agentleFS
OpenAIKnown publisheropenai/community-plugins/plugins/grafana-observability/skills/analyze-log-signals/SKILL.md

analyze-log-signals

Skill18 starsChanged 2 months agoApache-2.0View on GitHub

Analyze reviewed Grafana log evidence for event patterns, error-rate changes, and enrolled security markers. Use when a user needs bounded log analytics for an incident window, error-log comparison, anomaly triage, operational debugging, or a cautious check of approved security signals.

The file

We split this skill into the rules it gives an agent

12 rules. About 0 are common, 4 are distinctive, and 8 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 8

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run the fixed grafana.logs pack and analyze only the normalized, redacted events and aggregates it returns.

    line 8
  2. Very specific:

    Use only grafana_profile_status, grafana_list_packs, grafana_run_pack, grafana_get_evidence, and grafana_resolve_link.

    line 12
  3. Distinctive:

    Never accept or construct LogQL, PromQL, SQL, regex searches, URLs, headers, datasource IDs, panel JSON, variables, or raw provider …

    line 14
  4. Distinctive:

    Do not request broader logs to compensate for an empty result.

    line 15
  5. Distinctive:

    Keep logs, Grafana, alerts, datasources, and runtime state read-only.

    line 16
  6. Very specific:

    Call grafana_profile_status({profileAlias}).

    line 22
  7. Very specific:

    Call grafana_list_packs({profileAlias}) and require the exact active pack ID grafana.logs.

    line 23
  8. Distinctive:

    Create one non-secret idempotency key and reuse it only for an exact retry.

    line 24
  9. Very specific:

    Call grafana_run_pack with that key, the aliases, packId: "grafana.logs", origin, current window, and optional baseline window.

    line 25
  10. Very specific:

    Call grafana_get_evidence({reportRef,evidenceRef}) only for report-owned references needed to verify a pattern or issue.

    line 27
  11. Very specific:

    Call grafana_resolve_link({reportRef,linkRef}) only for a same-report link reference that helps a human inspect the enrolled Grafana view.

    line 28
  12. Very specific:

    Use anomaly, regression, or security-event language only when a returned issue has classification: SIGNED_RULE and its returned kind and …

    line 30

≈ “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

Analyze Log Signals

Run the fixed grafana.logs pack and analyze only the normalized, redacted events and aggregates it returns.

Safety contract

  • Use only grafana_profile_status, grafana_list_packs, grafana_run_pack, grafana_get_evidence, and grafana_resolve_link.
  • Accept only profile and scope aliases; one declared LIVE, REPLAY, or SIMULATED origin; bounded current and baseline windows; and opaque references returned by these tools.
  • Never accept or construct LogQL, PromQL, SQL, regex searches, URLs, headers, datasource IDs, panel JSON, variables, or raw provider requests.
  • Do not request broader logs to compensate for an empty result. Scope expansion requires a separately reviewed server-side binding.
  • Keep logs, Grafana, alerts, datasources, and runtime state read-only. Do not reveal or reconstruct redacted content.
  • Confirm LIVE access and keep LIVE, REPLAY, and SIMULATED conclusions separate.

Workflow

  1. Establish profileAlias, scopeAlias, origin, and a current window no longer than seven days. Use an equal-duration, non-overlapping baseline when evaluating a change in error logs.
  2. Call grafana_profile_status({profileAlias}). Stop on ERROR or blocked readiness; disclose PARTIAL readiness.
  3. Call grafana_list_packs({profileAlias}) and require the exact active pack ID grafana.logs.
  4. Create one non-secret idempotency key and reuse it only for an exact retry.
  5. Call grafana_run_pack with that key, the aliases, packId: "grafana.logs", origin, current window, and optional baseline window.
  6. Review log event volume, log error rate, and any returned enrolled security-marker signal. Preserve event times, summaries, units, severity, direction, and ordering.
  7. Call grafana_get_evidence({reportRef,evidenceRef}) only for report-owned references needed to verify a pattern or issue. Summarize bounded patterns; do not reproduce sensitive event content unnecessarily.
  8. Call grafana_resolve_link({reportRef,linkRef}) only for a same-report link reference that helps a human inspect the enrolled Grafana view. Never accept a URL as input.

Use anomaly, regression, or security-event language only when a returned issue has classification: SIGNED_RULE and its returned kind and evidence support that label. A marker is not proof of exploitation, attribution, or impact. Treat classification: DEVIATION only as a deviation and state the uncertainty.

Interpret report status exactly

  • COMPLETE: expected log evidence completed; assess its returned signals rather than assuming there were no problems.
  • EMPTY: no usable events were returned for the approved scope and window; this is not proof that no events occurred.
  • STALE: events are too old for a current claim; report their observed and collected times.
  • PARTIAL: an expected signal, enrolled security marker, or baseline is missing, failed, or mixed; identify the gap.
  • CONTRADICTORY: evidence conflicts; cite both sides and do not collapse them into one narrative.
  • ERROR: collection failed or was rejected; report the sanitized error and stop.

Completion format

Return scope, origin, windows, overall status, event and error-rate summary with returned severity and direction, issues and security markers with their returned classification, gaps or contradictions, evidence references for every material claim, and only successfully resolved report-owned links.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.