building-dashboards

building-dashboards is a skill for Claude Code, Codex from openclaw/clawhub. It costs 51 tokens per session (4,881 once invoked), scanned A, original, MIT.

A guide for designing and creating Axiom dashboards, which display queried logs and metrics as charts and filters.

In plain words
What is it for?
Use it to create dashboards, migrate Splunk or Grafana dashboards, choose chart types, write APL or metrics queries, and configure filters and layouts.
Why use it?
It helps turn operational questions into panels based on verified data, with drilldowns and percentile measurements instead of misleading averages.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to create dashboards, migrate Splunk or Grafana dashboards, choose chart…

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Install with agentmods
npx agentmods add skills/openclaw/clawhub/building-dashboards
About the project

ClawHub is a public registry where OpenClaw users publish, version, search, and install text-based agent skills and OpenClaw packages. It provides web browsing, a CLI-oriented API, moderation, vector search, and artifact hosting for code plugins, bundle plugins, and experimental whole-agent packages. The catalogue skills and agents are entries that can be discovered or used through this registry.

openclaw/clawhub · 9,393 stars · on GitHub · clawhub.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add openclaw/clawhub --skill building-dashboards
Clone the repo
git clone --depth 1 https://github.com/openclaw/clawhub

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for building-dashboards

README.md
[![agentmods](https://agentmods.dev/badge/skills/openclaw/clawhub/building-dashboards.svg)](https://agentmods.dev/skills/openclaw/clawhub/building-dashboards)
Your own site
<a href="https://agentmods.dev/skills/openclaw/clawhub/building-dashboards"><img src="https://agentmods.dev/badge/skills/openclaw/clawhub/building-dashboards.svg" alt="Measured on agentmods" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,881 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00051 $0.04881
Opus 5 $0.00026 $0.02440
Sonnet 5 $0.00010 $0.00976
Haiku 4.5 $0.00005 $0.00488

Measured 7d ago against content hash 121c66c65beb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

building-dashboards scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (tests/test-normalize.sh, tests/test-script-output.sh, tests/test-templates.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.agents/skills/building-dashboards/SKILL.md · 357 lines

How it starts

The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Building Dashboards

Philosophy

  1. Decisions first. Every panel answers a question that leads to an action.
  2. Overview → drilldown → evidence. Start broad, narrow on click/filter, end with raw logs.
  3. Rates and percentiles over averages. Averages hide problems; p95/p99 expose them.
  4. Simple beats dense. One question per panel. No chart junk.
  5. Validate with data. Never guess fields—discover schema first.
  6. Compute what's asked, or defer. If a panel can't be computed, replace it with a Note documenting the blocker. Never substitute a different quantity, even disclosed. See Compute or Defer.

Entry Points

Starting from Workflow
Vague description Intake → check dataset kind → design blueprint (APL or MPL) → queries per panel → deploy
Template Pick template → customize dataset/service/env → deploy
Splunk dashboard Extract SPL → translate via spl-to-apl → map to chart types → deploy
Grafana dashboard Project canonical panel spec (expr, legendFormat, unit, title, description) → translate PromQL → map chart types → deploy. See reference/grafana-migration.md.
Exploration Use axiom-sre to discover schema/signals → productize into panels

Intake: What to Ask First

  1. Audience & decision

    • Oncall triage? (fast refresh, error-focused)
    • Team health? (daily trends, SLO tracking)
    • Exec reporting? (weekly summaries, high-level)
  2. Scope

    • Service, environment, region, cluster, endpoint?
    • Single service or cross-service view?
  3. Dataset kind. Run scripts/metrics/datasets <deploy> and check kind.

    • otel:metrics:v1 → metrics dataset, follow the Metrics path.
    • anything else → events/logs dataset, follow the APL path.

    Never run getschema on a metrics dataset. It returns 0 rows without error.

    APL path: discover fields with ['dataset'] | where _time between (ago(1h) .. now()) | getschema. Continue to steps 4–5.

Read the full file on GitHub · 357 lines

Files

What ships with it

47 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 357 lines · 51 tokens per session scan A 121c66c65beb

Subscribe to this mod's changes

building-dashboards is a skill published in the GitHub repository openclaw/clawhub (9,393 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 4,881 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.