Skill: gechengling/agentic-workflow-designer (version 3.3.1)

--- name: Agentic Workflow Designer description: > AI-powered agentic workflow design and automation assistant �� map complex multi-step processes, identify automation opportunities, design autonomous AI agent pipelines, generate n8n/Make/Zapier workflow specs, and estimate ROI. Covers enterprise automation, self-healing workflows, human-in-the-loop patterns, and production deployment. Keywords: agentic workflow, workflow automation, n8n, Make, Zapier, enterprise automation, AI pipeline, autonomous agent, process automation, workflow design, ROI calculator, HITL, ���������, �����Զ���, �����幤����, ��ҵ�Զ���, n8n������, �����Ż�, ��������, RPA���. version: "3.3.1" --- # Agentic Workflow Designer > From messy manual processes to autonomous AI pipelines �� design, document, and deploy. ## What This Skill Does Agentic AI (AI that can autonomously execute multi-step tasks) is the #1 enterprise tech trend in 2026 with a projected $8.5B market and 40% CAGR. Yet most teams struggle to: - Map which workflows are actually suitable for agentic automation - Design reliable pipelines that don't break silently - Choose between n8n, Make, Zapier, or custom agent frameworks - Justify the ROI to business stakeholders This skill bridges the gap between AI hype and practical workflow automation: - **Workflow Discovery** �� Identify and prioritize automation opportunities in any business process - **Agentic Pipeline Design** �� Create detailed workflow blueprints with triggers, agents, tools, and fallbacks - **Platform Selection** �� Compare n8n / Make / Zapier / custom LangGraph for your use case - **Generate Workflow Specs** �� Produce JSON/YAML specs importable into n8n or Make - **ROI Calculator** �� Estimate time/cost savings from automation - **Human-in-the-Loop (HITL) Design** �� Design appropriate checkpoints for sensitive decisions ## Trigger Words Agentic workflow, automate my process, workflow automation, n8n, Make automation, Zapier flow, design a workflow, workflow design, process automation, automate with AI, AI pipeline, autonomous workflow, HITL pattern, ���������, �Զ���������, �����Զ���, �����幤����, �����������, �Զ����������, n8n������, ��ҵ�Զ���, RPA���, agentic AI pipeline ## Target Users - Operations managers digitizing manual business processes - Developers building production AI automation systems - Product managers scoping automation features - Consultants delivering workflow automation projects - Entrepreneurs building AI-native products ## Workflow ### �������ݣ�2026�棩 **Step 2 ��������������2026��**�� - LangGraph v1.0����������״̬��������/���ڼ���/����ָ����������������ҵ������֧��Kubernetes�Զ������ݣ�GitHub Starsͻ��85K - CrewAI v1.10��������Э����֧��6�ֽ�ɫ����+����������ţ�����20+��ҵ����������Slack/Notion/Airtable/GitHub����2026��Q1���������ĵ� - Claude Agent SDK / OpenAI Agents SDK����Աȣ����ߵ���׼ȷ��(94% vs 91%)/������������(78% vs 82%)/�ɱ�Ч��(��0.8/ǧToken vs ��1.2/ǧToken)����ά��ȫ������ - MCP(Model Context Protocol)��̬������50+�ٷ�����������GitHub/Slack/Notion/Postgres�ȣ���ҵ�ڲ�MCPע�����Ϊ�»�����ʩ - LLM��������֮ս��Gemini 2M Token / Claude 200K / GPT-4o 128K����ѡ��ָ�ϣ���Խ��ڳ��ĵ�(�й���/�걨)�������������Լ۱ȷ��� --- ## Step 1 �� Process Discovery Ask the user to describe their current workflow: - What triggers it? (email, schedule, webhook, human action?) - What are the key steps? (list them in plain language) - Who (or what system) does each step today? - Where do errors/delays typically occur? - What's the desired output/outcome? ### Step 2 �� Automation Suitability Assessment Score the workflow across 5 dimensions: | Dimension | Score | Notes | |-----------|-------|-------| | Repetitiveness | /10 | How often does this run identically? | | Rule-based | /10 | Are decisions clear-cut or judgment-based? | | Data availability | /10 | Is input data structured and accessible? | | Error tolerance | /10 | Can errors be caught and recovered automatically? | | Stakes | /10 (inverted) | Low-stakes = easier to automate | | **Automation Score** | /50 | >35 = High priority, 20�C35 = Medium, <20 = Keep manual | ### Step 3 �� Agentic Pipeline Design Generate a detailed pipeline blueprint: ``` ?? Workflow: [Name] ? Trigger: [webhook / cron / event / manual] ?? Agents: ������ Agent 1 [Role]: [Tool 1, Tool 2] �� Output: [description] ������ Agent 2 [Role]: [Tool 3] �� Output: [description] ������ Agent 3 [Role]: [Tool 4, Tool 5] �� Output: [description] ?? Flow: Sequential / Parallel / Conditional ?? Memory: [ephemeral / Redis / vector DB] ?? Error Handling: [retry / fallback agent / human escalation] ?? HITL Checkpoints: [list high-stakes decision points] ?? Output: [final deliverable description] ``` **Example �� Lead Qualification Pipeline:** ``` ?? Workflow: B2B Lead Qualification & Outreach ? Trigger: New form submission webhook ?? Agents: ������ Enrichment Agent [Clearbit + LinkedIn scraper] �� Company profile JSON ������ Scoring Agent [GPT-4o] �� Lead score (0�C100) + reasoning ������ Decision Gate [Human] �� Approve for outreach? (HITL) ������ Outreach Agent [Email API + CRM API] �� Personalized email + CRM update ?? Flow: Sequential with HITL gate ?? Memory: PostgreSQL (lead history) ?? Error: Retry enrichment 3x �� flag for manual review ?? HITL: Score > 80 auto-approves; 50�C80 requires human review; <50 auto-rejects ?? Output: CRM updated + email queued ``` ### Step 4 �� Platform Recommendation | Platform | Best For | Agent Support | Self-host | Price | |----------|----------|--------------|-----------|-------| | n8n | Technical teams, complex logic | ? via AI nodes | ? Yes | Free/OSS | | Make (Integromat) | Non-technical, API integrations | Partial | ? No | ~$9+/mo | | Zapier | Simple triggers, non-technical | Partial | ? No | ~$20+/mo | | LangGraph (custom) | Complex state machines, production | ? Native | ? Yes | Dev hours | | CrewAI | Role-based agent teams | ? Native | ? Yes | Dev hours | ### Step 4.5 �� 2026ƽ̨��ϸ�Աȱ�������ѡ�Ͳο��� | ά�� | n8n (v1.90) | Make (2026) | Zapier (2026) | LangGraph | CrewAI | |------|--------------|-------------|---------------|-----------|--------| | **AI�ڵ�** | ? ԭ��AI�ڵ㣨OpenAI/Claude/����LLM�� | ?? ��ͨ��HTTP�ڵ���� | ?? ��ͨ��Code�ڵ���� | ? ԭ�� | ? ԭ�� | | **���ۣ��£�** | ��ѣ�OSS��/ /�£�Cloud Pro�� | /�£�Core��~/�£�Enterprise�� | /�£�Starter��~/�£�Company�� | Dev�ɱ� | Dev�ɱ� | | **���й�** | ? Dockerһ������ | ? ��SaaS | ? ��SaaS | ? | ? | | **��ҵ������** | 400+�������ڶ���/��΢�� | 1000+��ƫ���⣩ | 6000+��ȫ����ࣩ | �Խ� | �Խ� | | **�ʺϳ���** | �����з�/�����߼�/�������� | �Ǽ���/�粿��/����ԭ�� | ����/�г�/���Զ��� | ����״̬��/������ | ��ɫЭ��/�о����� | | **���̰�** | ѧϰ���߶��� | ����SaaS������ | ����SaaS������+�� | �迪����Դ | �迪����Դ | **ѡ�ͽ��飨2026��**�� - �����Ŷ�/���ݺϹ�Ҫ�� �� **n8n���й�**�����ݲ�������֧�ֹ���LLM���룩 - ����ҵ��/�Ǽ����Ŷ� �� **Make**��1000+��������ѧϰ�ɱ��ͣ� - �򵥳���/�����Ŷ� �� **Zapier**���������ã������ڳɱ��ߣ� - ����AI����/�������� �� **LangGraph**��״̬�־û���֧��Human-in-the-Loop�� - ���ɫЭ��/�о����� �� **CrewAI**����ɫ�ֹ�������2026�������ĵ����ƣ� --- ### Step 5 �� n8n Workflow JSON Spec (Sample Output) ```json { "name": "Lead Qualification Pipeline", "nodes": [ { "name": "Webhook Trigger", "type": "n8n-nodes-base.webhook", "parameters": { "path": "lead-inbound" } }, { "name": "Enrich Lead", "type": "@n8n/n8n-nodes-langchain.agent", "parameters": { "promptType": "define", "text": "Enrich this lead data using Clearbit: {{ $json.email }}" } }, { "name": "Score Lead", "type": "@n8n/n8n-nodes-langchain.openAi", "parameters": { "resource": "text", "operation": "message", "modelId": "gpt-4o", "messages": { "values": [{ "content": "Score this lead 0-100..." }] } } } ] } ``` ### Step 6 �� ROI Calculator | Metric | Before Automation | After Automation | Savings | |--------|------------------|-----------------|---------| | Time per run | [X hours] | [Y minutes] | [Z%] | | Runs per week | [N] | [N] | �� | | Total time saved/week | �� | �� | [hours] | | Cost saved/month | �� | �� | [$$$] | | Automation setup cost | �� | �� | [one-time] | | **Payback period** | �� | �� | [weeks] | ## Example Interactions **User:** "I spend 3 hours every Monday pulling sales data from 5 spreadsheets, writing a summary email, and updating our CRM. Can this be automated?" **Skill response:** Scores the workflow (42/50 �� High priority), designs a 4-agent pipeline (data collector �� analyzer �� email writer �� CRM updater), recommends n8n as the platform (self-hostable, native AI nodes), generates a complete n8n JSON spec, and estimates 11.5 hours/month saved = ~$580 value at $50/hr. --- **User:** "I want to build a customer support triage system that reads emails, classifies them, and routes to the right team." **Skill response:** Designs a HITL-enabled pipeline with email reading, classification, confidence threshold (>85% auto-route, <85% human review), CRM ticket creation, and Slack notification. Recommends LangGraph for its state persistence and human review interrupt capability. ## Notes & Constraints - Always design **HITL checkpoints** for: financial decisions, customer communications, data deletions, external API calls with side effects - For **regulated industries** (finance, healthcare, insurance): flag compliance requirements - Workflows involving PII must include data retention and access control considerations - Recommend starting with a **pilot workflow** (lowest risk, highest frequency) before scaling - Provide rollback strategies: every agentic workflow should have a manual fallback *GitHub: https://github.com/gechengling/agentic-workflow-designer*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)