⚠️ Deprecated (September 2026), and that is the good news

ForgeCraft's job was to set a project up to Generative Specification and encode its quality properties. That job now belongs to the model itself. Point a capable coding assistant at the GS white paper and field guide and ask it to set the project up as GS: it will author the sentinel navigation tree and wire the gates for you, tuned to your codebase, with no separate tool. That is the discipline's own thesis coming true, the adoption friction folded into the executor.

• Setup is now a prompt: give your assistant the white paper / field guide (DOI https://doi.org/10.5281/zenodo.21726017, and pragmaworks.dev) and ask it to configure the project as GS.
• The gates are standard CI hooks (tsc, eslint, jscpd, coverage, dependency-cruiser, commit-msg) the model wires from the spec. The hard-won gate set that catches the pathology catalog is preserved in this repo's src/analyzers/ for reference, and a curated gate template will live at pragmaworks.dev.
• What still adds value are the composable GS MCP servers that do what a model cannot cheaply do itself: Chronicle (cross-session memory) and Chronos (git-history graph). ForgeCraft is not one of them.

This package receives no further updates. The last release stays installable for reference.

ForgeCraft

The quality contract your AI coding assistant works within.

You hired an AI engineer. It's brilliant. It also installed the same 14 VS Code extensions twice today, spun up 6 Docker containers it will never clean up, and your disk went from 12 GB free to 0 KB in one session.

A full disk doesn't fail gracefully. It kills VS Code, the terminal, Docker, and the database simultaneously.

ForgeCraft is the quality contract your AI coding assistant works within — so it builds fast and doesn't burn down the house.

npx forgecraft-mcp setup .

Supports: Claude (CLAUDE.md) · Cursor (.cursor/rules/) · GitHub Copilot (.github/copilot-instructions.md) · Windsurf (.windsurfrules) · Cline (.clinerules) · Aider (CONVENTIONS.md)

A quality framework for AI-assisted software development

Every session, every project, every AI assistant — measured against the same 7-property Generative Specification model. Not vibes. Not a linter score. A score out of 14 that tells you exactly where the gap is and why.

$ npx forgecraft-mcp verify .

| Property        | Score | Evidence                                        |
|-----------------|-------|-------------------------------------------------|
| Self-Describing | ✅ 2/2 | CLAUDE.md — 352 non-empty lines                |
| Bounded         | ✅ 2/2 | No direct DB calls in route files              |
| Verifiable      | ✅ 2/2 | 64 test files — 87% coverage                   |
| Defended        | ✅ 2/2 | Pre-commit hook + lint config present           |
| Auditable       | ✅ 2/2 | 11 ADRs in docs/adrs/ + Status.md              |
| Composable      | ✅ 2/2 | Service layer + repository layer detected       |
| Executable      | ✅ 2/2 | Tests passed + CI pipeline configured           |

Total: 14/14 ✅ PASS · Threshold 11/14

Dev environment hygiene — enforced by convention

ForgeCraft injects enforceable rules into every project's AI instructions that make environment pollution a convention violation, not an incident.

VS Code extensions Before installing: code --list-extensions | grep -i <name>. Only install if no version in the required major range is already present. The same extension doesn't get downloaded twice in the same day.

Docker containers Check before creating: docker ps -a --filter name=<service>. If it exists, start it — don't create it. Prefer docker compose up (reuse) over bare docker run (always creates new). Logs capped at 500 MB. docker system prune -f is documented as a periodic maintenance step, not an emergency.

Exception: Multiple containers of the same service are permitted when they differ meaningfully in plugin set or major version — for example, a postgres-pgvector container alongside a standard postgres container. Name containers to reflect the variant (e.g., db-pgvector, db-timescale); otherwise the deduplication rule applies.

Python virtual environments One .venv per project root. Reuse if the Python major.minor version matches. Never create a venv in a subdirectory unless it's a standalone installable package. Unused dependencies flagged by pip list --not-required.

Synthetic and time-series data Before writing more than 100 MB of generated data, the AI asks: retain raw, condense statistically, or delete after the run? Synthetic datasets older than 7 days with no code reference: ask to delete.

General If the workspace grows beyond 2 GB outside of known build artifacts (node_modules/, .venv/, dist/), surface a warning and stop. Never silently grow the workspace.

Project setup in one sentence

Read the spec in docs/specs/, set up this project with ForgeCraft,
scaffold it with the right tags, recommend the tech stack, start building.

That's the entire onboarding prompt. ForgeCraft reads the spec, the AI assigns the tags, and ForgeCraft writes the instruction file, emits Status.md, docs/adrs/, docs/PRD.md, docs/TechSpec.md, hooks, and skills. The AI has full context. You start building.

ForgeCraft scans your project, auto-detects your stack, and generates tailored instruction files from 116 curated blocks — SOLID, hexagonal architecture, testing pyramids, CI/CD, and 24 domain-specific rule sets — in seconds.

Quality gates

Quality gates are structured pass/fail checks your AI assistant runs at defined moments — before a commit, before a release, after a deployment. They're not linter rules. Each gate has a condition, an evidence requirement, and a flag for whether human review is mandatory.

Gates are organized by release phase so you're not running pre-release chaos tests on day one of a greenfield project:

Gates tagged requires_human_review: true cannot be auto-passed — some checks require a human.

The full gate library, contribution guide, and schema are in the quality gates repository →

ADRs, automatically sequenced

Every non-obvious architectural decision gets recorded. ForgeCraft auto-sequences docs/adrs/NNNN-slug.md in MADR format — context, decision, alternatives, consequences. Your AI assistant reasons about past choices. Your team stops re-litigating them.

npx forgecraft-mcp generate_adr . --title "Use event sourcing for order history" \
  --status Accepted \
  --context "Order mutations need full audit trail for compliance" \
  --decision "Append-only event log, project current state on read"
# → docs/adrs/0004-use-event-sourcing-for-order-history.md

AI assistant setup vs ForgeCraft

claude init, Cursor's workspace rules, or Copilot's instructions file get you started. ForgeCraft gets you to production standards — across every AI assistant, every session, every engineer on the team.

Workflow Playbook

After setup, your AI has the context. These prompts direct the work. Copy, paste, run.

→ Full Workflow Playbook · Online version

How It Works

# First-time setup — auto-detects your stack
npx forgecraft-mcp setup .

flowchart TD
    A["<b>setup .</b><br/>npx forgecraft-mcp setup ."] --> B["Phase 1 — Analyze<br/>Reads spec · infers tags"]
    B --> C{AI assistant\nin the loop?}
    C -->|"Yes (MCP)"| D["Phase 2 — Calibrate<br/>LLM corrects tags from spec<br/>Writes forgecraft.yaml · CLAUDE.md<br/>PRD.md · hooks · ADR-000"]
    C -->|"No (CLI only)"| E["⚠️ CLI-only mode<br/>Directory heuristics only<br/>→ configure an AI assistant"]
    D --> F["<b>check_cascade</b><br/>5-step readiness gate<br/>1 · Functional spec<br/>2 · Architecture + C4<br/>3 · Constitution<br/>4 · ADRs<br/>5 · Use cases"]
    F --> G{All 5 passing?}
    G -->|"Stubs / missing"| H["Fill artifacts<br/>docs/PRD.md · docs/adrs/<br/>docs/use-cases.md"]
    H --> F
    G -->|"✅ All pass"| I["<b>generate_session_prompt</b><br/>Bound context for next task"]
    I --> J["Implement with TDD<br/>RED → GREEN → REFACTOR<br/>+ Documentation Cascade"]
    J --> K["<b>audit_project</b><br/>Score 0 – 100"]
    K --> L{Score ≥ 90?}
    L -->|"Violations found"| M["WORKFLOWS.md remediation<br/>file_length · layer_violation<br/>hardcoded_url · missing_prd"]
    M --> J
    L -->|"✅ Score ≥ 90"| N["<b>close_cycle</b><br/>Re-check cascade · assess gates<br/>promote to registry · bump version"]
    N --> O{Roadmap\ncomplete?}
    O -->|"More features"| I
    O -->|"All done"| P["<b>start_hardening</b><br/>Mutation tests · OWASP · load test"]
    P --> Q["🚢 Ship"]

    style A fill:#1a2e1a,color:#90ee90,stroke:#3a6e3a
    style Q fill:#1a2a3e,color:#87ceeb,stroke:#3a5a8e
    style E fill:#2e1a1a,color:#ffaa88,stroke:#6e3a3a
    style M fill:#2e2a00,color:#ffd700,stroke:#6e6000

ForgeCraft is a setup-time CLI tool. Run it once to configure your project, then remove it — it has no runtime footprint.

Optionally add the MCP sentinel to let your AI assistant diagnose and recommend commands:

claude mcp add forgecraft -- npx -y forgecraft-mcp

The sentinel is a single tool (~200 tokens). It reads three artifacts — forgecraft.yaml, CLAUDE.md, .claude/hooks — derives the correct next CLI command, and returns it. Nothing more. This is the methodology's core principle expressed as tool design: a stateless reader, a finite artifact set, a derived action. Remove it after initial setup to reclaim token budget.

What You Get

After npx forgecraft-mcp setup, your project has:

your-project/
├── forgecraft.yaml        ← Your config (tags, tier, customizations)
├── CLAUDE.md              ← Engineering standards (Claude)
├── .cursor/rules/         ← Engineering standards (Cursor)
├── .github/copilot-instructions.md  ← Engineering standards (Copilot)
├── Status.md              ← Session continuity tracker
├── .claude/hooks/         ← Pre-commit quality gates
├── docs/
│   ├── PRD.md             ← Requirements skeleton
│   └── TechSpec.md        ← Architecture + NFR sections
└── src/shared/            ← Config, errors, logger starters

The Instruction Files

This is the core value. Assembled from curated blocks covering:

• SOLID principles — concrete rules, not platitudes
• Hexagonal architecture — ports, adapters, DTOs, layer boundaries
• Testing pyramid — unit/integration/E2E targets, test doubles taxonomy
• Clean code — CQS, guard clauses, immutability, pure functions
• CI/CD & deployment — pipeline stages, environments, preview deploys
• Domain patterns — DDD, CQRS, event sourcing (when your project needs it)
• 12-Factor ops — config, statelessness, disposability, logging

Every block is sourced from established engineering literature (Martin, Evans, Wiggins) and adapted for AI-assisted development.

24 Tags — AI-detected, user-adjustable

Tags tell ForgeCraft what your project is. On first setup, the AI analyzes your spec and codebase and assigns them. You can review and override in forgecraft.yaml. Blocks merge without conflicts — add or remove tags as the project evolves.

The full tag list and contribution guide live in the quality gates repository →

Content depth tiers

Not every project needs DDD on day one.

Set in forgecraft.yaml:

projectName: my-api
tags: [UNIVERSAL, API]
tier: recommended

CLI Commands

npx forgecraft-mcp <command> [dir] [flags]

Common flags

--tags UNIVERSAL API     Project classification tags (or read from forgecraft.yaml)
--tier core|recommended  Content depth (default: recommended)
--targets claude cursor  AI assistant targets (default: claude)
--dry-run                Preview without writing files
--compact                Strip explanatory bullet tails and deduplicate lines (~20-40% smaller output)
--apply                  Apply changes (for refresh)
--language typescript    typescript | python (default: typescript)
--scope focused          comprehensive | focused (for review)

MCP Sentinel

Optionally add the ForgeCraft MCP sentinel to let your AI assistant diagnose your project and suggest the right CLI command:

The sentinel is a single minimal tool (~200 tokens per request, vs ~1,500 for a full tool suite). It checks whether forgecraft.yaml, your AI instruction file, and your hooks exist, then returns the targeted CLI command for the project's current state.

The design is intentional. The full ForgeCraft command surface — 21 actions — lives in the CLI, not the MCP server. The MCP server exposes exactly one tool that reads three artifacts and returns one recommendation. This is the Generative Specification principle in the tool's own architecture: a stateless reader, a bounded artifact set, a derived action. The tool practices what it writes into your instruction files.

A side effect: every declared MCP tool is read by the model on every turn whether invoked or not. One tool costs 200 tokens. Twenty-one tools costs 1,500. The sentinel keeps the methodology's recommended MCP budget (≤3 active servers) by design.

Recommended workflow:

1. Add the sentinel to your AI assistant (see config examples below)
2. Let your AI assistant run npx forgecraft-mcp setup .
3. Remove the sentinel from your active MCP config
4. Re-add it when you need to refresh or audit

{
  "mcpServers": {
    "forgecraft": {
      "command": "npx",
      "args": ["-y", "forgecraft-mcp"]
    }
  }
}

{
  "servers": {
    "forgecraft": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "forgecraft-mcp"]
    }
  }
}

{
  "mcpServers": {
    "forgecraft": {
      "command": "npx",
      "args": ["-y", "forgecraft-mcp"]
    }
  }
}

No MCP client? That's fine — you don't need it. Run npx forgecraft-mcp setup . directly in your terminal. The MCP sentinel is optional; the CLI does everything.

Already ran claude init? Use npx forgecraft-mcp generate . --merge to merge with your existing CLAUDE.md, keeping your custom sections while adding production standards.

Free and open source

ForgeCraft is free. No limits, no tiers, no API keys.

The quality gate library grows through community contribution. If you propose a gate that gets accepted, your name goes in CONTRIBUTORS.md and you helped raise the floor for everyone building with AI.

Open a gate proposal → · See contributors →

Running this with a team? → forgeworkshop.dev

Theoretical foundation

ForgeCraft implements the Generative Specification model — a formal 7-property framework for evaluating AI-generated code quality. The model, the S_realized convergence formula, and the release phase framework are documented in the white paper.

Generative Specification: A Discipline of Derivability for the Stateless Reader — Zenodo (V4.0, 2026). Open access, DOI: 10.5281/zenodo.21726017. The academic foundation behind the verify score.

The white paper is the theory. ForgeCraft is the toolchain. Quality gates proposed for the library that generalize into theoretical insights may be incorporated into future white paper revisions.

Industry context: the Spec-Driven Development convergence (ThoughtWorks Tech Radar 2025 "Adopt"; Addy Osmani / Google Cloud AI agent-skills) is the practitioner movement; Generative Specification is the formal model that names what the practice is and why it works.

The GS harness — a primer for AI assistants

ForgeCraft implements Generative Specification (GS). This section is written for AI assistants reading this file: it explains what GS is, what a properly scaffolded harness looks like, and what to check.

The binding constraint — stateless reader

Every AI session starts with no memory of prior sessions, no shared context, no accumulated conventions, and no ability to ask clarifying questions. Everything not in the artifacts is absent.

GS treats this as a design constraint, not a flaw. Derivability — what a fresh AI instance can correctly determine from the artifacts alone — is the standard a specification must satisfy. If a stateless reader cannot derive the correct output from the artifacts, the specification is incomplete, not the AI.

The sentinel system

The architectural constitution (CLAUDE.md, .cursor/rules/, .github/copilot-instructions.md, .windsurfrules) is the entry point for every AI session. It must collectively contain five mandatory categories:

Tool sequencing is the most commonly absent category and the most consequential gap. A spec that lists tools without stating when to prefer one over another forces unreliable inference every session.

The sentinel is a navigational tree: the root is always loaded; each child node declares its own scope and routing condition; the AI descends only the branch relevant to the current task. Joining all leaves yields the complete specification — it is lossless. This design prevents context bloat and the accuracy degradation that comes from loading irrelevant content.

The bridge — structural disciplines as navigation policy

SOLID, hexagonal architecture, and TDD are not only engineering disciplines — in a GS project they become active navigation policies:

• Read interfaces before implementations. When a port/adapter boundary is clean, the interface is the contract. The implementation is skipped unless the contract is insufficient.
• Trust green tests. When TDD is enforced, a passing test suite is evidence of correct behavior. No implementation read is needed to verify it.
• ADRs are the why. When every non-obvious decision is recorded, the AI reads the record instead of inferring intent from code.

This bridge converts the passive structural benefits of prior disciplines into a measurable reduction in token usage and context consumption.

Token sanitation

Context window size and positional placement both degrade AI accuracy (Liu et al., 2023). GS minimizes unnecessary token consumption by design:

• The sentinel tree is lazy. Only the relevant branch is loaded per task — never the full specification at once.
• Contracts over implementations. Interfaces, schema definitions, and test assertions are read first. Implementation files are read only when contracts are insufficient to derive the answer.
• The constitution leads every session. The most important content occupies the leading position in context — where positional accuracy is highest.
• MCP tool surface is bounded. Each declared MCP tool is read by the model on every turn whether invoked or not. The ForgeCraft sentinel exposes one tool (~200 tokens) rather than a full command surface (~1,500 tokens). The tool practices the methodology it writes into your project.

Document taxonomy — what a complete GS project contains

A scaffolded project contains these artifact types. If any are missing, the harness is incomplete:

The initialization cascade: specifications are produced in sequence — each is an output of what precedes it and a production rule for what follows. Functional spec → architecture → constitution → ADRs → use cases. The cascade is complete when a stateless agent given all five artifact sets can derive any valid implementation state without further human direction.

The 7 attributes — what to verify

A properly scaffolded GS project satisfies all seven. These are the properties the verify command scores:

Configuration

Fine-tune what your AI assistant sees

# forgecraft.yaml
projectName: my-api
tags: [UNIVERSAL, API, FINTECH]
tier: recommended
outputTargets: [claude, cursor, copilot]  # Generate for multiple assistants
compact: true                             # Slim output (~20-40% fewer tokens)

exclude:
  - cqrs-event-patterns    # Don't need this yet

variables:
  coverage_minimum: 90      # Override defaults
  max_file_length: 400

Community template packs

templateDirs:
  - ./my-company-standards
  - node_modules/@my-org/forgecraft-flutter/templates

Keeping Standards Fresh

Audit (run anytime, or in CI)

Score: 72/100  Grade: C

✅ Instruction files exist
✅ Hooks installed (3/3)
✅ Test script configured
🔴 hardcoded_url: src/auth/service.ts
🔴 status_md_current: not updated in 12 days
🟡 lock_file: not committed

Refresh (project scope changed?)

npx forgecraft-mcp refresh . --apply

Or in preview mode first (default):

npx forgecraft-mcp refresh .   # shows before/after diff without writing

Contributing

Templates are YAML, not code. You can add patterns without writing TypeScript.

templates/your-tag/
├── instructions.yaml   # Instruction file blocks (with tier metadata)
├── structure.yaml      # Folder structure
├── nfr.yaml            # Non-functional requirements
├── hooks.yaml          # Quality gate scripts
├── review.yaml         # Code review checklists
└── mcp-servers.yaml    # Recommended MCP servers for this tag

PRs welcome. See templates/universal/ for the format.

MCP Server Discovery

npx forgecraft-mcp configure-mcp dynamically discovers recommended MCP servers matching your project tags. Servers are curated in mcp-servers.yaml per tag — community-contributable via PRs.

Built-in recommendations include Context7 (docs), Playwright (testing), Chrome DevTools (debugging), Stripe (fintech), Docker/K8s (infra), and more across all 24 tags.

Optionally fetch from a remote registry at setup time:

# In forgecraft.yaml or via tool parameter
include_remote: true
remote_registry_url: https://your-org.com/mcp-registry.json

Development

git clone https://github.com/jghiringhelli/forgecraft-mcp.git
cd forgecraft-mcp
npm install
npm run build
npm test   # 610 tests, 42 suites

License

MIT

Part of Generative Specification

A free tool behind Generative Specification (GS) — the discipline for building software with AI that doesn't drift: you author a specification precise enough that a stateless AI derives correct code from it, and a harness verifies it against a live system.

• 📄 White paper (open access): https://doi.org/10.5281/zenodo.21726017
• 🧭 Start here — method, tools, testimonials: https://pragmaworks.dev
• 🔨 The Forge — 2-day hands-on GS workshop for your team: https://forgeworkshop.dev