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The package manager for AI agent configuration

Define AI agent configuration once. Render it for every tool.

Author instructions, skills, hooks and MCP configs as versioned packages. Pin them to each repository, and Workloom renders the files each AI coding tool reads, holds risky assets for approval, and tells you when disk drifts from the desired state.

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Sample render plan$ wl pull --dry-run

package @acme/base-instructions@2.1.0

  • claudeCLAUDE.mdpassive_text · automatic
  • claude.claude/skills/review/SKILL.mdpassive_text · automatic
  • claude.claude/hooks/pre-commit.shexecutable_hook · needs approval
  • codexAGENTS.mdpassive_text · automatic
  • codex.codex/config.tomltool_permission · needs approval

Sample data · 1 package · 2 providers · 5 files · 2 held for approval

One source, every provider, nothing silent

From a package to files on disk

  1. Step 1

    Define

    Author a canonical package of instructions, skills, hooks and configs, then `wl push` it to the registry as a draft package version.

  2. Step 2

    Review and promote

    The content scan runs on the new version. Once it passes, `wl publish` moves the draft to pending_review, and an approver promotes it.

  3. Step 3

    Assign

    Create a workspace for a repository that pins package versions per target and provider, then `wl bind --registry <workspaceId>` connects a working directory to it.

  4. Step 4

    Render

    `wl pull` resolves the packages, renders each provider’s files from the render plan, and writes the lock file and state file under .loom/.

The full loop, step by step →

Stop copy-pasting AI config between repos

Put your instructions, skills and hooks in one registry, pin them per repository, and let CI tell you when anything drifts.

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