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ECC/skills/dashboard-builder/SKILL.md
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d29cf651c7 fix(skills): declare activation triggers in descriptions and normalize version metadata (#2618)
* fix(skills): move version into metadata and normalize to semver

29 skills declared `version` at the top level of their frontmatter. The
schema reads it from `metadata`, so tooling that follows the schema either
misses it or has to special-case the top level.

Three motion skills also declared `version: 1.0`, which is not a valid
semantic version; normalized to `1.0.0`.

No behavioral change — frontmatter metadata only.

* fix(skills): state activation triggers in skill descriptions

148 skills described what they cover but never named the situation that
should trigger them. Since the description is what Claude matches against
to decide whether to load a skill, a description without a trigger makes
activation guesswork — the skill is either missed or loaded at the wrong
time.

Added a "Use when ..." clause to each, derived from the skill's own body
(most already stated the trigger under "## When to Use" or in the opening
line; that intent is now reflected in the frontmatter where it is actually
read from).

Descriptions were only appended to; no existing wording was removed.

* fix(skills): sync activation triggers into the Codex skill mirror

10 of the skills whose descriptions changed are also mirrored under
`.agents/skills/`, where the description was previously a verbatim copy.
Left alone, the two surfaces would disagree about when the skill applies.

Only the description line is synced; the Codex copies keep their reduced
frontmatter, since that validator accepts only name, description,
metadata, license, and allowed-tools.

* fix(skills): correct three activation clauses from review

- autonomous-loops: the clause pulled new loop work into a skill that its
  own body marks as a compatibility shim retained for one release. It now
  points at the canonical continuous-agent-loop instead.
- continuous-learning: the description carried the v1 routing directive
  twice; collapsed to one.
- homelab-pihole-dns: the clause fired on any broken home DNS. Narrowed to
  tasks that actually involve Pi-hole.

* chore: retain current main lockfile

---------

Co-authored-by: Çağrı Solakoğlu <cagri.solakoglu@vtcenerji.com>
Co-authored-by: haelyra <49814733+haelyra@users.noreply.github.com>
2026-08-11 23:58:14 -04:00

2.3 KiB

name, description, metadata
name description metadata
dashboard-builder Build monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms. Use when turning metrics into a working dashboard instead of a vanity board.
version origin
1.0.0 ECC direct-port adaptation

Dashboard Builder

Use this when the task is to build a dashboard people can operate from.

The goal is not "show every metric." The goal is to answer:

  • is it healthy?
  • where is the bottleneck?
  • what changed?
  • what action should someone take?

When to Use

  • "Build a Kafka monitoring dashboard"
  • "Create a Grafana dashboard for Elasticsearch"
  • "Make a SigNoz dashboard for this service"
  • "Turn this metrics list into a real operational dashboard"

Guardrails

  • do not start from visual layout; start from operator questions
  • do not include every available metric just because it exists
  • do not mix health, throughput, and resource panels without structure
  • do not ship panels without titles, units, and sane thresholds

Workflow

1. Define the operating questions

Organize around:

  • health / availability
  • latency / performance
  • throughput / volume
  • saturation / resources
  • service-specific risk

2. Study the target platform schema

Inspect existing dashboards first:

  • JSON structure
  • query language
  • variables
  • threshold styling
  • section layout

3. Build the minimum useful board

Recommended structure:

  1. overview
  2. performance
  3. resources
  4. service-specific section

4. Cut vanity panels

Every panel should answer a real question. If it does not, remove it.

Example Panel Sets

Elasticsearch

  • cluster health
  • shard allocation
  • search latency
  • indexing rate
  • JVM heap / GC

Kafka

  • broker count
  • under-replicated partitions
  • messages in / out
  • consumer lag
  • disk and network pressure

API gateway / ingress

  • request rate
  • p50 / p95 / p99 latency
  • error rate
  • upstream health
  • active connections

Quality Checklist

  • valid dashboard JSON
  • clear section grouping
  • titles and units are present
  • thresholds/status colors are meaningful
  • variables exist for common filters
  • default time range and refresh are sensible
  • no vanity panels with no operator value
  • research-ops
  • backend-patterns
  • terminal-ops