Conductor

A thin deterministic conductor that drives Claude Code as its engine.

The skeleton (route -> gate -> execute -> verify -> retry -> fallback -> log) is real code that cannot be skipped. The judgment (reasoning, extraction, semantic checks) is delegated to hermetic claude -p calls on your subscription. Reliability comes from putting structure in code and quarantining the model to the judgment-shaped holes.

Use it in Claude Code

The conductor ships as an MCP server, so its verified commands appear as native tools in any Claude Code session (extract_contact, redact_pii, summarize, normalize_phone, ...), each routed through verify + retry + fallback. Three ways to install, simplest first.

A. One command (this machine):

git clone <repo> && cd conductor && npm install && npm run build
claude mcp add conductor -s user -- node "$PWD/dist/conductor-mcp.cjs"

Remove with claude mcp remove conductor -s user.

B. As a plugin (shareable):

claude plugin marketplace add <repo-or-path>
claude plugin install conductor@conductor

The plugin bundles the prebuilt, self-contained server (dist/conductor-mcp.cjs) and wires it via .mcp.json using ${CLAUDE_PLUGIN_ROOT} - no build or deps on the user's side. (Note: opening this repo directly as a CC project will show a failed .mcp.json because that variable only expands when installed as a plugin - harmless.)

C. Published (future): after npm publish, the plugin's .mcp.json can run npx -y conductor-mcp, so users install with nothing local at all.

Requirement: the machine needs the claude CLI installed and authenticated - stochastic commands run hermetic claude -p sub-calls on that subscription.

Why it's reliable (the load-bearing ideas)

• Hermetic calls. Every model call fully replaces the system prompt and loads no settings, so ambient context (global CLAUDE.md, your identity, foreign hooks) can't leak in. We caught this for real: a naive call returned the operator's own email instead of the input's. See src/cc.ts.
• Mandatory verifiers. A command can't even register without a verify export (src/registry.ts). Mechanical checks first (schema, regex, grounding), LLM-judge only for genuinely semantic criteria.
• Fallback-to-freeform. Every chain ends at plain Claude, so the conductor can never do worse than raw Claude, only better when a verified command applies.
• Composition that can't silently compound errors. runPipeline threads verified steps; each step's output is checked before the next runs (src/pipeline.ts).
• Retry-with-feedback. A failed verifier's feedback is fed into the next attempt.
• Telemetry. Every node is logged to JSONL (src/telemetry.ts), the substrate for reliability-aware routing (next) and RL reward (later).
• Determinism gradient. pure commands run as cached deterministic code (free, 100% reliable); stochastic commands use the model; effectful will need a gate.

Command library

command	kind	verifier highlight
slugify	pure	url-safe slug shape
word_count	pure	recomputes + asserts equality (self-checking)
normalize_phone	pure	+ and digits only, length bound
extract_contact	stochastic	schema + email/phone format + grounding (value must appear in source)
summarize	stochastic	sentence budget + compression
extract_keywords	stochastic	each keyword must appear verbatim in source (grounded)
classify_sentiment	stochastic	enum label + confidence in 0..1
redact_pii	stochastic	no email/phone may remain in output
json_extract	stochastic	every requested field present as a key
truncate	pure	output length never exceeds max
dedupe_lines	pure	no duplicates; every line from the source
extract_urls	pure	each URL matches pattern and appears in source
template_fill	pure	no unresolved {{placeholders}} remain
fix_json	stochastic	result is a valid parsed JSON value
extract_dates	stochastic	each date is valid ISO-8601 YYYY-MM-DD
categorize	stochastic	chosen category is one of the allowed set
translate	stochastic	non-empty, length-sane translation
base64_encode	pure	round-trips (decode equals input)
base64_decode	pure	round-trips (re-encode equals input)
extract_emails	pure	each email valid and present in source
json_minify	pure	output is JSON-equivalent to input
to_snake_case	pure	recomputed canonical form
detect_language	stochastic	valid ISO 639-1 two-letter code

Run it

npm install

# pure command, no model call, instant
npx tsx src/cli.ts slugify '{"text":"Hello, World!"}'

# stochastic command, hermetic model call + mechanical verify + fallback
npx tsx src/cli.ts extract_contact '{"text":"Call Sam at [email protected]"}'

# per-command test suite (each command vs its tests/cases.json)
npx tsx eval/run-tests.ts            # all
npx tsx eval/run-tests.ts slugify    # one

# evals: baseline (naive) vs conductor
REPEATS=2 npx tsx eval/run-eval.ts        # single-shot extraction
REPEATS=2 npx tsx eval/contact-card.ts    # composition pipeline
REPEATS=1 npx tsx eval/triage.ts          # 4-step pipeline

# reliability scoreboard (what the router consults) + goal-based routing
npm run scores
npx tsx src/cli.ts route "make a url slug" '{"text":"Hello World"}'

Results (measured)

• Per-command tests: 25/25 across the stochastic+pure suite (plus pure exact-match).
• Single-shot extraction (run-eval): baseline 100% vs conductor 100%, +0. Honest: on easy single-shot tasks a frontier model is already reliable, so there's no gap to close. The value isn't here.
• Composition (contact-card): baseline 80% vs conductor 100%, +20 points. The baseline emitted a phone number with an invisible Unicode character; the deterministic normalize_phone step is structurally immune to that class of error. Cost tradeoff: conductor ~2x the baseline (more calls per task).

Takeaway: the reliability delta grows with composition depth and machine-consumed output, not with single easy calls. The harness now measures it either way.

Layout

src/
  cc.ts          one hermetic claude call (structured output, retry)
  registry.ts    loads commands; ENFORCES "no command without a verifier"
  router.ts      structured pick from the command list (reliability-weighted next)
  verify.ts      verifier helpers: schema, regex, grounding/PII detectors, llmJudge
  telemetry.ts   jsonl logging + rolling reliability scores
  freeform.ts    the universal fallback rung (plain Claude)
  solve.ts       the loop: execute -> verify -> retry -> fallback
  pipeline.ts    compose verified commands (errors can't silently compound)
  cli.ts         manual driver
commands/<name>/index.ts        manifest + optional run() + MANDATORY verify()
commands/<name>/tests/cases.json  {input, expect?} cases
eval/
  run-tests.ts   per-command test runner
  run-eval.ts    single-shot extraction: baseline vs conductor
  contact-card.ts  composition pipeline: baseline vs conductor
projects/<p>/    telemetry.jsonl (per project)

What's deliberately deferred

Router using telemetry scores, recursion/decomposition by the model (pipelines are explicit for now), persistent plan/memory files, human gates for effectful commands, semi-auto induction (trace -> new command), and RL on the router. Each later layer trusts the earlier one, so the inner loop ships and is measured first.