prd-spec-generator

What An Agent Asks · Getting Started · Pipeline · Tools · Verification · Calibration · Architecture · Zetetic Standard

Companion projects:
Cortex — persistent memory that injects past decisions into every PRD
zetetic-team-subagents — 97 genius reasoning patterns that judge each claim
automatised-pipeline — the codebase intelligence layer this generator consumes upstream

Every AI agent that drafts a PRD eventually invents a function that doesn't exist, claims latency it can't measure, or writes acceptance criteria that don't tie back to the requirements they're supposed to test. The output sounds confident. It is not actionable. The next stage in the pipeline — code generation, ticket import, sprint planning — silently inherits the hallucination, ships it, and pays for it later.

prd-spec-generator is a TypeScript MCP server that fixes this at the structural level. The pipeline is a stateless reducer (step(state, result?) → next_state, action) driven by a host (Claude Code or any MCP-speaking agent). Sections are produced one at a time, validated by deterministic Hard Output Rules before the host ever sees them, and every load-bearing claim is judged by a panel of genius reasoning agents drawn from zetetic-team-subagents against the codebase graph from automatised-pipeline. Phase 4 then closes the loop: per-judge reliability is calibrated from history, retry budgets are derived from survival statistics, KPI gates are tuned against frozen baselines, and held-out partitions are mechanically sealed so no calibration result can be peeked at before evaluation.

10 packages. 17 MCP tools. 10 pipeline steps. Multi-judge verification with consensus. Closed-loop calibration with externally-grounded falsifiers. 629 tests. Every numeric constant traces to a citation, a benchmark, or a // source: provisional heuristic admission.

Phase 4 — closed-loop reliability calibration (shipped)

The verification subsystem is no longer a one-shot pass/fail report. Every claim resolution can flush an observation back to a calibration repository, every consensus run can pull calibrated posteriors from history, and every closed loop runs an external control arm so the calibration's effect is measured, not assumed.