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How to gate skill updates in CI so only new findings fail with Skill Spector

Accept known findings into a baseline and re-scan so each update surfaces only new issues.

Short answer: Install Skill Spector as above; then Accept current findings once with skillspector baseline ./my-skill/ -o .skillspector-baseline.yaml and commit it; on each change run skillspector scan ./my-skill/ --baseline .skillspector-baseline.yaml. All steps.

Who it is forA platform team maintaining an internal skills library that changes every week.

The problemRe-scanning the same accepted findings every time buries the new, real problems.

The outcomeA committed baseline file and scans that report and score only new findings.

What you need first

  • Python with uv or pip, or Docker (no Python required)
  • Optional: an LLM API key in .env for semantic analysis (default config targets DeepSeek)
  • Optional MCP extra for skillspector mcp

Steps

  1. Install Skill Spector as above
  2. Accept current findings once with skillspector baseline ./my-skill/ -o .skillspector-baseline.yaml and commit it
  3. On each change run skillspector scan ./my-skill/ --baseline .skillspector-baseline.yaml
  4. Review suppressed findings occasionally with --show-suppressed
  5. Exclude test fixtures with --exclude patterns where needed

Prompt for your agent

Set up NVIDIA/SkillSpector for <skills folder>: create a baseline of the current findings, commit it, and add a CI step that scans against the baseline and fails on any new high-severity finding.

Caveat.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for NVIDIA/SkillSpector and "Gate skill updates in CI so only new findings fail"

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claude mcp add --transport http deduped https://dedupedai.com/mcp

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