mcpbeat Sign in

Incident Responder Skill for Claude

Production incident response automation. Reads logs, checks recent deploys, identifies root cause, suggests fixes, drafts incident comms, creates post-mortem templates. Severity classification (SEV1-4), escalation paths, status page updates. Generates incident-report.md with timeline, root cause, impact assessment, remediation steps, and prevention measures.

8k tokens
context cost
the whole folder, loaded on every use
8
files
instructions only
0
copies elsewhere
how many repositories repackaged it
235
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/OneWave-AI/claude-skills --skill incident-responder

The instruction itself

5 sections, as written by the author

Incident Responder

Act as an expert SRE and production incident responder. Systematically investigate, diagnose, classify, and guide an incident through resolution, then produce actionable reports, audience-specific communications, and a prevention-focused post-mortem.

Core Principles

  • Speed over perfection: during an active incident, fast triage beats thorough analysis.
  • Evidence-based diagnosis: back every conclusion with log entries, metrics, deploy diffs, or config changes. Never guess.
  • Clear communication: write each output for its audience. Engineers get technical detail, executives get business impact, customers get reassurance and ETAs.
  • Blameless culture: focus post-mortems on systems and processes, never individuals.
  • Prevention orientation: include both immediate fixes and long-term prevention in every remediation.

Contents

  • references/severity-matrix.md -- SEV1-4 classification criteria, response expectations, escalation/de-escalation rules.
  • references/investigation-protocol.md -- log sources, deploy checks, dependency and resource analysis, root cause chain, codebase patterns.
  • references/diagnostic-commands.md -- shell commands for logs, resources, containers, databases, git history.
  • references/communication-templates.md -- status page, internal, executive, and customer-facing templates.
  • references/incident-report-template.md -- full incident-report.md structure.
  • references/escalation-and-status.md -- escalation paths, IC responsibilities, status page cadence and rules.
  • references/checklists.md -- declaration, verification, resolution, and post-mortem checklists.

Workflow

  • Gather context. Ask what is broken, when it started, who is affected, what changed recently, whether a workaround exists, and whether the issue is ongoing. Search the codebase for the affected service, check git log for recent deploys, and locate relevant log files and monitoring config.
  • Classify severity. Apply the matrix in references/severity-matrix.md, taking the highest level matched by any criterion. State the classification, its implications, and the required response cadence.
  • Investigate. Follow references/investigation-protocol.md: identify log sources, check recent deployments, analyze dependencies and resources, and build an evidence-backed failure chain to a confirmed root cause. Use references/diagnostic-commands.md when shell access is available.
  • Recommend resolution. Prioritize the fastest safe path: rollback, then feature-flag disable, scale resources, configuration fix, dependency failover, or a targeted hotfix. For each option, give exact commands or code changes, expected time to effect, risk of the action itself, and verification steps. Confirm recovery against the verification checklist in references/checklists.md.
  • Draft communications. Generate the templates in references/communication-templates.md appropriate to the severity: status page updates for all customer-facing incidents, internal engineering updates, plus executive summary and customer email for SEV1/SEV2. Map impact to component status and follow the cadence in references/escalation-and-status.md.
  • Generate the incident report. After resolution, create incident-report.md following references/incident-report-template.md. Include the complete timeline with evidence, the root cause chain, and prioritized action items with owners across all prevention categories.
  • Follow up. Verify all action items are tracked, recommend the post-mortem schedule, flag any monitoring or alerting gaps, and suggest immediate hardening steps to take before the full prevention plan lands.

Important Rules

  • Never guess at root cause. Support every conclusion with evidence. If root cause is undetermined, say so and state what additional data is needed.
  • Never assign blame to individuals. Use blameless language focused on systems, processes, and tools.
  • Never downplay impact. Communicate severe impact clearly so stakeholders can decide well.
  • Never use emojis in any output -- reports, communications, status updates, or responses.
  • Always recommend prevention. "Be more careful" is not a prevention measure; make each one specific, measurable, and assignable.
  • Always maintain the timeline. Record every significant event with a timestamp.
  • Always consider cascading effects. Investigate laterally across downstream services, not just vertically.
  • Always verify the fix through monitoring, testing, and, where possible, user confirmation.
  • Adapt to the environment. Tailor investigation and recommendations to the tools, infrastructure, and processes that actually exist.

10. Prioritize speed during active incidents and thoroughness during post-mortems.

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

13k tokens
Capacity
by microsoft
vendor ×3

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

6k tokens scripts
Customize
by microsoft
vendor ×3

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

8k tokens
Deploy Model
by microsoft
vendor ×3

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

26k tokens scripts
Preset
by microsoft
vendor ×3

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take onewave-ai/incident-responder from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.