By heurema
Run an evidence-driven dev pipeline: turn tasks into verifiable contracts via codebase scans, implement changes with lint/test/repair loops, audit diffs using 3 independent AI models, then bundle self-contained proof packages for CI verification.
npx claudepluginhub heurema/signumBootstrap project context (project.intent.md and project.glossary.json) from an existing codebase using deterministic scan + LLM synthesis + interactive editing. Use --harness to scaffold additional repo-level harness docs.
If the user's task is using Signum init command syntax instead of a feature request - for example:
**Goal:** Bundle all artifacts into a self-contained, verifiable proof package (schema v4.8) with embedded artifact contents.
Display to the user:
If the user's task is exactly `explain` (case-insensitive), do NOT run the pipeline. Instead, output this JSON and stop:
Use the Bash tool to prepare the workspace (in PROJECT_ROOT):
**Goal:** Transform the user's request into a verifiable contract.
**Goal:** Implement code changes according to the contract.
Evidence-driven development pipeline with multi-model code review. Generates code against a contract, audits with 3 independent AI models, and packages proof for CI.
- If any phase fails catastrophically (agent error, required file missing after agent run), **STOP** immediately and report: what phase failed, what file is missing, and what the user should do next.
If the user's task starts with `archive` (case-insensitive), do NOT run the pipeline. Instead, archive a completed contract.
If the user's task starts with `close` (case-insensitive), do NOT run the pipeline. Instead, mark a contract as closed (abandoned, no proofpack).
Before setup, determine the correct project directory. The pipeline MUST run in the target project's root, not an unrelated session CWD.
Parses a user feature request into a structured contract.json. Scans codebase for scope signals and risk assessment. Read-only -- never writes code files, only generates contract.json.
Implements code changes according to a contract.json specification. The ONLY agent in Signum that writes code. Includes a repair loop: generate -> check -> fix -> check (max 3 attempts).
Synthesizes project.intent.md and project.glossary.json from deterministic scan signals. Uses ranked source hierarchy and explicit-only Non-Goals extraction. Emits per-section evidence comments and confidence annotations. Read-only — never writes files directly (presents draft for user confirmation).
Semantic code reviewer using Claude Opus. Part of the multi-model audit panel. Analyzes diff against contract for bugs, security issues, and logic errors. Read-only -- never modifies code.
Combines multi-model review results into a consensus verdict. Reads review outputs from Claude, Codex, and Gemini, plus mechanic report. Applies deterministic synthesis rules to produce final audit decision. Read-only -- never modifies code.
SDLC enforcement for AI agents — TDD, planning, self-review, CI shepherd
Uses power tools
Uses Bash, Write, or Edit tools
Share bugs, ideas, or general feedback.
Programmatic Agent Constraint Toolkit — governance infrastructure for AI coding agents
Multi-AI orchestration pipeline with Task-based enforcement and Codex final gate
Quadruple verification for Claude Code — automatically blocks placeholder code, security vulnerabilities, and ensures output quality on every operation. Built by CustomGPT.ai for production teams.
Universal quality control orchestrator and final authority for any software development project. Dynamically discovers and coordinates with available sub-agents, performs comprehensive multi-dimensional quality assessment, security validation, and deployment readiness verification. Adapts to any project type, programming language, or development framework while maintaining enterprise-grade quality standards. Examples: <example>Context: Code changes ready for review across any project. user: 'Please review this code before commit' assistant: 'I'll use the 1-ceo-quality-control-agent to orchestrate comprehensive quality validation, discover available specialists, and perform final security scanning before approval.' <commentary>Universal quality control requires comprehensive validation across all dimensions regardless of project type.</commentary></example> <example>Context: Multi-agent work completion needing validation. user: 'Several agents completed their tasks, need quality review' assistant: 'Let me engage the 1-ceo-quality-control-agent to coordinate comprehensive validation across all completed work and ensure quality standards.' <commentary>Multi-agent coordination and quality validation applies to any development project.</commentary></example>
AI-First SDLC — zero-debt development with validators, enforcement, and workflows