workflow-automator
$
npx mdskill add OneWave-AI/claude-skills/workflow-automatorTransform a manual business workflow into an optimized automated system: map how work gets done today, then design a complete automated replacement with triggers, conditions, actions, branching, and error handling. Deliver a comprehensive `workflow-automation.md`.
SKILL.md
.github/skills/workflow-automatorView on GitHub ↗
--- name: workflow-automator description: Takes a manual business workflow description and designs the automated version. Maps current steps, handoffs, decision points, and bottlenecks. Designs automated flow with triggers, conditions, actions, and error handling. Outputs workflow-automation.md with before/after Mermaid diagrams, tool recommendations, implementation steps, and time savings estimate. tools: Read, Write, Glob, Grep, Bash, WebSearch, WebFetch model: inherit --- # Workflow Automator Transform a manual business workflow into an optimized automated system: map how work gets done today, then design a complete automated replacement with triggers, conditions, actions, branching, and error handling. Deliver a comprehensive `workflow-automation.md`. ## Contents - `references/intake.md` -- What to gather and how to ask for missing detail - `references/analysis-framework.md` -- Current-state mapping, pain-point scoring, decision and handoff analysis - `references/automation-design.md` -- Triggers, actions, branching, parallelism, error handling, human-in-the-loop templates - `references/tool-recommendations.md` -- Platform decision matrix and when to recommend each tool - `references/output-template.md` -- Full `workflow-automation.md` structure to fill in - `references/diagram-and-estimation-standards.md` -- Mermaid conventions and time/ROI estimation rules ## Workflow 1. **Intake.** Gather a complete description of the manual workflow: who, what, when, where, how long, what fails, how often, what volume. When the description is brief or partial, ask all clarifying questions in one organized message, then proceed. See `references/intake.md`. 2. **Map the current state.** Document every step, actor, handoff, decision point, wait time, and failure mode in a structured table; classify each step. See `references/analysis-framework.md`. 3. **Identify pain points.** Score each step on automation potential, impact, and risk. Surface bottlenecks, redundant steps, error-prone handoffs, and wasted time. See `references/analysis-framework.md`. 4. **Design the automated flow.** Define triggers, action specs, decision gates, parallel blocks, human checkpoints, and three-level error handling. See `references/automation-design.md`. 5. **Recommend tools.** Evaluate Zapier, Make, n8n, custom code, and Power Automate against this workflow; recommend a primary platform and any hybrid architecture. See `references/tool-recommendations.md`. 6. **Estimate impact.** Calculate conservative time savings, error reduction, throughput gain, cost, and ROI period. See `references/diagram-and-estimation-standards.md`. 7. **Deliver.** Generate the full `workflow-automation.md` in the current working directory, including both before and after Mermaid diagrams and the time-savings table. See `references/output-template.md` and `references/diagram-and-estimation-standards.md`. ## Output Rules - Always generate the full document, not a summary or abbreviated version. Make it self-contained enough to implement from alone. - Always include both Mermaid diagrams (current state and automated state) and the quantified time-savings table. - Use no emojis anywhere in the output. ## Quality Checklist Before delivering, verify: - [ ] Every manual step is mapped - [ ] Every decision point has explicit logic - [ ] Every handoff is analyzed - [ ] The automated flow handles all identified failure modes - [ ] Error handling exists at step, flow, and system levels - [ ] Human-in-the-loop checkpoints exist for high-risk decisions - [ ] Tool recommendations are justified with specific criteria - [ ] Time savings estimates are conservative and show the math - [ ] Cost analysis includes all ongoing costs - [ ] Implementation is phased with quick wins first - [ ] Both Mermaid diagrams render correctly - [ ] The document is self-contained and actionable - [ ] No emojis are used anywhere in the output
More from OneWave-AI/claude-skills
- accessibility-auditorAudit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
- agent-armyDeploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. 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.
- agent-swarm-deployerDeploys 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.
- agent-team-builderDesigns 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.
- agent-to-agentAgent-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.
- ai-readiness-assessmentAssesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.
- animateGenerate animated videos and motion graphics from natural language descriptions. Creates a standalone Vite + React project with Framer Motion scenes that auto-play in the browser. Use when the user wants to create animations, motion graphics, video intros, animated presentations, or product demos.
- api-documentation-writerGenerate comprehensive API documentation including endpoint descriptions, request/response examples, authentication guides, error codes, and SDKs. Creates OpenAPI/Swagger specs, REST API docs, and developer-friendly reference materials. Use when users need to document APIs, create technical references, or write developer documentation.
- api-endpoint-scaffolderGenerate REST API endpoints with proper structure, validation, error handling, and types. Use when creating new API routes, endpoints, or backend services.
- api-load-testerLoad tests API endpoints with progressive concurrency. Measures response times, error rates, throughput, and identifies breaking points. Generates a detailed report with latency percentiles, throughput curves, bottleneck analysis, and optimization recommendations.