agentsclimarketplace

Artifact librarian

Skill ElvinMorales/agent-librarian/packages/openai/codex/.agents/skills/artifact-librarian

A framework-neutral CLI that catalogs, de-duplicates, and reviews local collections of agent skills, prompts, tool specs, and agentic AI artifacts.

Install
npx -y skills add ElvinMorales/agent-librarian --skill artifact-librarian

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Operate the local Agent Librarian workflow using the synthetic sample collection, approval-gated runtime wrapper, deterministic CLI outputs, and human-review summary.

SKILL.md

3.6 KB, as published. Nobody here has run it

Artifact Librarian

Use this skill to run a public-safe Codex demo of agent-librarian. Lead with the functional portable-agent workflow, not taxonomy.

Workflow

Codex reads AGENTS.md
-> Codex explains safe scope
-> Codex proposes runtime-wrapper commands
-> user approves exact command
-> wrapper runs deterministic CLI backend
-> Codex summarizes CLI evidence
-> human reviews generated outputs

Steps

  1. Explain the agent in plain language.

    • Codex is the interface layer.
    • The deterministic CLI and generated outputs are the source of truth.
    • The demo uses only the synthetic examples/sample-collection.

  2. Inspect only allowed public demo files.

    • Allowed source: examples/sample-collection.
    • Allowed generated catalog: examples/generated-catalog.
    • Allowed package instructions: packages/openai/codex/.
    • Do not scan private/work files, work-internal folders, credentials, secrets, private prompts, private traces, logs, memory snapshots, state snapshots, employer/client data, internal URLs, or private generated catalogs.
    • Do not use non-demo paths.

  3. Propose commands before running anything.

    • Prefer runtime-wrapper propose commands.
    • Show exact command, read scope, write scope, generated files, and sensitivity note.

  4. Require exact approval.

    • Do not run on vague approval.
    • Do not run if the approval string is different from the command shown.
    • Changed command, path, argument, sensitivity, or retry requires fresh approval.

  5. Run only approved wrapper commands.

    • Do not run arbitrary shell.
    • Do not chain commands.
    • Do not execute, edit, delete, merge, publish, or rewrite source files.
    • Do not create git commits, pushes, tags, releases, pull requests, or GitHub/yeet publishing flows.
    • Do not edit repo files during the demo unless the user explicitly asks for development work.

  6. Summarize deterministic outputs.

    • Use runtime-wrapper output, CLI output, and generated files as evidence.
    • Preserve warnings, diagnostics, validation failures, and overlap candidates.
    • Do not invent counts, files, findings, or status.

  7. Explain what the demo proves.

    • Codex can scope a synthetic public demo.
    • Codex can propose bounded wrapper commands.
    • Exact approval gates local execution.
    • The deterministic backend produces evidence for human review.

  8. Explain what the demo does not prove.

    • It does not certify safety, privacy, correctness, completeness, approval, compliance, or publication readiness.
    • It does not scan private or work-internal material.
    • It does not add OpenAI API integration, network behavior, MCP server code, arbitrary shell execution, or autonomous publication.

Allowed Demo Commands

python -m agent_librarian.runtime_wrapper propose catalog examples/sample-collection --out examples/generated-catalog
python -m agent_librarian.runtime_wrapper propose validate examples/generated-catalog
python -m agent_librarian.runtime_wrapper propose report examples/generated-catalog
python -m agent_librarian.runtime_wrapper run report examples/generated-catalog --approve-exact "agent-librarian report examples/generated-catalog"

Wrong approval demonstration:

python -m agent_librarian.runtime_wrapper run report examples/generated-catalog --approve-exact "wrong command"

The wrong approval should fail with a nonzero status and must not run the backend.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.