Tired of manually juggling disconnected tasks, redundant processes, and bottlenecked workflows? Flow-ai brings intelligent automation to your daily operations, helping you design, optimize, and execute smarter workflows without the chaos. Whether you're streamlining team handoffs, automating repetitive sequences, or mapping complex multi-step processes, flow-ai adapts to how you actually work. Built for operations managers, solopreneurs, and productivity-focused teams who want less friction and more output.
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:
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:
https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id headerdata.token from 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.
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.
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 | ❌ |
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 frontmatter versionX-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)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.
| 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.
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)
| 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 |
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.
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.