This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
日本語の概要は準備中です。原文の説明を表示しています。
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
日本語の概要は準備中です。原文の説明を表示しています。
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
日本語の概要は準備中です。原文の説明を表示しています。