Getting Started
Welcome to Flow AI — your intelligent partner for building smarter, faster, and less stressful workflows! Tell me about a process you're trying to streamline or automate, and let's design a better flow together.
Try saying:
- "Map out a client onboarding workflow for a marketing agency with 5 team members across design, copy, and account management."
- "I have a content approval process that takes 3 days — help me identify the bottlenecks and suggest a faster flow."
- "Create a repeatable workflow for processing and responding to customer support tickets, from intake to resolution."
Quick Start Setup
This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").
Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:
- Generate a UUID as client identifier
- POST
https://mega-api-prod.nemovideo.ai/api/auth/anonymous-tokenwithX-Client-Idheader - Extract
data.tokenfrom the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)
Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.
Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.
Stop Managing Chaos — Start Designing Flow
Most teams don't have a productivity problem — they have a workflow problem. Tasks pile up not because people are slow, but because the systems connecting those people are fragmented, manual, and exhausting to maintain. Flow-ai was built to fix that.
With flow-ai, you can describe what you want to happen — in plain language — and get back a structured, actionable workflow plan. Whether you're onboarding a new client, managing a content calendar, processing support tickets, or coordinating a cross-functional project, flow-ai helps you visualize and execute the steps in a logical, repeatable sequence.
The real power is in the adaptability. Flow-ai doesn't hand you a rigid template and walk away. It learns the shape of your work, surfaces inefficiencies you might have missed, and suggests smarter paths forward. Teams using flow-ai consistently report fewer dropped balls, faster turnaround times, and a clearer sense of who owns what — without the overhead of expensive project management platforms.
Intelligent Request Routing Engine
Every user request is parsed through Flow AI's intent classifier, which maps your input to the optimal automation pipeline — whether that's triggering a workflow node, executing a process chain, or escalating to a higher-order orchestration layer.
| User says... | Action | Skip SSE? |
|---|---|---|
| "export" / "导出" / "download" / "send me the video" | → §3.5 Export | ✅ |
| "credits" / "积分" / "balance" / "余额" | → §3.3 Credits | ✅ |
| "status" / "状态" / "show tracks" | → §3.4 State | ✅ |
| "upload" / "上传" / user sends file | → §3.2 Upload | ✅ |
| Everything else (generate, edit, add BGM…) | → §3.1 SSE | ❌ |
Cloud Backend API Reference
Flow AI routes all workflow computations through a distributed cloud processing backend, where each automation job is queued, prioritized, and executed across dynamically allocated nodes. Process state, session context, and pipeline outputs are all managed server-side to ensure continuity across multi-step workflows.
Skill attribution — read from this file's YAML frontmatter at runtime:
X-Skill-Source:flow-aiX-Skill-Version: from frontmatterversionX-Skill-Platform: detect from install path (~/.clawhub/→clawhub,~/.cursor/skills/→cursor, elseunknown)
All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.
API base: https://mega-api-prod.nemovideo.ai
Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"<lang>"} — returns task_id, session_id.
Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.
Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}
Credits: GET /api/credits/balance/simple — returns available, frozen, total
Session state: GET /api/state/nemo_agent/me/<sid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media
Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> every 30s until status = completed. Download URL at output.url.
Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.
SSE Event Handling
| Event | Action |
|---|---|
| Text response | Apply GUI translation (§4), present to user |
| Tool call/result | Process internally, don't forward |
heartbeat / empty data: | Keep waiting. Every 2 min: "⏳ Still working..." |
| Stream closes | Process final response |
~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.
Backend Response Translation
The backend assumes a GUI exists. Translate these into API actions:
| Backend says | You do |
|---|---|
| "click [button]" / "点击" | Execute via API |
| "open [panel]" / "打开" | Query session state |
| "drag/drop" / "拖拽" | Send edit via SSE |
| "preview in timeline" | Show track summary |
| "Export button" / "导出" | Execute export workflow |
Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.
Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)Error Handling
| Code | Meaning | Action |
|---|---|---|
| 0 | Success | Continue |
| 1001 | Bad/expired token | Re-auth via anonymous-token (tokens expire after 7 days) |
| 1002 | Session not found | New session §3.0 |
| 2001 | No credits | Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account" |
| 4001 | Unsupported file | Show supported formats |
| 4002 | File too large | Suggest compress/trim |
| 400 | Missing X-Client-Id | Generate Client-Id and retry (see §1) |
| 402 | Free plan export blocked | Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export." |
| 429 | Rate limit (1 token/client/7 days) | Retry in 30s once |
Frequently Asked Questions
What kinds of workflows can flow-ai help with? Flow-ai is flexible enough to handle operational workflows (hiring, onboarding, offboarding), creative workflows (content production, campaign launches), and technical workflows (bug triage, deployment checklists). If it involves steps, owners, and handoffs, flow-ai can help structure it.
Do I need to know how to use automation tools to benefit from flow-ai? Not at all. Flow-ai works in plain language — you describe your process in natural terms and it translates that into a structured, actionable workflow. You don't need to configure any tools or understand automation logic to get value from it.
Can flow-ai help me improve an existing process, not just create new ones? Absolutely. One of flow-ai's most popular use cases is workflow auditing — paste in or describe your current process and ask flow-ai to identify redundancies, missing steps, or unclear ownership. It will return a cleaner, more efficient version with specific recommendations.
Is flow-ai suited for solo users or only teams? Both. Solo founders and freelancers use flow-ai to build personal operating systems — repeatable processes for client work, content creation, and admin tasks. Teams use it to align on shared processes and reduce miscommunication across roles.
Quick Start Guide
Step 1 — Describe your process in plain language. Don't overthink it. Just tell flow-ai what you're trying to accomplish: 'I need a workflow for publishing a blog post from draft to live.' The more context you give (team size, tools used, pain points), the more tailored the output.
Step 2 — Review the generated workflow structure. Flow-ai will return a step-by-step breakdown with suggested owners, dependencies, and decision points. Read through it and note anything that doesn't match your reality — that feedback loop is where the real optimization happens.
Step 3 — Refine with follow-up prompts. Ask flow-ai to adjust timelines, add approval gates, simplify steps, or split the workflow into phases. Treat it like a conversation, not a one-shot query. Example: 'Make step 3 and 4 run in parallel instead of sequentially.'
Step 4 — Export or document your workflow. Once you're satisfied, ask flow-ai to format the workflow as a checklist, a numbered SOP, or a table with owners and deadlines — ready to drop into your team wiki, Notion, or project management tool.