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cladding

Unified Governance for AI-Coupled Engineering.
AI-generated code, held to the same bar as human code.

Reference implementation of the Ironclad standard. 27 detectors and a 13-stage gate verify, on every commit, that the code your AI assistant wrote still matches the spec.

Why

What you get

How a vanilla AI coding environment and a cladding environment behave when the same situation comes up.

The hero's 8/8 vs 2/8 is an early benchmark (details) · larger-scale measurements are in progress.

How it works

Spec → Code → Tests runs as a single cycle — the spec captures the why, Iron Law verifies the implementation, and Drift Detection blocks anything that no longer matches.

1. Spec — SSoT, single source of intent

The spec is where the why (what we're building and why) lives. A 4-tier (A/B/C/D) Single Source of Truth — intent on top, implementation below.

A outranks B — if code and spec disagree, the code is wrong. The spec is sealed because changing the why shakes everything downstream, so LLMs are kept out.

Sharded · multi-dev safe — spec/features/<slug>-<hash6>.yaml puts each feature in its own file with a 6-char hash ID (e.g. F-5f6b45). Two devs creating new features at the same time land in different files with different IDs — zero merge conflicts. Details: Hash-based feature IDs.

2. Code — Iron Law (required) gate

Every change has to clear all 13 stages — typically called from CI, a git pre-push hook, or manual clad check. Each stage ships with its own unit tests.

3. Tests — 27 drift detectors

Seven categories of mismatch across spec · code · test, all caught automatically. Full catalog: src/stages/detectors/README.md.

4. Cycle — one feature's lifecycle

The 4 steps that wrap Spec → Code → Test into a single cycle. Merge if drift is 0, block otherwise.

Multi-Agent Workflow

cladding is a 5-agent system working in concert. Each agent has a clear role under CQS (Command-Query Separation — the agents that do are kept apart from the agents that verify), so no agent can sign off on its own work. This is the foundation that maps cleanly to compliance regimes (EU AI Act · K-AI Framework · SOX).

Ecosystem

cladding sits at the intersection of three existing categories.

How cladding differs from the neighbors

• Spec Kit · OpenSpec · Tessl · Kiro help you write a good spec. cladding goes further — it verifies on every commit that the code still matches that spec.
• BMAD · ChatDev · Claude Code Agent Teams are about splitting work across multiple AI agents. cladding's 5 agents take that further by tying spec, code, and audit log into the same loop.
• tdd-guard forces test-first development. That's roughly what the Unit · Coverage stages do inside cladding's 13-stage gate.
• OpenHands · Cline · Aider · Goose are runners — they tell the AI to write code. cladding is the governance layer that verifies and controls what those runners produce.

cladding's edge is the combination — it folds the strongest parts of all four categories into one verification loop.

Install

Two steps: install the infrastructure, then create the project spec.

Step 1 — Install the infrastructure

Pick the route that fits how you work — both land in the same place:

(a) npm — for terminal / CI users

npm install -g cladding   # install the cladding CLI
cd <project>                # go to your project
clad setup                  # connect your AI tools (Claude / Codex / Gemini)

(b) Marketplace — for AI-tool plugin users

1. Open the plugin marketplace inside your AI tool (Claude Code · Codex CLI · Gemini CLI)
2. Search for cladding and install it
3. No clad setup needed — the plugin manifest wires everything

clad setup invokes the per-host activation commands automatically when claude / gemini binaries are on PATH. Safe to re-run after a cladding upgrade or after installing another AI tool.

About the MCP server. Every host gets cladding wired as an MCP server — only the wire location differs. Claude Code and Gemini CLI auto-start it through the plugin/extension manifest's mcpServers field; Codex through ~/.codex/config.toml [mcp_servers.cladding]; Cursor through ~/.cursor/mcp.json. You never invoke MCP directly — no /mcp slash, no manual server-connect step. The AI in each host calls cladding's tools (clad_create_feature, etc.) in response to natural-language requests; you keep typing /cladding:init plus normal chat.

Benchmark. v0.4.0 measurements show ~60% consistency improvement and ~50% LOC reduction vs unguided AI coding on a fixed task, with 100% drift detection across a 5-iteration dev cycle. Full methodology and honest caveats (some of the consistency gain is the "more-specific-prompt" effect, not exclusively cladding) in docs/benchmarks/v0.4.0-consistency-bench.md.

Step 2 — Init (create the project spec)

Inside your project, run it once from your AI tool:

[inside your AI tool] /cladding:init "B2B payment SaaS"

This creates spec.yaml and the 4-tier docs. One-time per project.

Three init scenarios

/cladding:init takes a natural-language intent and picks the right path on its own. Same command, three starting points.

Init once, then carry on

cladding's goal is to be the infrastructure that prevents spec ↔ code drift — after init, you just keep coding. The AI references the spec while it writes, and clad check runs automatically in CI or as a pre-commit hook to block anything that drifts. No extra commands to remember.

Status

_{100 test files · installable from the Claude Code · OpenAI Codex · Gemini CLI marketplaces.}

Road to Ironclad 1.0 — 1.0 locks when two independent implementations pass the L4 conformance fixtures (GOVERNANCE § 1). cladding is the first one.

Docs

• Why cladding (project context)
• 4-tier governance model
• Hash-based feature IDs
• 27 detector catalog
• Benchmark — event store trap catch
• A/B evaluation cases
• Governance · roadmap to 1.0

License

MIT. LICENSE · Related: Ironclad (the standard cladding implements) · harness-boot (the seed project).