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claude-dashboards

Skill7 starsChanged 13 days agoMITView on GitHub

Build and verify Claude Dashboards with source queries, metric definitions, freshness, filters, partial failures and safe sharing. Distinguishes Anthropic's dashboard artifacts from custom dashboard libraries.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep secrets, raw personal data and tenant identifiers out of client code.

    line 18
  2. Very specific:

    Verify empty/partial/error states, keyboard controls, narrow widths, unauthorized records, stale responses after filter changes and refresh …

    line 25

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Claude Dashboards

Claude Dashboards is Anthropic's artifact product, not a chart package. As checked 2026-11-01 it is in beta on paid plans; organizational controls can restrict access. Use the existing authenticated artifact/connector capabilities when present. Do not enroll, grant data access, change admin settings or invent a tool. If unavailable, say so and build a custom dashboard only when requested.

  1. Define the decision, audience, source, metric, time zone, interval, denominator, null handling and permission boundary. Ask only missing details that materially change the query.
  2. Inspect source schema and permission scope. Run bounded read-only queries. Keep secrets, raw personal data and tenant identifiers out of client code.
  3. Show the query behind each metric, unit and last successful refresh time. Distinguish zero, unknown, loading, stale and failed. Keep a failed panel's old data labeled stale; one failed source must not pretend the whole refresh succeeded.
  4. Validate filters against the same metric definitions, compare totals to the source and provide a readable data table. Never invent growth or trends.
  5. Verify empty/partial/error states, keyboard controls, narrow widths, unauthorized records, stale responses after filter changes and refresh races. Sharing is a separate explicit action; inspect recipients before publishing.

For custom UI, read the main UFS references/workflows.md. Functional check first, adversarial check second. Document any live connector or sharing check that could not run.

Sources: Anthropic guide, release announcement.

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