For an organization to trust AI with its code, three things must hold — trust, traceability, and stability at scale....
Copy the install, test the workflow, then decide if it earns a permanent slot.
The signal is softer here. Treat it like a pattern source unless it solves a very specific gap.
Copy the install, test the workflow, then decide if it earns a permanent slot.
You can test this quickly and remove it cleanly if it misses.
GitHub health unknown. no security policy. 0 open issues make this testable, but not something to trust blind.
AI Agent
Multiple
Model
Claude
Fastest way to find out if cladding belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
claude mcp add cladding -- npx claddingRun this first. You will know quickly if the workflow earns a permanent slot.
claude mcp remove claddingNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude.json └─ mcp_servers/ └─ cladding ← registers here
For an organization to trust AI with its code, three things must hold — trust, traceability, and stability at scale. cladding wraps your AI coding agent: your intent goes in before it writes, and the result is verified against your spec after, so those three are earned, not assumed. First L4 implementation of the Ironclad standard.
Source: GitHub repository
Source check: No source check recorded
Repository state: Not marked archived
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