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Get Started Free →Production incident response. Use when handling incidents, classifying severity, reconstructing timelines, writing postmortems, generating comms templates, or building response playbooks with severity scoring and RCA frameworks.
.claude/skills/borghei-incident-commander/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 89% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 141% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 96% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 43% | 0% |
Classify incident severity, reconstruct timelines from heterogeneous event sources, and generate structured post-incident reviews with root cause analysis and action items. Codifies PagerDuty, Google SRE, and Atlassian incident-management practices into severity scoring, escalation matrices, communication templates, RCA frameworks, and SLA/error-budget tracking.
Before producing the artifact, confirm these inputs. If any is unknown or vague, ASK — do not assume:
severity_classifier.py vs timeline_reconstructor.py vs pir_generator.py/postmortem_generator.py)pir_generator.py --rca-method and the postmortem structure)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command | |------|---------|---------| | incident_classifier.py | Classify severity, recommend response teams and comms templates | python scripts/incident_classifier.py --input incident.json --format text | | severity_classifier.py | Multi-dimensional severity score with escalation path | python scripts/severity_classifier.py incident.json --format markdown | | timeline_reconstructor.py | Reconstruct timeline from timestamped events with phase + gap analysis | python scripts/timeline_reconstructor.py --input events.json --detect-phases --gap-analysis --format markdown | | incident_timeline_builder.py | Build structured timeline with MTTD/MTTR and comms templates | python scripts/incident_timeline_builder.py incident_data.json --format markdown | | pir_generator.py | Generate Post-Incident Review with RCA and action items | python scripts/pir_generator.py --incident incident.json --rca-method fishbone --action-items | | postmortem_generator.py | Generate postmortem with 5-Whys, benchmarks, coverage gaps | python scripts/postmortem_generator.py incident_data.json --format markdown |
Load the reference that matches the task — keep this file lean and pull detail on demand:
Covers: severity classification, timeline reconstruction, PIR/postmortem generation, RCA frameworks, escalation matrices, communication templates, and SLA/error-budget tracking. Tools are deterministic stdlib Python (no ML/LLM calls), accepting JSON input and emitting text/JSON/markdown.
Does NOT cover: live monitoring/alerting infrastructure (feeds in from senior-devops), security forensics (see senior-secops), or deployment/rollback execution (see release-orchestrator).
| Skill | Integration | |-------|-------------| | senior-devops | Monitoring alerts feed timeline; runbook templates inform playbooks | | senior-secops | Security incidents auto-escalate to SEV-1; breach indicators trigger SecOps response | | release-orchestrator | Deployment events feed timeline; rollback data informs release gates | | senior-architect | Architectural root causes escalate to architecture review | | code-reviewer | PIR action items route to code review workflows |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | fail→pass | 16,702 | 18,848 | +13% | 1 | 1 | 0% | 2,301 | 4,344 | +89% | 0 | 0 | — |
case-17 | fail→pass | 6,691 | 10,356 | +55% | 1 | 1 | 0% | 1,030 | 2,485 | +141% | 0 | 0 | — |
case-01 | fail→fail | 10,743 | 22,405 | +109% | 1 | 1 | 0% | 1,576 | 5,068 | +222% | 0 | 0 | — |
case-02 | fail→fail | 30,195 | 32,696 | +8% | 1 | 1 | 0% | 4,679 | 6,768 | +45% | 0 | 0 | — |
case-03 | fail→pass | 20,473 | 16,220 | -21% | 1 | 1 | 0% | 3,215 | 3,978 | +24% | 0 | 0 | — |
case-04 | fail→fail | 14,518 | 19,952 | +37% | 1 | 1 | 0% | 2,639 | 5,163 | +96% | 0 | 0 | — |
case-05 | fail→fail | 5,506 | 13,324 | +142% | 1 | 1 | 0% | 848 | 3,557 | +319% | 0 | 0 | — |
case-07 | fail→pass | 7,202 | 3,444 | -52% | 1 | 1 | 0% | 1,063 | 2,080 | +96% | 0 | 0 | — |
case-08 | fail→pass | 9,499 | 2,585 | -73% | 1 | 1 | 0% | 1,346 | 1,922 | +43% | 0 | 0 | — |
case-09 | fail→pass | 8,735 | 3,542 | -59% | 1 | 1 | 0% | 1,346 | 2,052 | +52% | 0 | 0 | — |
case-10 | fail→pass | 13,392 | 2,876 | -79% | 1 | 1 | 0% | 2,439 | 1,988 | -18% | 0 | 0 | — |
case-11 | fail→pass | 11,434 | 3,161 | -72% | 1 | 1 | 0% | 1,877 | 2,051 | +9% | 0 | 0 | — |
case-12 | pass→pass | 16,791 | 17,243 | +3% | 1 | 1 | 0% | 2,516 | 4,171 | +66% | 0 | 0 | — |
case-13 | fail→pass | 13,247 | 14,111 | +7% | 1 | 1 | 0% | 1,938 | 3,562 | +84% | 0 | 0 | — |
case-14 | pass→pass | 14,757 | 13,721 | -7% | 1 | 1 | 0% | 2,364 | 3,688 | +56% | 0 | 0 | — |
case-15 | pass→pass | 14,358 | 20,927 | +46% | 1 | 1 | 0% | 2,366 | 5,169 | +118% | 0 | 0 | — |
case-16 | pass→pass | 10,245 | 12,293 | +20% | 1 | 1 | 0% | 1,535 | 3,233 | +111% | 0 | 0 | — |
case-18 | pass→pass | 16,260 | 18,466 | +14% | 1 | 1 | 0% | 2,572 | 4,450 | +73% | 0 | 0 | — |
case-19 | pass→pass | 7,705 | 7,976 | +4% | 1 | 1 | 0% | 1,108 | 2,597 | +134% | 0 | 0 | — |
case-20 | pass→pass | 6,194 | 8,226 | +33% | 1 | 1 | 0% | 924 | 2,720 | +194% | 0 | 0 | — |
case-21 | pass→pass | 11,508 | 13,568 | +18% | 1 | 1 | 0% | 1,712 | 3,564 | +108% | 0 | 0 | — |
case-22 | pass→pass | 14,836 | 14,697 | -1% | 1 | 1 | 0% | 2,277 | 3,693 | +62% | 0 | 0 | — |
case-23 | pass→pass | 8,253 | 12,323 | +49% | 1 | 1 | 0% | 1,218 | 3,182 | +161% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +39 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.