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  • License MIT

Switchboard CLI — portable governance substrate for AI workflows

Package Exports

  • switchboard-cli
  • switchboard-cli/src/index.ts

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (switchboard-cli) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

@switchboard/cli

Portable governance substrate for AI workflows.

Compile specs, dispatch to Claude/ChatGPT/Cursor/Codex, ingest structured returns, issue trust receipts, evaluate independently, and calibrate your pipeline.

Alpha release. Expect breaking changes. Report issues.

Install

npm install -g @switchboard/cli

Requires Node >= 18.

Quick start

# Initialize a governed project
sb init

# Compile project contract + spec from canonical state
sb compile

# Generate a dispatch packet for a surface
sb packet claude    # or: chatgpt, cursor, codex

# Launch governed dispatch to Claude Code
sb run claude

# Ingest the structured return
sb ingest

# Issue an immutable trust receipt
sb receipt

# Export receipt as standalone JSON
sb receipt --export json

# Run independent evaluation
sb evaluate

# Run the two-layer calibration harness
sb calibrate

Pipeline

init -> compile -> packet -> run -> ingest -> receipt -> evaluate
                                                            |
                                                        calibrate

Each step reads from and writes to .switchboard/ in your project root. All state is file-based (YAML + Markdown). No database, no hosted service.

Commands

Command What it does
sb init Scaffold .switchboard/ directory with contract, working state, and spec
sb compile Compile a loop contract from canonical state
sb packet <surface> Generate a governed dispatch packet (SBX bundle)
sb run claude Dispatch to Claude Code via Agent SDK with proof capture
sb ingest Ingest structured SB_RETURN.yaml, run reconciliation + gates
sb receipt Build immutable ReceiptIssuedV2 from persisted ingest artifacts
sb receipt --export json Export receipt as standalone JSON file
sb receipt --export yaml Export receipt as standalone YAML file
sb evaluate Run independent evaluation on the latest receipt
sb audit codex Generate Codex audit packet for independent review
sb calibrate Run two-layer calibration harness

Calibration

The calibration harness tests the trust machinery itself:

  • Layer A (internal): Runs seeds through the real governance pipeline with full trace access
  • Layer B (external): Scores independently with zero imports from core — only sees raw output
  • Blind seeds: Fresh ideas the system has never seen
  • Adversarial seeds: Designed to break the machinery (scope traps, non-goal evasion, evidence gaps)
  • Living ledger: Every run appends to an auditable history with trends
# Default run: 5 blind + 3 adversarial seeds
sb calibrate

# Custom mix
sb calibrate --blind 10 --adversarial 5

# Stress test only
sb calibrate --adversarial-only 8

# Review pending escalations
sb calibrate --review

# Show calibration trends
sb calibrate --trends

Trust machinery

The governance pipeline enforces:

  • Honesty guardrails: Banned terms ("certified", "guaranteed", "no issues") in receipts
  • Reconciliation: Six drift checks (scope inflation, non-goal violation, goal drift, target-user shift, untracked decisions, evidence contradiction)
  • Gates: Approval gates for scope changes, unparented decisions, weak evidence, trust boundary changes
  • Claim spread: Deterministic measurement of how far executor claims drift from proven evidence
  • Receipts: Immutable trust artifacts with claim assessments, verification results, and closure basis

What this is NOT

  • Not a chat wrapper
  • Not an LLM gateway
  • Not an eval platform
  • Not an autonomous agent

It is a governance substrate. It preserves project coherence while you bounce work across AI surfaces.

License

MIT