agoraio/agora-intake

Agora Intake — Product Routing & Needs Analysis

First entry point for vague or multi-product Agora requests. This skill understands the full product landscape, identifies what the user needs, and routes to the right place.

Note: Skip-intake logic is defined in skills/agora/SKILL.md (root router). If you are here, the root router has already determined that intake is needed. Do NOT second-guess the routing decision — proceed with the intake flow below.

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Product Landscape

| Product | What it does | Typical user says | |---------|-------------|-------------------| | RTC SDK | Real-time audio/video between humans | "video call", "live streaming", "voice chat" | | RTM | Real-time messaging / signaling | "chat", "messaging", "signaling", "notifications" | | ConvoAI | AI voice agent (ASR→LLM→TTS over RTC) | "AI assistant", "voice bot", "conversational AI" | | Cloud Recording | Record RTC sessions server-side | "recording", "archive sessions", "record calls" | | Server/Tokens | Auth token generation | "token server", "authentication", "App Certificate" | | Server Gateway SDK | Server-side media streaming (Linux) | "server SDK", "Linux SDK", "send audio from server", "server gateway" |

Product Relationships

                    ┌─────────────┐
                    │   RTC SDK   │  ← foundation layer
                    └──────┬──────┘
                           │
          ┌────────────────┼────────────────┐
          │                │                │
    ┌─────┴─────┐   ┌─────┴─────┐
    │  ConvoAI  │   │  Cloud    │
    │ (AI Agent)│   │ Recording │
    └─────┬─────┘   └───────────┘
          │
    ┌─────┴─────┐
    │    RTM    │  ← often paired with RTC/ConvoAI
    └───────────┘

Key relationships:

• ConvoAI depends on RTC — the AI agent joins an RTC channel; the client needs RTC SDK
• Cloud Recording depends on RTC — records what happens in an RTC channel
• RTM is independent but often paired with RTC or ConvoAI for signaling (call invitation, presence, chat history)

Common Product Combinations

| Use case | Products needed | |----------|----------------| | 1v1 / group video call | RTC SDK | | Video call + chat | RTC SDK + RTM | | AI voice assistant | ConvoAI + RTC SDK (client) | | AI voice assistant + chat history | ConvoAI + RTC SDK + RTM | | Live streaming with recording | RTC SDK + Cloud Recording | | Record AI conversations | ConvoAI + RTC SDK + Cloud Recording |

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Intake Flow

Step 1: Understand the Use Case

If the user's intent is not immediately clear, ask:

"What are you trying to build? Please describe the use case."

Listen for keywords and map to products using the Product Landscape table above.

Step 2: Identify Products

Based on the user's description, determine:

1. Primary product — the main capability they need
2. Supporting products — additional products required by the primary, or that enhance the use case
3. Optional products — nice-to-haves the user may not have considered

Use the Product Relationships and Common Combinations tables to make this determination.

Server-side disambiguation — "backend", "Python/Go/Node.js server", or "server-side" alone is ambiguous. Clarify which server role is needed before routing:

| User says | They need | |-----------|-----------| | "authenticate users", "issue tokens", "token server" | Server/Tokens → references/server/ | | "start an AI agent", "call the ConvoAI API", "Python ConvoAI backend" | ConvoAI → references/conversational-ai/ | | "my server sends audio/video", "server joins RTC channel", "Linux media SDK" | Server Gateway → references/server-gateway/ |

If unclear, ask: "Does your server need to (a) generate auth tokens, (b) call the ConvoAI REST API to start agents, or (c) send/receive audio-video media directly?"

Example analysis:

User: "I want to build an AI customer service bot where users call in and an AI answers"

• Primary: ConvoAI (AI voice agent)
• Supporting: RTC SDK (client-side for user to join channel)
• Optional: Cloud Recording (record conversations for QA), RTM (send chat transcript)

Step 3: Confirm with User

Present your analysis:

Needs Analysis:
─────────────────────────────────────
Use case:           [user's described use case]
Primary product:    [primary product]
Supporting:         [supporting products]
Optional:           [optional enhancements]
─────────────────────────────────────

Does this look right? Any adjustments needed?

Wait for user confirmation before proceeding.

Step 4: Route to Product Skill

For each identified product, route to its skill file:

| Product | Product skill | |---------|--------------| | ConvoAI | references/conversational-ai/README.md | | RTC SDK | references/rtc/README.md | | RTM | references/rtm/README.md | | Cloud Recording | references/cloud-recording/README.md | | Token generation | references/server/README.md | | Server Gateway SDK | references/server-gateway/README.md |

When multiple products are needed, run the primary product's skill first, then address supporting products in order.

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Decision Shortcuts

For common patterns, skip the full intake flow:

| User says | Shortcut | |-----------|----------| | "video call" / "live stream" / "RTC" | → references/rtc/README.md directly | | "screen share" / "screen sharing" | → references/rtc/README.md → cross-platform-coordination.md | | "chat" / "messaging" / "signaling" | → references/rtm/README.md directly | | "voice bot" / "AI assistant" / "ConvoAI" | → references/conversational-ai/README.md directly | | "recording" / "record sessions" / "record calls" | → references/cloud-recording/README.md directly | | "generate token" / "token server" / "App Certificate" | → references/server/README.md directly | | "Server Gateway" / "Linux SDK" / "server sends audio" | → references/server-gateway/README.md directly |