Generates comprehensive operational runbooks for any system or process. Reads codebase, infrastructure config, and deployment scripts to produce structured runbook.md files formatted for on-call engineers. Use when you need operations documentation, incident response guides, deployment procedures, or disaster recovery plans.
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Generate a comprehensive operational runbook.md from a system's codebase, infrastructure config, and deployment scripts -- written for on-call engineers working incidents, deployments, and routine operations at 3am.
references/discovery-patterns.md -- Glob/Grep patterns and config files to inspect during discovery.references/output-template.md -- The full 9-section runbook.md structure to populate.references/writing-style.md -- Clarity, urgency, completeness, formatting, and accuracy rules plus output requirements.references/discovery-patterns.md (Step 1) to identify the stack and infrastructure.references/discovery-patterns.md (Step 2): IaC, containers, CI/CD, app config, deploy scripts, monitoring, migrations, load balancers, docs.references/discovery-patterns.md (Step 3) to surface health checks, metrics, caching, jobs, migrations, rollback, scaling, backups, TLS, secrets, alerting, and more.runbook.md following the structure in references/output-template.md. Cover all 9 sections; adapt content to what discovery found; never include purely speculative sections.references/writing-style.md -- copy-pasteable commands, expected outputs, sequential steps, P1-first ordering, tables for structured data, and the accuracy rules.[VERIFY] and missing sections with [ACTION REQUIRED]: ... so the runbook doubles as a gap analysis.Write runbook.md to the project root (or a directory the user specifies). It must be 500+ lines, cover all 9 sections, contain real commands derived from the codebase, include at least one architecture diagram, carry a complete table of contents, and never fabricate infrastructure details. See references/writing-style.md for the full output checklist.
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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