user review and "improve" based on your own reading of outputs, you optimize against your taste, not the user's.
Step 7: Verify & Refactor
- Does the skill trigger automatically for the right queries?
- Does the agent follow body instructions (not just description)?
- Does the output meet use case requirements?
- Does it NOT trigger on unrelated queries?
If any fail → iterate. Find how the agent rationalizes around the skill, plug loopholes, re-verify.
Mode 2: IMPROVE
Step 1: Diagnose
Read the existing SKILL.md completely. Identify the problem class:
| Problem | Signal | Fix |
|---|
| Undertriggering | skill doesn't load | add keywords, trigger phrases, file types to description |
| Overtriggering | loads for unrelated queries | add negative triggers, be more specific |
| Skips body | follows description only | remove process/workflow from description |
| Inconsistent output | varies across sessions | add explicit templates, reduce freedom, add scripts |
| Too slow | large context | move detail to references/, cut body to <500 lines |
Improvement mindset
- Generalize from feedback. You're iterating on a few examples, but the skill will be used on thousands of prompts. Don't overfit — avoid fiddly patches or oppressive MUSTs for one test case. Try different metaphors or patterns instead
- Keep the prompt lean. Read transcripts, not just outputs. If the skill makes the model waste time on unproductive steps, remove those instructions and see what happens
- Explain the why. LLMs have good theory of mind. Instead of ALWAYS/NEVER in caps, explain the reasoning — it's more powerful and robust. If you're writing rigid rules, reframe as explanations
- Look for repeated work. If all test runs independently write the same helper script, bundle it in
scripts/. Saves every future invocation from reinventing the wheel
- Apply the authoring canon. Read
references/sop-practices.md — the 9 canonical principles (universal) map directly to skill failure modes: process leaking into description, SKILL.md bloated instead of a map, env/keys inlined, silent improvisation from missing "why", missed edge cases, agents skipping end-of-doc checklists. For process skills (ticket, quote, escalation) also apply the deep SOP methodology in the same file
Step 2: Eval Iteration Loop
The improvement cycle mirrors CREATE Step 6, but focused on the broken behavior:
- Run the failing case with current skill → document failure
- Apply fix using writing rules from CREATE Step 5
- Run eval again → grade with
agents/grader.md
- Launch viewer:
uv run eval-viewer/generate_review.py <workspace>
- Headless/Cowork: use
--static <output.html> instead of live server
- Review, provide feedback, iterate
Step 3: Self-Update Loop
Drive iteration off failing BinEval questions, not taste. Run the loop:
- Generate questions and evaluate the skill (see Mode 3 Stage 3 +
references/bineval-method.md) → collect failing[]
- Spawn
agents/analyzer.md as note-taker: turn the failing questions + their explanations into generalized, deduped lessons (not one-off patches for a single test case)
- Apply targeted edits addressing those lessons
- Re-evaluate. Revert any edit that introduces a NEW failing question.
- Terminate when
failing[] (or its critical subset) is empty, or after 3 iterations. Keep the best result by gate_passed first, then overall S.
Step 3b: Blind Comparison (optional, for major changes)
When you have two meaningfully different versions:
- Run both versions on the same evals
- Spawn
agents/comparator.md — answers the SAME binary questions for outputs A and B without knowing which skill produced which
- Comparator reports per-dimension yes-rate for each version; winner = higher overall yes-rate, tiebreak = critical-dimension yes-rate
- Spawn
agents/analyzer.md — unblinds results, analyzes WHY the winner won
- Apply insights to improve the losing version
This prevents bias. The comparator judges output quality, not skill design.
Mode 3: VALIDATE
Three stages, run in order.
Stage 1: Structural Validation
uv run scripts/eval_skill.py <skill-folder>
Checks: frontmatter, naming, description quality, process leak detection, body size, structure, scripts. Target: 10/10, no warnings.
Stage 2: Discovery (trigger testing)
Generate 6 test prompts:
- 3 that SHOULD trigger the skill
- 3 that should NOT (similar-sounding but wrong domain)
Run each in clean session. Target: 6/6 correct.
For automated trigger testing at scale, use:
uv run scripts/run_eval.py --eval-set <path> --skill-path <path> --runs-per-query 3
Stage 3: BinEval Scoring
Evaluate with atomic binary yes/no questions across 5 dimensions — each answered 1/0 with evidence. See references/bineval-method.md for the method, references/quality-questions.md for the question bank, and agents/bineval.md for the evaluator that emits bineval.json.
The 5 dimensions: Discovery, Clarity, Structure, Robustness, Completeness.
Questions come from two sources:
- Deterministic — emitted by
scripts/eval_skill.py --json (the sole emitter), e.g. DET-STRUCT-SKILLMD-EXISTS, DET-DISCOVERY-DESC-PRESENT. Some are flagged critical.
- Generated — per-skill binary questions via the two-step meta-prompt (summarize the skill into requirements → decompose each into ≥1 yes/no question with a violation example).
Aggregate: per-dimension dimension_scores S_d = mean of that dimension's answers; overall S = mean of all answers.
Display bands: S≥0.90 production-ready · 0.70–0.89 solid · 0.50–0.69 needs-work · <0.50 rewrite.
GATE = every critical question (deterministic + critical bank questions) answered 1. The GATE is the pass criterion — not the scalar S.
Mode 4: REVIEW
Quick quality gate for third-party skills.
Checklist (pass/fail)
[ ] SKILL.md exists, exact case
[ ] Valid YAML frontmatter (name + description)
[ ] name: kebab-case, matches folder, ≤64 chars
[ ] description: ≤1024 chars, no angle brackets
[ ] description has triggers ("Use when...")
[ ] description has NO workflow/process steps
[ ] No README.md inside skill folder
[ ] SKILL.md < 500 lines
[ ] References max 1 level deep
[ ] Scripts tested and executable
[ ] No hardcoded paths/tokens/secrets
Then run VALIDATE Stage 2 (discovery) on the description. Report score + checklist.
The deterministic subset of this checklist is emitted as binary BinEval question records by scripts/eval_skill.py --json (e.g. DET-STRUCT-SKILLMD-EXISTS, DET-DISCOVERY-DESC-PRESENT, DET-ROBUST-NO-SECRETS) — the sole emitter of those records.
The checklist exists because these are the failure modes that actually happen in practice — especially process-in-description, which causes the agent to skip the body entirely.
Mode 5: OPTIMIZE
Automated description optimization. The description competes with other skills for Claude's attention — optimization finds the wording that triggers most accurately.
How it works
- Create an eval set: 20 queries (10 should-trigger, 10 should-not)
Writing good eval queries
Queries must be realistic — concrete, detailed, with file paths, context, abbreviations, typos. Not "Format this data" but "my boss sent Q4 sales final FINAL v2.xlsx, add profit margin % column, revenue is col C costs col D".
Should-trigger (10): Different phrasings of the same intent — formal, casual, implicit. Include cases where user doesn't name the skill but clearly needs it. Add competing-skill edge cases.
Should-NOT-trigger (10): Near-misses that share keywords but need something different. Adjacent domains, ambiguous phrasing. "Write fibonacci" as negative for PDF skill = useless — too easy. Make negatives genuinely tricky.
Triggering mechanics: Claude only consults skills for tasks it can't handle directly. Simple queries ("read this PDF") won't trigger skills regardless of description — Claude handles them with basic tools. Eval queries must be substantive enough that consulting a skill would help.
- Review queries in the browser:
assets/eval_review.html
- Run the optimization loop:
uv run scripts/run_loop.py \
--eval-set evals/eval_set.json \
--skill-path <skill-dir> \
--model claude-sonnet-4-20250514 \
--max-iterations 5 \
--holdout 0.4 \
--verbose
The loop:
- Splits queries into train (60%) and test (40%) to prevent overfitting
- Each iteration: evaluates current description → Claude proposes improvement → re-evaluates
- Improvement model sees only train results (blinded to test)
- Selects the best description by test score
- Opens live HTML report automatically
Supporting scripts
| Script | Purpose |
|---|
scripts/run_eval.py | Run trigger evaluation on a description |
scripts/improve_description.py | Claude proposes improved description |
scripts/generate_report.py | HTML visualization of optimization history |
scripts/aggregate_benchmark.py | Statistical aggregation of benchmark runs |
Mode 6: PACKAGE
- Run REVIEW checklist (Mode 4)
- Validate:
uv run scripts/quick_validate.py <skill-folder>
- Package:
uv run scripts/package_skill.py <skill-folder> [output-dir]
Creates skill-name.skill (zip with .skill extension). Verify: unzip in temp dir, check structure intact.
Quick Reference
Skill categories
- Document/Asset Creation — consistent output (docs, designs, code)
- Workflow Automation — multi-step processes with methodology
- MCP Enhancement — workflow guidance on top of tool access
- Procedural / Process — business procedures with decision points and exceptions (handling a request, generating a quote, processing an invoice, onboarding, escalation). For these → read
references/sop-practices.md
File purposes
| Directory | Loaded? | Purpose |
|---|
| SKILL.md | on trigger | brain — instructions |
| references/ | on demand | detailed docs, schemas |
| scripts/ | executed, not loaded | deterministic operations |
| assets/ | never loaded | templates, images |
Progressive disclosure budget
| Level | When loaded | Budget |
|---|
| Frontmatter | always (system prompt) | ~100 words |
| SKILL.md body | on trigger | <500 lines |
| Bundled resources | on demand | unlimited |
Description formula
[What it does] + Use when [triggers, file types, symptoms]. + Do NOT use for [negatives].
Reference Files
| Path | What's inside |
|---|
agents/grader.md | Evidence-based assertion grading |
agents/comparator.md | Blind A/B output comparison |
agents/analyzer.md | Post-hoc analysis + benchmark notes |
agents/bineval.md | BinEval evaluator — emits bineval.json |
references/patterns.md | 5 architectural patterns + anti-patterns |
references/schemas.md | JSON schemas for evals, grading, benchmark |
references/bineval-method.md | BinEval method: dimensions, scoring, GATE |
references/quality-questions.md | BinEval question bank (deterministic + bank) |
references/sop-practices.md | Canon: 9 authoring principles (universal) + deep SOP methodology for procedural skills |
references/runtime-setup.md | Pre-flight: uv/env/path checks, LLM-access options |
eval-viewer/ | Interactive HTML viewer for eval results |
assets/eval_review.html | Trigger eval set editor |
scripts/eval_skill.py | Structural validation (10-point scoring) |
scripts/init_skill.py | Skill scaffolder |
scripts/run_eval.py | Trigger evaluation runner |
scripts/run_loop.py | Eval + improve optimization loop |
scripts/improve_description.py | Claude-powered description improvement |
scripts/aggregate_benchmark.py | Benchmark statistics aggregator |
scripts/generate_report.py | HTML report generator |
scripts/quick_validate.py | Quick validation for packager |
scripts/test_smoke.py | Smoke tests for all scripts (12 tests) |
scripts/package_skill.py | Skill → .skill packager |
scripts/utils.py | Shared utilities (parse_skill_md) |
