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SKILL verified MIT Self-run

Create Honeycomb Board

skill-honeycombio-agent-skill-create-honeycomb-board · by honeycombio

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Install

$ agentstack add skill-honeycombio-agent-skill-create-honeycomb-board

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Create a Honeycomb Board

Build a board (dashboard) in Honeycomb using the create_board MCP tool. There is no update tool — define it well before creating.

When building a board, think about the purpose and time frame involved. Some examples:

  • a board for a service. This should be timeless, looking at the service's health, performance, and business metrics. Do not do any problem diagnosis or investigation when building this board. Do not express opinions or summarize graphs in text panels. The board should be a representation of the service's health at whatever moment someone looks at it.
  • a board for a feature. This should look at the feature's usage trends, and its impact on the business. This board would have a time frame of 7 days, and would not include any infrastructure metrics or service dependencies.
  • a board for a problem. This might be created during an incident, or afterward. This one would have a time frame specific to the incident. It would include investigations, and your opinions about what is happening. Patterns of what to look for are appropriate here.

Workflow

Gather SLOs

Use get_slos to list SLOs in the environment. Relevant SLOs will go on the board as slo panels.

Gather descriptive context

Look at the code and docs (using Read/Grep/Glob) to understand the service or feature. Use this to write a text panel. Link to GitHub or documentation if you can.

This will vary greatly depending on the purpose of the board.

Description of the application and link to the code - great for a service board.

What is the feature, and what business impact does it have? - great for a feature board.

What is the problem, and what is the impact? What patterns do we see? - great for a problem board.

Build candidate queries

Get Honeycomb context: Call get_workspace_context for available environments. Use get_environment to find datasets — each dataset corresponds to an OTEL_SERVICE_NAME.

Code context: Look at the language and any custom attributes (often prefixed app.). Custom fields are prime candidates for breakdowns and business metrics.

Discover columns: Use find_columns or get_dataset_columns. Pay special attention to non-standard columns — those are specific to the application.

Find queries: Use find_queries to see what people have already queried. Check get_triggers for what they alert on — those indicate what matters.

Time range: Default to 2 hours. Use 8–24 hours for lower-volume services. Use 7 days for feature usage boards. Keep it consistent across all panels.

Aim for 6–12 graphs. Stat panels are bonus — they don't count against this limit.

List your candidate queries for the user with reasons before running them. See ${CLAUDE_PLUGIN_ROOT}/skills/create-honeycomb-board/references/board-queries.md for what to include and how to write each kind.

Run and check queries

Use run_query for each candidate query. Each result returns a query run PK (like QR-abc123) — you'll use this as the panel id when building the board.

Fix errors. Eliminate queries that return no interesting results.

Plan the layout

Think about visual flow and how to make the board expressive. The board uses a 12-column grid — stat panels can sit side-by-side, a heatmap deserves full width, breakdowns benefit from extra height. See ${CLAUDE_PLUGIN_ROOT}/skills/create-honeycomb-board/references/board-layout.md for sizing examples.

Consider preset_filters if viewers will want to slice the board interactively (by route, region, account tier, etc.).

Show the proposed board to the user — always, without exception

For each panel, display:

  • Text panels: Show the full markdown content that will appear on the board. The user needs to review the exact wording before creation since boards can't be updated.
  • SLO panels: Show the SLO name, target, and current compliance.
  • Query panels: Show the name, description, chart type, display style, and a link to the query (the query_url from the run_query result metadata). Briefly describe what the results showed.
  • Planned layout (sizing and groupings)
  • Tags: Display the tags you plan to add to the board.
  • Any preset filters

End with: "Here's the board I'd create. Shall I go ahead?"

This step is non-negotiable. If the user says "just create it", "I trust you", or "skip the preview" — show the plan anyway. The user cannot meaningfully confirm something they haven't seen. There is no way to update a board after creation; the only fix is to delete it and start over. Showing the plan first protects them even when they think they don't need it.

The one exception: if the user has already reviewed and approved a specific plan in this conversation, you may proceed.

Create the board

Call create_board with a panels array. Every panel requires a type field — "query", "slo", or "text" — that determines what other fields apply. See ${CLAUDE_PLUGIN_ROOT}/skills/create-honeycomb-board/references/board-layout.md for the full field reference.

{
  "environment_slug": "production",
  "name": "Checkout Service",
  "description": "...",
  "panels": [
    {
      "type": "text",
      "content": "## Checkout Service\nOwned by Platform team. [Source](https://github.com/...)"
    },
    {
      "type": "slo",
      "id": "SLO-abc123",
      "size": { "width": 4 }
    },
    {
      "type": "query",
      "id": "QR-abc123",
      "name": "Request Rate",
      "chart_type": "stat",
      "display_style": "chart",
      "size": { "width": 4 }
    },
    {
      "type": "query",
      "id": "QR-def456",
      "name": "Error Rate",
      "chart_type": "stat",
      "display_style": "chart",
      "size": { "width": 4 }
    },
    {
      "type": "query",
      "id": "QR-ghi789",
      "name": "Latency Distribution",
      "description": "Overall request latency as a heatmap",
      "chart_type": "default",
      "display_style": "chart",
      "size": { "width": 12, "height": 3 }
    }
  ],
  "preset_filters": [{ "column": "http.route", "alias": "Route" }],
  "tags": ["team:platform", "tier:critical"]
}

Follow up

Link the user to the board.

Cross-References

  • For query construction patterns and calculated fields: query-patterns skill
  • For SLO interpretation and burn alert design: slos-and-triggers skill

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.