BB observability-aiops
Use this skill whenever the user needs to operate a self-hosted observability stack on Prometheus (HTTP API + PromQL), Alertmanager, Grafana, or Grafana Loki (logs) — a one-shot overview, PromQL instant/range queries, label + series metadata, scrape-target health (up/down + why) and dropped targets, recording/alerting rule health, firing/pending alerts, Alertmanager alerts + silences, Grafana dashboards/datasources/folders, bounded Loki LogQL log reads (labels, query, error-tail), five flagship analyses (firing-alert RCA, target-scrape-health, alert-noise/flap, log-error-burst RCA, log-volume/cardinality) plus an alert->log cross-signal, and guarded writes (create/expire silence, create annotation, update/delete dashboard, reload Prometheus config). Always use this skill for "Prometheus", "PromQL", "Alertmanager", "Grafana", "Loki", "LogQL", "logs", "which targets are down", "scrape failing", "why is this alert firing", "root cause this alert", "firing alerts", "silence this alert", "noisy alerts", "alert flapping", "recording rule", "alerting rule", "dashboard", "datasource health", "reload prometheus config", "TSDB cardinality", "error burst", "log volume", "log cardinality", "tail errors" when the context is a self-hosted metrics/logs/observability stack. Do NOT use when the target is something other than a Prometheus/Grafana observability stack (a hypervisor, storage appliance, backup product, container-orchestrator control plane, network device config, or OT/industrial equipment) — route those to the appropriate other AIops-tools skill. Hosted/SaaS monitoring suites (Datadog, New Relic, enterprise NMS) are out of scope. Governed observability operations with a built-in governance harness (audit, policy, token budget, undo, risk-tiers). Beyond the mock suite, the Prometheus/Alertmanager/Grafana surfaces have been exercised against a live Prometheus 3.x + Alertmanager + Grafana 13 stack (RCAs, governed writes, undo); the Loki surface has not (see docs/VERIFICATIO
Use this skill whenever the user needs to operate a self-hosted observability stack on Prometheus (HTTP API + PromQL), Alertmanager, Grafana, or Grafana Loki…
As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Shorten the description to 1024 characters.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
✓ No critical or high findings
Medium and low: 1
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medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Bash
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 2007 chars, limit 1024 - note
description-budgetdescription takes 2007 of the ~15000-char shared budget for all skills - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "installer"
Process rating: all ten parameters 68/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 20 mutating operations with no state check
- 55Failures and branches. 1 branches
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 49 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3487 tokens
- 100Progress reporting. Reports progress
- low The response is described with custom markup (11 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +3Description length 2006: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 22 example trigger phrases
- +4Description says when NOT to use the skill
- +4Structure: 13 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.