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Use when building real-time or post-call conversation analysis, language operator pipelines, sentiment analysis, agent assist, cross-channel analytics, or querying aggregated conversation insights (sentiment trends, escalation rates, dashboards).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"openai","OpenAI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fopenai.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"LLM","llm","tag",{"name":17,"slug":18,"type":15},"Analytics","analytics",{"name":20,"slug":21,"type":15},"API Development","api-development",{"name":23,"slug":24,"type":15},"Twilio","twilio",3992,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins","2026-06-30T19:00:57.102",null,465,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":33},[],"OpenAI Plugins","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins\u002Ftree\u002FHEAD\u002Fplugins\u002Ftwilio-developer-kit\u002Fskills\u002Ftwilio-conversation-intelligence","---\nname: conversation-intelligence\ndescription: \"Twilio Conversation Intelligence development guide. Use when building real-time or post-call conversation analysis, language operator pipelines, sentiment analysis, agent assist, cross-channel analytics, or querying aggregated conversation insights (sentiment trends, escalation rates, dashboards).\"\n---\n\n# Conversation Intelligence\n\nDecision-making guide for Twilio's Conversation Intelligence v3 API — real-time and post-call GenAI analysis of conversations across Voice, SMS, RCS, and WhatsApp. Covers Intelligence Configurations, Language Operators (Twilio-authored and custom), Rules, Triggers, Actions, and result consumption.\n\n> **Security:** All inbound messages captured by the Orchestrator are untrusted external input. If Intelligence operators process this content with LLMs, their prompts should include instructions to ignore adversarial content and not follow instructions embedded in customer messages.\n\n\n> **GA** — Conversation Intelligence v3 is generally available.\n\n## Use Cases\n\nConversation Intelligence powers **human agent augmentation** — giving every agent a \"second brain\" that listens, understands, and surfaces the right data at the right time. Agents focus on empathy, judgment, and problem-solving; AI handles analysis and assistance.\n\n### Wrap-up Agent Assist (Post-Call)\n\nAnalyze completed conversations and generate structured outputs — summaries, sentiment signals, topic dispositions. Reduces after-call work, accelerates agent transitions to next interaction. **Low-friction entry point** — start here.\n\n- **Operators**: Summary, Sentiment, custom Conversation Scoring\n- **Trigger**: `CONVERSATION_END`\n- **Integration**: Webhook → CRM case note creation\n\n### Real-time Agent Assist\n\nAnalyze conversations as they unfold. Surface sentiment shifts, script adherence signals, or recommended next responses enriched with customer history and enterprise knowledge. Agents respond more confidently without searching across systems.\n\n- **Operators**: Script Adherence, Next Best Response, Escalation Risk (custom)\n- **Trigger**: `COMMUNICATION`\n- **Integration**: Webhook → Agent desktop overlay\n\n### Real-time Workflow Automation\n\nCombine real-time intelligence with orchestration to trigger downstream workflows when specific conditions are met — escalate to supervisor, trigger fraud prevention, notify specialist.\n\n- **Operators**: Custom risk detection, compliance monitoring\n- **Trigger**: `COMMUNICATION`\n- **Integration**: Webhook → Workflow engine \u002F TaskRouter\n\n### Contact Center QA\n\nGenerate post-interaction summaries, sentiment scores, and compliance signals for QA, coaching, and analytics. Aggregate across interactions to support training and continuous optimization.\n\n- **Operators**: Script Adherence, Summary, custom Conversation Scoring\n- **Trigger**: `CONVERSATION_END`\n- **Integration**: Webhook → Analytics \u002F BI tools\n\n## How It Works\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  1. Customer engages agent (Voice, SMS, WhatsApp, RCS, Chat, Email)         │\n└─────────────────────────────────────────────────────────────────────────────┘\n                                      │\n                                      ▼\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  2. Conversations (Conversation Orchestrator) groups communications into a  │\n│     Conversation                                                            │\n│     - Normalizes channel events                                             │\n│     - Groups related messages\u002Futterances                                    │\n│     - Tracks participants (CUSTOMER, HUMAN_AGENT, AI_AGENT)                 │\n└─────────────────────────────────────────────────────────────────────────────┘\n                                      │\n                                      ▼\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  3. Conversation events trigger Intelligence rules                          │\n│     - COMMUNICATION: on each new message\u002Futterance                          │\n│     - CONVERSATION_END: when conversation closes                            │\n│     - CONVERSATION_INACTIVE: when conversation goes idle                    │\n└─────────────────────────────────────────────────────────────────────────────┘\n                                      │\n                                      ▼\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  4. Language Operators analyze the conversation                             │\n│     - Twilio-authored: Sentiment, Summary, NBR, Script Adherence            │\n│     - Custom: domain-specific analysis with your prompts                    │\n│     - Context: enriched with Customer Memory + Enterprise Knowledge         │\n└─────────────────────────────────────────────────────────────────────────────┘\n                                      │\n                                      ▼\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  5. Results delivered via webhook + REST API                                │\n│     - Real-time: Agent desktop, workflow triggers                           │\n│     - Post-call: CRM notes, QA systems, analytics                           │\n│     - Aggregated: Conversational Insights for cross-conversation analysis   │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n**Key insight**: Real-time and post-conversation intelligence use the **same underlying model**. Start with low-friction post-call summaries, then progressively introduce real-time assist using the same components.\n\n## Scope\n\n### CAN\n\n- Analyze conversations in real-time (per-message) and post-conversation (at close\u002Finactive) via Language Operators\n- Use 4 Twilio-authored operators: Sentiment, Summary, Next Best Response, Script Adherence\n- Create custom Language Operators with natural language prompts and structured output (TEXT, JSON, CLASSIFICATION) — `EXTRACTION` is a read-only format returned by some Twilio-authored operators; it cannot be set on custom operators you create\n- Define up to 5 rules per Intelligence Configuration, each with 1-5 operators (minimum 1 required), 0 or 1 trigger, and 0-2 webhook actions\n- Throttle real-time triggers with `count` parameter (run every N communications, min 1, max 20)\n- Deliver results via webhook (POST) and query historically via REST API\n- Track conversations across SMS, Voice, RCS, WhatsApp, Chat, and Email channels via Conversation Orchestrator (Conversations v2) integration\n- Create custom operators with parameters (`{{parameters.name}}` syntax), including knowledge base references (`KNOWLEDGE_BASE_AND_SOURCE_IDS` type — value format: `knowledge_base_id:knowledge_source_id`)\n- Enrich operators with Customer Memory (`context.memory.enabled: true`) and Enterprise Knowledge (`context.knowledge.bases: [...]`) at the rule or operator level\n- Add `trainingExamples` (input\u002Foutput pairs) to custom operators to improve accuracy\n- Pin a specific operator version in a rule via `operators[].version`; omit to use latest\n- Query OperatorResults filtered by `intelligenceConfigurationId`, `conversationId`, or `operatorId`\n- Query operator versions and fetch specific version details\n- Delete Intelligence Configurations, custom Operators, and individual OperatorResults via REST API\n- Use ETag\u002FIf-Match headers for optimistic locking on **Operator** updates (returns 412 on mismatch) — ETag is not supported on Configuration updates\n- Filter Conversations by `status`, `channels`, `createdAtBefore`\u002F`createdAtAfter`, `channelId`, `intelligenceConfigurationIds`, `operatorIds`\n- Authenticate with both Account SID\u002FAuth Token and API Key\u002FSecret\n- Define rules without a trigger (trigger is optional per spec; runs on all events if omitted)\n\n### CANNOT\n\n- **JSON-only API** — All v3 endpoints require `Content-Type: application\u002Fjson`. Form-encoded bodies return HTTP 415 with error 20422.\n- **No standalone operation** — v3 requires Conversation Orchestrator (Conversations v2) for conversation capture. You cannot feed raw messages or recordings into v3 directly.\n- **No per-message sentiment** — Sentiment is conversation-level, accumulating across all messages. A conversation with one positive and one negative message returns \"mixed\", not separate results per message.\n- **No deleting Twilio-authored operators** — DELETE returns 404 \"Operator not found\" for Twilio-authored operators, not 403. They are not treated as \"yours\" to delete.\n- **No editing Twilio-authored operator prompts** — Twilio-authored operators have `prompt: null` when retrieved via GET. The prompt is hidden and not configurable.\n- **No more than 2 actions per rule** — The API enforces `size must be between 0 and 2` for actions.\n- **No more than 5 rules per configuration** — API enforces `size must be between 0 and 5`.\n- **No PCI or HIPAA compliance** — Conversation Intelligence v3 is not PCI compliant or HIPAA Eligible. Do not use for payment data or protected health information.\n- **No GET\u002FPUT\u002FDELETE on v3 Conversations** — The Conversations endpoint is read-only (GET list, GET by ID). Conversation lifecycle is controlled by Conversation Orchestrator, not by the Intelligence API.\n- **No unsupported JSON schema features** — The following are rejected in `outputSchema`: `minLength`\u002F`maxLength` (strings), `patternProperties` (objects), `uniqueItems` (arrays). Use basic types only.\n- **Cannot use PUT to update a live configuration** — PUT creates an inactive version with no activation API. Operators silently stop returning results. Workaround: DELETE the configuration and POST to recreate it.\n- **Silent Memory Store linkage failures** — If `memoryStoreId` points to a deleted or invalid store, capture still works but identity resolution and extraction silently fail with no error. Implement periodic health checks to verify Memory Store linkage is functioning.\n\n## Quick Decision\n\n| Need | Use | Why |\n|------|-----|-----|\n| Real-time agent assist during live calls\u002Fchats | v3 + COMMUNICATION trigger + Next Best Response operator | Real-time webhook delivery per utterance |\n| Post-call QA scoring | v3 + CONVERSATION_END trigger + Script Adherence operator | Runs once at conversation close, returns detailed score |\n| Conversation sentiment tracking | v3 + Sentiment operator (Twilio-authored) | Conversation-level classification: positive\u002Fnegative\u002Fneutral\u002Fmixed |\n| Post-call recording transcription + analysis | v2 Voice Intelligence (existing) | v3 does not ingest recordings directly — v2 pipeline handles recording→transcript→operators |\n| Custom domain-specific analysis | v3 + Custom operator with JSON output | Define prompt, parameters, structured output schema |\n| Cross-channel conversation history | Conversations v2 (Conversation Orchestrator) alone | v3 adds analysis on top; Conversation Orchestrator handles capture and history |\n| Simple keyword extraction | v3 + Custom operator (EXTRACTION format) | Structured extraction with custom prompt |\n\n## Decision Frameworks\n\n### v2 (Voice Intelligence) vs v3 (Conversation Intelligence)\n\n| Dimension | v2 (Voice Intelligence) | v3 (Conversation Intelligence) |\n|-----------|------------------------|-----------------------------------|\n| Input source | Recording SIDs (audio) | Conversation Orchestrator conversations (text\u002Ftranscriptions) |\n| Channels | Voice only | SMS, Voice, RCS, WhatsApp, Chat, Email |\n| Operator management | Console only (attach to Intelligence Service) | REST API (full CRUD on custom operators) |\n| Trigger model | Post-transcription (async) | Real-time (per-message) or post-conversation |\n| Result delivery | Webhook (`voice_intelligence_transcript_available`) | Webhook (per-rule action) + REST query |\n| SDK support | `client.intelligence.v2.transcripts` | Twilio Node.js SDK supported |\n| SID prefix | `GA` (service), `GT` (transcript), `LY` (operator) | `intelligence_configuration_*`, `intelligence_operator_*` |\n| Status | GA | GA |\n| Coexistence | Works alongside v3 | Works alongside v2 |\n\nUse v2 when: You need post-call transcription from recordings, or need GA stability.\nUse v3 when: You need real-time analysis, cross-channel support, or API-managed custom operators.\n\n### Real-Time vs Post-Conversation\n\n| Factor | COMMUNICATION trigger | CONVERSATION_END trigger | CONVERSATION_INACTIVE trigger |\n|--------|----------------------|-------------------------|-------------------------------|\n| When it fires | On each new message\u002Futterance | When conversation closes | When conversation goes idle |\n| Latency | Near real-time | Seconds after close | After inactive timeout |\n| Use cases | Agent assist, escalation detection, live compliance | QA scoring, summaries, CRM updates | Idle conversation follow-up |\n| Operator context | Accumulating — sees all messages so far | Complete conversation | Messages up to inactivity point |\n| Throttling | `count` parameter (every N messages) | N\u002FA | N\u002FA |\n| Cost implication | Runs per message (more executions) | Runs once per conversation | Runs once per inactivity event |\n\n### Operator Version Lifecycle\n\n| Status | Behavior | When |\n|--------|----------|------|\n| `PREVIEW` | Normal execution, restricted visibility | Internal\u002Ftesting versions |\n| `ACTIVE` | Normal execution, full availability | Production-ready versions |\n| `DEPRECATED` | Executes with Warn event via Watch | Migration window — update to newer version |\n| `RETIRED` | Hard failure, Error logged in Watch | Must update Intelligence Configuration manually |\n\n### Custom Operator Output Formats\n\n| Format | Use When | Result Shape | Schema Support |\n|--------|----------|-------------|----------------|\n| TEXT | Free-form analysis, summaries, translations | `{\"text\": \"...\"}` | Auto-generated (not customizable) |\n| JSON | Structured extraction with custom fields | User-defined via `outputSchema` | Full JSON Schema (max 100 props, 10 nesting levels, 1000 enum values max) |\n| CLASSIFICATION | Category labeling (sentiment, intent, topic) | `{\"label\": \"...\"}` | Auto-generated |\n| EXTRACTION | Returned by some Twilio-authored operators only — cannot be set on custom operators | `{\"entities\": [{\"text\": \"...\", \"label\": \"...\"}]}` | N\u002FA (read-only) |\n\n### Twilio-Authored Operator Reference\n\nReady-to-use operators maintained by Twilio. Use these IDs directly in rules — no custom prompt required.\n\n| Operator | ID | Best Trigger | Use Case |\n|----------|-----|--------------|----------|\n| **Sentiment** | `intelligence_operator_01kcrvw16kfa88qvgrfmr7y151` | COMMUNICATION | Real-time sentiment tracking (positive\u002Fnegative\u002Fneutral\u002Fmixed) |\n| **Summary** | `intelligence_operator_01kcv35pnkeysaf6z6cqtbpegn` | CONVERSATION_END | Post-call conversation summary |\n| **Next Best Response** | `intelligence_operator_01kea27sy7ffsafmtsfp17nzx4` | COMMUNICATION | Real-time agent assist with suggested responses |\n| **Script Adherence** | `intelligence_operator_01kf34tcyefpyb1t4m0nbd8rxg` | CONVERSATION_END | QA scoring for script compliance |\n\n**Note**: Twilio-authored operators have `author: \"TWILIO\"` and `prompt: null` when retrieved via GET. Prompts are hidden and not configurable. Use custom operators if you need control over the prompt.\n\n## Integration Patterns\n\nCode samples use raw `fetch()` for clarity, but the Twilio Node.js SDK is also supported for v3.\n\n### Authentication Helper\n\n```javascript\nconst INTELLIGENCE_V3_BASE = 'https:\u002F\u002Fintelligence.twilio.com\u002Fv3';\n\nfunction getAuthHeaders() {\n  const credentials = Buffer.from(\n    `${process.env.TWILIO_ACCOUNT_SID}:${process.env.TWILIO_AUTH_TOKEN}`\n  ).toString('base64');\n  return {\n    'Authorization': `Basic ${credentials}`,\n    'Content-Type': 'application\u002Fjson',\n  };\n}\n```\n\n### Create Intelligence Configuration with Rules\n\n```javascript\n\u002F\u002F Step 1: Create configuration (empty rules initially)\nconst configResponse = await fetch(\n  `${INTELLIGENCE_V3_BASE}\u002FControlPlane\u002FConfigurations`,\n  {\n    method: 'POST',\n    headers: getAuthHeaders(),\n    body: JSON.stringify({\n      displayName: 'Customer Support Analytics',\n      description: 'Real-time sentiment + post-call summary',\n      rules: [],\n    }),\n  }\n);\nconst config = await configResponse.json();\n\u002F\u002F config.id = \"intelligence_configuration_...\"\n\n\u002F\u002F Step 2: Add rules via PUT (replaces all rules)\nconst updateResponse = await fetch(\n  `${INTELLIGENCE_V3_BASE}\u002FControlPlane\u002FConfigurations\u002F${config.id}`,\n  {\n    method: 'PUT',\n    headers: getAuthHeaders(),\n    body: JSON.stringify({\n      displayName: 'Customer Support Analytics',\n      rules: [\n        {\n          operators: [\n            { id: 'intelligence_operator_01kcrvw16kfa88qvgrfmr7y151' }, \u002F\u002F Sentiment\n          ],\n          triggers: [{ on: 'COMMUNICATION' }],\n          actions: [\n            { type: 'WEBHOOK', method: 'POST', url: 'https:\u002F\u002Fyour-app.com\u002Frealtime-results' },\n          ],\n        },\n        {\n          operators: [\n            { id: 'intelligence_operator_01kcv35pnkeysaf6z6cqtbpegn' }, \u002F\u002F Summary\n          ],\n          triggers: [{ on: 'CONVERSATION_END' }],\n          actions: [\n            { type: 'WEBHOOK', method: 'POST', url: 'https:\u002F\u002Fyour-app.com\u002Fpost-call-results' },\n          ],\n        },\n      ],\n    }),\n  }\n);\nconst updatedConfig = await updateResponse.json();\n\u002F\u002F updatedConfig.version = 2 (auto-incremented)\n```\n\n### Link to Conversation Orchestrator Conversation Configuration\n\n```javascript\n\u002F\u002F Intelligence config must be linked to a Conversation Orchestrator conversation config\n\u002F\u002F See conversation-orchestrator skill for full Conversation Orchestrator setup\nconst convConfigResponse = await fetch(\n  'https:\u002F\u002Fconversations.twilio.com\u002Fv2\u002FControlPlane\u002FConfigurations',\n  {\n    method: 'POST',\n    headers: getAuthHeaders(),\n    body: JSON.stringify({\n      displayName: 'Support Config',\n      memoryStoreId: 'mem_store_...',  \u002F\u002F Required — create via Memory API first\n      conversationGroupingType: 'GROUP_BY_PARTICIPANT_ADDRESSES',\n      intelligenceConfigurationIds: [config.id],\n      channelSettings: {\n        SMS: {\n          statusTimeouts: { inactive: 5, closed: 10 },\n          captureRules: [{ from: '*', to: '+1XXXXXXXXXX', metadata: {} }],\n        },\n      },\n    }),\n  }\n);\n```\n\n### Consume Operator Results\n\n```javascript\n\u002F\u002F Query all results for an intelligence configuration\nconst resultsResponse = await fetch(\n  `${INTELLIGENCE_V3_BASE}\u002FOperatorResults?intelligenceConfigurationId=${config.id}`,\n  { headers: getAuthHeaders() }\n);\nconst results = await resultsResponse.json();\n\nfor (const operatorResult of results.items) {\n  console.log(`Operator: ${operatorResult.operator.id}`);\n  console.log(`Format: ${operatorResult.outputFormat}`);\n  console.log(`Payload: ${JSON.stringify(operatorResult.result)}`); \u002F\u002F e.g. { text: \"...\" } or { label: \"...\" }\n  console.log(`Conversation: ${operatorResult.conversationId}`);\n  console.log(`Trigger: ${operatorResult.executionDetails.trigger.on}`);\n  \u002F\u002F Context that was actually used at runtime (single source of truth):\n  console.log(`Memory profile: ${operatorResult.executionDetails.resolvedContext?.memory?.profileId}`);\n  console.log(`Knowledge sources: ${JSON.stringify(operatorResult.executionDetails.resolvedContext?.knowledge?.sources)}`);\n  \u002F\u002F Cost\u002Fperf metadata:\n  console.log(`Model: ${operatorResult.metadata.system.resolvedModel}, latencyMs: ${operatorResult.metadata.system.latencyMs}`);\n}\n```\n\n### Paginate Through Results\n\nAll list endpoints (`\u002FOperatorResults`, `\u002FConversations`, `\u002FOperators`, `\u002FConfigurations`) use cursor-based pagination. Default page size is 50; maximum is 1000.\n\n```javascript\nasync function* getAllOperatorResults(configId) {\n  let pageToken = undefined;\n  do {\n    const url = new URL(`${INTELLIGENCE_V3_BASE}\u002FOperatorResults`);\n    url.searchParams.set('intelligenceConfigurationId', configId);\n    url.searchParams.set('pageSize', '1000');\n    if (pageToken) url.searchParams.set('pageToken', pageToken);\n\n    const response = await fetch(url, { headers: getAuthHeaders() });\n    const data = await response.json();\n\n    yield* data.items;\n    pageToken = data.meta?.nextToken; \u002F\u002F null\u002Fundefined when no more pages\n  } while (pageToken);\n}\n\n\u002F\u002F Usage:\nfor await (const result of getAllOperatorResults(config.id)) {\n  console.log(result.operator.id, result.result);\n}\n```\n\n### Enable Customer Memory and Enterprise Knowledge on a Rule\n\n```javascript\n\u002F\u002F Context is configured at the rule level (not the operator level)\nrules: [\n  {\n    operators: [{ id: 'intelligence_operator_01kea27sy7ffsafmtsfp17nzx4' }], \u002F\u002F NBR\n    triggers: [{ on: 'COMMUNICATION' }],\n    actions: [{ type: 'WEBHOOK', method: 'POST', url: 'https:\u002F\u002Fyour-app.com\u002Fnbr' }],\n    context: {\n      memory: { enabled: true },          \u002F\u002F inject customer profile from Memory Store\n      knowledge: {\n        bases: ['knowledge_base_id_here'], \u002F\u002F inject enterprise KB articles\n      },\n    },\n  },\n]\n```\n\n### Create a Custom Operator with Training Examples and Parameters\n\n```javascript\nconst operatorResponse = await fetch(\n  `${INTELLIGENCE_V3_BASE}\u002FControlPlane\u002FOperators`,\n  {\n    method: 'POST',\n    headers: getAuthHeaders(),\n    body: JSON.stringify({\n      displayName: 'Escalation Risk Detector',\n      prompt: `Analyze this conversation between a customer and agent.\nProduct context: {{parameters.productName}}\nClassify escalation risk as LOW, MEDIUM, or HIGH based on customer frustration signals.`,\n      outputFormat: 'CLASSIFICATION',\n      parameters: {\n        productName: { type: 'STRING', required: true, description: 'Product line being discussed' },\n        \u002F\u002F KNOWLEDGE_BASE_AND_SOURCE_IDS parameters are passed as \"kb_id:source_id\" at rule time\n        knowledgeContext: { type: 'KNOWLEDGE_BASE_AND_SOURCE_IDS', required: false },\n      },\n      trainingExamples: [\n        {\n          input: 'Customer: This is the third time I have called about this issue',\n          output: 'HIGH',\n        },\n        {\n          input: 'Customer: Thanks, I think that might work',\n          output: 'LOW',\n        },\n      ],\n    }),\n  }\n);\nconst operator = await operatorResponse.json();\n\u002F\u002F Pin this operator to a specific version in your rule:\n\u002F\u002F operators: [{ id: operator.id, version: operator.version }]\n```\n\n## Gotchas\n\n### Setup\n\n1. **Memory Store is required for Conversation Orchestrator**: You cannot create a Conversations v2 Configuration without a `memoryStoreId`. The Memory API returns `\"memoryStoreId: must not be null\"` (error 20001). Create the Memory Store first via `POST memory.twilio.com\u002Fv1\u002FControlPlane\u002FStores`.\n\n2. **JSON-only API**: All v3 endpoints require `Content-Type: application\u002Fjson`. Form-encoded bodies return HTTP 415 with error 20422 (\"does not support this payload format\"). This matches Conversation Orchestrator but differs from most Twilio APIs.\n\n3. **v2 and v3 coexist independently**: Creating v3 configurations does not affect v2 Intelligence Services (`GA*` SIDs). Both are accessible on the same account simultaneously. They share the `intelligence.twilio.com` host but use different URL paths (`\u002Fv2\u002FServices` vs `\u002Fv3\u002FControlPlane\u002FConfigurations`).\n\n### Configuration\n\n4. **PUT creates an inactive version — operators stop returning results**: When you PUT to update an Intelligence Configuration, the new version is created in an **inactive state**. There is no activation API to make it live. Your operators will silently stop producing results. **Workaround: DELETE the configuration and POST to recreate it** with the updated rules\u002Foperators. This is the only reliable way to update a live configuration.\n\n5. **PUT replaces all rules**: Updating a configuration replaces the entire `rules` array. There is no PATCH or per-rule update. Always include all rules in the PUT body, not just the changed one.\n\n6. **Config version auto-increments**: Each PUT bumps the `version` field. Conversation Orchestrator conversation configs use `version` for optimistic locking, but Intelligence configs accept PUT without version checks.\n\n7. **Rules get their own IDs**: When rules are created via PUT, each gets an auto-generated ID (`intelligence_configurationrule_*`). These IDs appear in OperatorResult references but are not user-settable.\n\n8. **Trigger count parameter throttles execution**: Setting `{\"on\":\"COMMUNICATION\",\"parameters\":{\"count\":3}}` runs the operator every 3 messages instead of every message.\n\n### Runtime\n\n9. **Dual capture rules cause duplicate processing**: If both inbound (`from: *, to: +1XXX`) and outbound (`from: +1XXX, to: *`) capture rules match the same SMS, Conversation Orchestrator creates two Communications for one message, and Intelligence produces two OperatorResults. Use unidirectional capture rules unless you specifically want both.\n\n10. **Twilio number is auto-typed HUMAN_AGENT**: In Conversation Orchestrator conversations, the Twilio number is automatically assigned `type: \"HUMAN_AGENT\"` and the external number gets `type: \"CUSTOMER\"` with automatic memory profile resolution (`mem_profile_*`).\n\n11. **Sentiment accumulates across messages**: The Sentiment operator analyzes the full conversation context, not individual messages. After a positive message, sentiment was \"positive\". After adding a negative message to the same conversation, it became \"mixed\".\n\n12. **Near real-time delivery for COMMUNICATION trigger**: Results are delivered via webhook shortly after each utterance — the full pipeline is Conversation Orchestrator capture → Intelligence trigger → Operator execution → webhook delivery.\n\n13. **Custom operator TEXT output auto-wraps**: Custom operators with `outputFormat: \"TEXT\"` always return `{\"text\": \"...\"}` regardless of the prompt. The outputSchema is auto-generated and not customizable for TEXT format.\n\n### Observability\n\n14. **conversationConfigurationId returns \"unused\"**: The v3 Conversations endpoint returns `\"conversationConfigurationId\": \"unused\"` instead of the actual Conversation Orchestrator config ID. Use `intelligenceConfigurationIds` array instead for linking.\n\n15. **No isTwilioAuthored field**: Twilio-authored operators are distinguished by `author: \"TWILIO\"`, custom operators by `author: \"SELF\"`. There is no boolean `isTwilioAuthored` field.\n\n16. **Twilio-authored operator prompts are hidden**: GET on a Twilio-authored operator returns `prompt: null`. You cannot inspect or modify the system prompt. Custom operators return the full prompt.\n\n17. **Two separate metadata sections**: OperatorResults carry both `metadata.system` and `executionDetails.resolvedContext` — they serve different purposes:\n   - `metadata.system`: cost and performance — `resolvedModel` (LLM used), `latencyMs`, `inputCharacters`\u002F`outputCharacters` (billing units), `inputTruncated`\n   - `executionDetails.resolvedContext`: what context was actually injected at runtime — `memory` (`profileId`, `memoryStoreId`) and `knowledge` (`sources`: array of `{baseId, sourceId}`). This is the single source of truth for context resolution.\n\n### Error Handling\n\n18. **Error codes are consistent**: v3 uses Twilio standard error codes: `20001` (bad request\u002Fvalidation), `20404` (not found), `20422` (unsupported format), `70001` (operator validation). All include `userError: true` and descriptive messages.\n\n19. **Invalid operator ID gives specific error**: Using a non-existent operator ID in a rule returns 400 with code `70001` and message identifying the exact invalid operator.\n\n### JSON Schema\n\n20. **All JSON schema fields are required by default — and Twilio auto-sets this**: Twilio automatically sets `additionalProperties: false` and marks all provided fields as required in `outputSchema`. Do not add `required` or `additionalProperties` yourself — Twilio overwrites any values you provide. The practical consequence: if the LLM cannot populate a field, the operator execution fails. Use union types for nullable fields: `\"type\": [\"string\", \"null\"]`.\n\n21. **Nullable field pattern**: To make a field optional\u002Fnullable in JSON output, use array type union:\n```json\n{\n  \"outputSchema\": {\n    \"type\": \"object\",\n    \"properties\": {\n      \"requiredField\": { \"type\": \"string\" },\n      \"optionalField\": { \"type\": [\"string\", \"null\"] }\n    }\n  }\n}\n```\nThis allows the operator to return `null` for fields where the LLM has insufficient context.\n\n21. **Unsupported JSON schema features cause silent or hard failures**: The following JSON Schema features are NOT supported in `outputSchema` and will be rejected. Stick to `type`, `enum`, `properties`, `items`, `anyOf`, `$defs`\u002F`$ref`.\n   - Strings: `minLength`, `maxLength`\n   - Objects: `patternProperties`, `unevaluatedProperties`, `propertyNames`, `minProperties`, `maxProperties`\n   - Arrays: `unevaluatedItems`, `contains`, `minContains`, `maxContains`, `uniqueItems`\n\n22. **`executionDetails.context` was removed**: Older docs and some live responses may show `executionDetails.context`. This field was removed in a breaking change. Use `executionDetails.resolvedContext` — it contains `memory` (profileId, memoryStoreId) and `knowledge` (array of baseId\u002FsourceId pairs).\n\n23. **Operator result query param is `intelligenceConfigurationId`** (not `intelligenceConfiguration`): The REST API filter parameter for listing OperatorResults by config is `intelligenceConfigurationId`. Using the shorter form returns unfiltered results.\n\n24. **`KNOWLEDGE_BASE_AND_SOURCE_IDS` parameter values must use colon-separated format**: When passing a knowledge base parameter to an operator at rule time, the value must be formatted as `\"knowledge_base_id:knowledge_source_id\"`. Passing just the KB ID or using any other separator returns a validation error. Only plaintext KB sources are supported.\n\n25. **`KNOWLEDGE_BASE_AND_SOURCE_IDS` parameters do not support `default`**: Unlike `STRING`, `INTEGER`, `NUMBER`, and `BOOLEAN` parameter types which all allow a `default` value, `KNOWLEDGE_BASE_AND_SOURCE_IDS` does not. Defining a `default` on a KB parameter will be ignored or rejected.\n\n26. **Each rule requires at least 1 operator**: The `operators` array on a rule has `minItems: 1`. Submitting a rule with an empty operators array on create or update returns a 400 validation error.\n\n27. **List endpoints are paginated — don't assume you got all results**: All list endpoints (`\u002FOperatorResults`, `\u002FConversations`, `\u002FOperators`, `\u002FConfigurations`) return a max of 50 items by default (max 1000 with `pageSize`). The response `meta.nextToken` is non-null when more pages exist. Always paginate when querying production data sets.\n\n## Conversational Insights (Cross-Conversation Analytics)\n\nWhere the Intelligence API gives you per-conversation OperatorResults, the **Insights API v3** is the query layer for aggregating across thousands of conversations — grouping, filtering, and counting by dimensions like sentiment, channel, language, and operator output.\n\nBase URL: `https:\u002F\u002Finsights.twilio.com`\n\n> **Public Beta** — Insights v3 is currently in public beta. The query schema is subject to change.\n\n### When to Use Insights vs Intelligence REST API\n\n| Goal | Use |\n|------|-----|\n| Get the result for a specific conversation | Intelligence API: `GET \u002Fv3\u002FOperatorResults?conversationId=...` |\n| Count conversations by sentiment over time | Insights API: query with `OperatorResult.Value` dimension |\n| Find all conversations where agent went off-script | Insights API: filter on `OperatorResult.Value` |\n| Build a sentiment trend dashboard | Insights API: group by `DateCreated` + `OperatorResults` |\n| Discover available metrics and dimensions | Insights API: `GET \u002Fv3\u002FInsightsDomains\u002FConversations\u002FMetadata` |\n\n### Endpoints\n\n| Method | Path | Purpose |\n|--------|------|---------|\n| `POST` | `\u002Fv3\u002FInsightsDomains\u002FConversations\u002FQuery` | Execute a semantic query, returns first page |\n| `GET` | `\u002Fv3\u002FInsightsDomains\u002FConversations\u002FQuery?pageToken=...` | Fetch subsequent pages |\n| `GET` | `\u002Fv3\u002FInsightsDomains\u002FConversations\u002FMetadata` | Discover available cubes, measures, dimensions |\n\nSame Basic Auth as Intelligence API. JSON-only (`Content-Type: application\u002Fjson`).\n\n### Query a Sentiment Distribution\n\n```javascript\nconst INSIGHTS_BASE = 'https:\u002F\u002Finsights.twilio.com';\n\nconst response = await fetch(\n  `${INSIGHTS_BASE}\u002Fv3\u002FInsightsDomains\u002FConversations\u002FQuery`,\n  {\n    method: 'POST',\n    headers: getAuthHeaders(), \u002F\u002F same helper as Intelligence API\n    body: JSON.stringify({\n      domain: 'Conversations',\n      query: {\n        measures: ['Conversation.Count'],\n        dimensions: ['OperatorResults', 'Channels', 'DateCreated'],\n        filters: [{\n          op: 'AND',\n          expressions: [\n            { op: 'IN', field: 'OperatorResult.Value', values: ['positive', 'negative'] },\n          ],\n        }],\n        orderBy: [{ field: 'OperatorResults.CreatedDate', direction: 'DESC' }],\n      },\n    }),\n  }\n);\nconst data = await response.json();\n\u002F\u002F data.items = [{ Id: 'conv1', OperatorResults: 'positive', Channels: ['voice'], ... }]\n\u002F\u002F data.meta.nextToken — use for next page (null if last page)\n```\n\n**Query fields:**\n\n| Field | Description |\n|-------|-------------|\n| `query.measures` | What to aggregate — e.g. `\"Conversation.Count\"`, `\"OperatorResult.Count\"` |\n| `query.dimensions` | What to group by — e.g. `\"OperatorResults\"`, `\"Channels\"`, `\"Languages\"`, `\"DateCreated\"` |\n| `query.filters` | Nested filter tree with `op` + `expressions`. Filter ops: `AND`, `OR`, `EQ`, `NE`, `GT`, `LT`, `IN` |\n| `query.orderBy` | Sort by field + `ASC`\u002F`DESC` |\n\n### Pagination\n\nPOST returns the first page. Subsequent pages use GET with `pageToken`. Stop when `meta.nextToken` is null.\n\n```javascript\nasync function* queryAll(queryBody) {\n  const first = await fetch(`${INSIGHTS_BASE}\u002Fv3\u002FInsightsDomains\u002FConversations\u002FQuery`,\n    { method: 'POST', headers: getAuthHeaders(), body: JSON.stringify(queryBody) }\n  ).then(r => r.json());\n  yield* first.items;\n\n  let nextToken = first.meta?.nextToken;\n  while (nextToken) {\n    const page = await fetch(\n      `${INSIGHTS_BASE}\u002Fv3\u002FInsightsDomains\u002FConversations\u002FQuery?pageToken=${nextToken}`,\n      { headers: getAuthHeaders() }\n    ).then(r => r.json());\n    yield* page.items;\n    nextToken = page.meta?.nextToken;\n  }\n}\n```\n\n### Discover Available Dimensions and Measures\n\n```javascript\nconst meta = await fetch(\n  `${INSIGHTS_BASE}\u002Fv3\u002FInsightsDomains\u002FConversations\u002FMetadata`,\n  { headers: getAuthHeaders() }\n).then(r => r.json());\n\nfor (const cube of meta.cubes) {\n  console.log('Measures:', cube.measures.map(m => m.name));\n  console.log('Dimensions:', cube.dimensions.map(d => d.name));\n}\n```\n\nKnown dimensions: `DateCreated`, `OperatorResults`, `OperatorResult.Value`, `Channels`, `Languages`, `Conversation.AccountSid`\n\nKnown measures: `Conversation.Count`, `OperatorResult.Count`\n\n### Insights CANNOT\n- Return raw OperatorResult payloads — use Intelligence `GET \u002Fv3\u002FOperatorResults` for that\n- Write anything — read-only\n- Query in real-time — data mart has indexing lag vs. the live Intelligence API\n- Query domains other than `Conversations`\n\n## Related Resources\n\n- [Conversation Orchestrator Skill](..\u002Ftwilio-conversation-orchestrator\u002FSKILL.md) — Conversation Orchestrator setup: Memory Store, Conversation Configuration, capture rules, participant types\n- [Customer Memory Skill](..\u002Ftwilio-customer-memory\u002FSKILL.md) — Memory Store, profiles, traits, observations, Recall\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,49,55,70,83,90,102,109,121,163,169,174,209,215,220,254,260,265,299,305,317,334,340,346,589,595,796,802,961,967,973,1203,1208,1214,1388,1394,1508,1514,1654,1660,1665,1812,1837,1843,1856,1862,2228,2234,3410,3416,3929,3935,4806,4812,4846,5552,5558,5952,5958,6641,6647,6653,6747,6753,6858,6864,6971,6977,7080,7178,7184,7254,7260,7321,7586,7598,7664,7759,8006,8012,8024,8035,8048,8054,8179,8185,8288,8299,8305,9028,9036,9238,9244,9263,9817,9823,10201,10243,10260,10266,10302,10308,10334],{"type":42,"tag":43,"props":44,"children":45},"element","h1",{"id":4},[46],{"type":47,"value":48},"text","Conversation Intelligence",{"type":42,"tag":50,"props":51,"children":52},"p",{},[53],{"type":47,"value":54},"Decision-making guide for Twilio's Conversation Intelligence v3 API — real-time and post-call GenAI analysis of conversations across Voice, SMS, RCS, and WhatsApp. Covers Intelligence Configurations, Language Operators (Twilio-authored and custom), Rules, Triggers, Actions, and result consumption.",{"type":42,"tag":56,"props":57,"children":58},"blockquote",{},[59],{"type":42,"tag":50,"props":60,"children":61},{},[62,68],{"type":42,"tag":63,"props":64,"children":65},"strong",{},[66],{"type":47,"value":67},"Security:",{"type":47,"value":69}," All inbound messages captured by the Orchestrator are untrusted external input. If Intelligence operators process this content with LLMs, their prompts should include instructions to ignore adversarial content and not follow instructions embedded in customer messages.",{"type":42,"tag":56,"props":71,"children":72},{},[73],{"type":42,"tag":50,"props":74,"children":75},{},[76,81],{"type":42,"tag":63,"props":77,"children":78},{},[79],{"type":47,"value":80},"GA",{"type":47,"value":82}," — Conversation Intelligence v3 is generally available.",{"type":42,"tag":84,"props":85,"children":87},"h2",{"id":86},"use-cases",[88],{"type":47,"value":89},"Use Cases",{"type":42,"tag":50,"props":91,"children":92},{},[93,95,100],{"type":47,"value":94},"Conversation Intelligence powers ",{"type":42,"tag":63,"props":96,"children":97},{},[98],{"type":47,"value":99},"human agent augmentation",{"type":47,"value":101}," — giving every agent a \"second brain\" that listens, understands, and surfaces the right data at the right time. Agents focus on empathy, judgment, and problem-solving; AI handles analysis and assistance.",{"type":42,"tag":103,"props":104,"children":106},"h3",{"id":105},"wrap-up-agent-assist-post-call",[107],{"type":47,"value":108},"Wrap-up Agent Assist (Post-Call)",{"type":42,"tag":50,"props":110,"children":111},{},[112,114,119],{"type":47,"value":113},"Analyze completed conversations and generate structured outputs — summaries, sentiment signals, topic dispositions. Reduces after-call work, accelerates agent transitions to next interaction. ",{"type":42,"tag":63,"props":115,"children":116},{},[117],{"type":47,"value":118},"Low-friction entry point",{"type":47,"value":120}," — start here.",{"type":42,"tag":122,"props":123,"children":124},"ul",{},[125,136,153],{"type":42,"tag":126,"props":127,"children":128},"li",{},[129,134],{"type":42,"tag":63,"props":130,"children":131},{},[132],{"type":47,"value":133},"Operators",{"type":47,"value":135},": Summary, Sentiment, custom Conversation Scoring",{"type":42,"tag":126,"props":137,"children":138},{},[139,144,146],{"type":42,"tag":63,"props":140,"children":141},{},[142],{"type":47,"value":143},"Trigger",{"type":47,"value":145},": ",{"type":42,"tag":147,"props":148,"children":150},"code",{"className":149},[],[151],{"type":47,"value":152},"CONVERSATION_END",{"type":42,"tag":126,"props":154,"children":155},{},[156,161],{"type":42,"tag":63,"props":157,"children":158},{},[159],{"type":47,"value":160},"Integration",{"type":47,"value":162},": Webhook → CRM case note creation",{"type":42,"tag":103,"props":164,"children":166},{"id":165},"real-time-agent-assist",[167],{"type":47,"value":168},"Real-time Agent Assist",{"type":42,"tag":50,"props":170,"children":171},{},[172],{"type":47,"value":173},"Analyze conversations as they unfold. Surface sentiment shifts, script adherence signals, or recommended next responses enriched with customer history and enterprise knowledge. Agents respond more confidently without searching across systems.",{"type":42,"tag":122,"props":175,"children":176},{},[177,186,200],{"type":42,"tag":126,"props":178,"children":179},{},[180,184],{"type":42,"tag":63,"props":181,"children":182},{},[183],{"type":47,"value":133},{"type":47,"value":185},": Script Adherence, Next Best Response, Escalation Risk (custom)",{"type":42,"tag":126,"props":187,"children":188},{},[189,193,194],{"type":42,"tag":63,"props":190,"children":191},{},[192],{"type":47,"value":143},{"type":47,"value":145},{"type":42,"tag":147,"props":195,"children":197},{"className":196},[],[198],{"type":47,"value":199},"COMMUNICATION",{"type":42,"tag":126,"props":201,"children":202},{},[203,207],{"type":42,"tag":63,"props":204,"children":205},{},[206],{"type":47,"value":160},{"type":47,"value":208},": Webhook → Agent desktop overlay",{"type":42,"tag":103,"props":210,"children":212},{"id":211},"real-time-workflow-automation",[213],{"type":47,"value":214},"Real-time Workflow Automation",{"type":42,"tag":50,"props":216,"children":217},{},[218],{"type":47,"value":219},"Combine real-time intelligence with orchestration to trigger downstream workflows when specific conditions are met — escalate to supervisor, trigger fraud prevention, notify specialist.",{"type":42,"tag":122,"props":221,"children":222},{},[223,232,245],{"type":42,"tag":126,"props":224,"children":225},{},[226,230],{"type":42,"tag":63,"props":227,"children":228},{},[229],{"type":47,"value":133},{"type":47,"value":231},": Custom risk detection, compliance monitoring",{"type":42,"tag":126,"props":233,"children":234},{},[235,239,240],{"type":42,"tag":63,"props":236,"children":237},{},[238],{"type":47,"value":143},{"type":47,"value":145},{"type":42,"tag":147,"props":241,"children":243},{"className":242},[],[244],{"type":47,"value":199},{"type":42,"tag":126,"props":246,"children":247},{},[248,252],{"type":42,"tag":63,"props":249,"children":250},{},[251],{"type":47,"value":160},{"type":47,"value":253},": Webhook → Workflow engine \u002F TaskRouter",{"type":42,"tag":103,"props":255,"children":257},{"id":256},"contact-center-qa",[258],{"type":47,"value":259},"Contact Center QA",{"type":42,"tag":50,"props":261,"children":262},{},[263],{"type":47,"value":264},"Generate post-interaction summaries, sentiment scores, and compliance signals for QA, coaching, and analytics. Aggregate across interactions to support training and continuous optimization.",{"type":42,"tag":122,"props":266,"children":267},{},[268,277,290],{"type":42,"tag":126,"props":269,"children":270},{},[271,275],{"type":42,"tag":63,"props":272,"children":273},{},[274],{"type":47,"value":133},{"type":47,"value":276},": Script Adherence, Summary, custom Conversation Scoring",{"type":42,"tag":126,"props":278,"children":279},{},[280,284,285],{"type":42,"tag":63,"props":281,"children":282},{},[283],{"type":47,"value":143},{"type":47,"value":145},{"type":42,"tag":147,"props":286,"children":288},{"className":287},[],[289],{"type":47,"value":152},{"type":42,"tag":126,"props":291,"children":292},{},[293,297],{"type":42,"tag":63,"props":294,"children":295},{},[296],{"type":47,"value":160},{"type":47,"value":298},": Webhook → Analytics \u002F BI tools",{"type":42,"tag":84,"props":300,"children":302},{"id":301},"how-it-works",[303],{"type":47,"value":304},"How It Works",{"type":42,"tag":306,"props":307,"children":311},"pre",{"className":308,"code":310,"language":47},[309],"language-text","┌─────────────────────────────────────────────────────────────────────────────┐\n│  1. Customer engages agent (Voice, SMS, WhatsApp, RCS, Chat, Email)         │\n└─────────────────────────────────────────────────────────────────────────────┘\n                                      │\n                                      ▼\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  2. Conversations (Conversation Orchestrator) groups communications into a  │\n│     Conversation                                                            │\n│     - Normalizes channel events                                             │\n│     - Groups related messages\u002Futterances                                    │\n│     - Tracks participants (CUSTOMER, HUMAN_AGENT, AI_AGENT)                 │\n└─────────────────────────────────────────────────────────────────────────────┘\n                                      │\n                                      ▼\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  3. Conversation events trigger Intelligence rules                          │\n│     - COMMUNICATION: on each new message\u002Futterance                          │\n│     - CONVERSATION_END: when conversation closes                            │\n│     - CONVERSATION_INACTIVE: when conversation goes idle                    │\n└─────────────────────────────────────────────────────────────────────────────┘\n                                      │\n                                      ▼\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  4. Language Operators analyze the conversation                             │\n│     - Twilio-authored: Sentiment, Summary, NBR, Script Adherence            │\n│     - Custom: domain-specific analysis with your prompts                    │\n│     - Context: enriched with Customer Memory + Enterprise Knowledge         │\n└─────────────────────────────────────────────────────────────────────────────┘\n                                      │\n                                      ▼\n┌─────────────────────────────────────────────────────────────────────────────┐\n│  5. Results delivered via webhook + REST API                                │\n│     - Real-time: Agent desktop, workflow triggers                           │\n│     - Post-call: CRM notes, QA systems, analytics                           │\n│     - Aggregated: Conversational Insights for cross-conversation analysis   │\n└─────────────────────────────────────────────────────────────────────────────┘\n",[312],{"type":42,"tag":147,"props":313,"children":315},{"__ignoreMap":314},"",[316],{"type":47,"value":310},{"type":42,"tag":50,"props":318,"children":319},{},[320,325,327,332],{"type":42,"tag":63,"props":321,"children":322},{},[323],{"type":47,"value":324},"Key insight",{"type":47,"value":326},": Real-time and post-conversation intelligence use the ",{"type":42,"tag":63,"props":328,"children":329},{},[330],{"type":47,"value":331},"same underlying model",{"type":47,"value":333},". 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Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10414,10417,10420,10423,10426],{"name":10415,"slug":10416,"type":15},"Frontend","frontend",{"name":10418,"slug":10419,"type":15},"React","react",{"name":10421,"slug":10422,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":10424,"slug":10425,"type":15},"UI Components","ui-components",{"name":10427,"slug":10428,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":10431,"name":10431,"fn":10432,"description":10433,"org":10434,"tags":10435,"stars":25,"repoUrl":26,"updatedAt":10447},"ai-gateway","configure Vercel AI Gateway","Vercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10436,10439,10442,10443,10446],{"name":10437,"slug":10438,"type":15},"AI Infrastructure","ai-infrastructure",{"name":10440,"slug":10441,"type":15},"Cost Optimization","cost-optimization",{"name":13,"slug":14,"type":15},{"name":10444,"slug":10445,"type":15},"Performance","performance",{"name":10427,"slug":10428,"type":15},"2026-04-06T18:40:44.377464",{"slug":10449,"name":10449,"fn":10450,"description":10451,"org":10452,"tags":10453,"stars":25,"repoUrl":26,"updatedAt":10459},"ai-generation-persistence","implement persistence patterns for AI generations","AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10454,10455,10458],{"name":10440,"slug":10441,"type":15},{"name":10456,"slug":10457,"type":15},"Database","database",{"name":13,"slug":14,"type":15},"2026-04-06T18:41:08.513425",600,{"items":10462,"total":10657},[10463,10484,10507,10524,10538,10555,10574,10586,10600,10614,10626,10641],{"slug":10464,"name":10464,"fn":10465,"description":10466,"org":10467,"tags":10468,"stars":10481,"repoUrl":10482,"updatedAt":10483},"prior-auth-packet-builder","build healthcare prior authorization packets","Build a concise prior authorization packet from local case files and payer policy docs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10469,10472,10475,10478],{"name":10470,"slug":10471,"type":15},"Documents","documents",{"name":10473,"slug":10474,"type":15},"Healthcare","healthcare",{"name":10476,"slug":10477,"type":15},"Insurance","insurance",{"name":10479,"slug":10480,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":10485,"name":10485,"fn":10486,"description":10487,"org":10488,"tags":10489,"stars":10504,"repoUrl":10505,"updatedAt":10506},"aspnet-core","build ASP.NET Core web applications","Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10490,10493,10495,10498,10501],{"name":10491,"slug":10492,"type":15},".NET","dotnet",{"name":10494,"slug":10485,"type":15},"ASP.NET Core",{"name":10496,"slug":10497,"type":15},"Blazor","blazor",{"name":10499,"slug":10500,"type":15},"C#","csharp",{"name":10502,"slug":10503,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":10508,"name":10508,"fn":10509,"description":10510,"org":10511,"tags":10512,"stars":10504,"repoUrl":10505,"updatedAt":10523},"chatgpt-apps","build ChatGPT Apps SDK applications","Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10513,10516,10519,10522],{"name":10514,"slug":10515,"type":15},"Apps SDK","apps-sdk",{"name":10517,"slug":10518,"type":15},"ChatGPT","chatgpt",{"name":10520,"slug":10521,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":10525,"name":10525,"fn":10526,"description":10527,"org":10528,"tags":10529,"stars":10504,"repoUrl":10505,"updatedAt":10537},"cli-creator","build CLIs from API docs","Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read\u002Fwrite commands, return stable JSON, manage auth, and pair with a companion skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10530,10531,10534],{"name":20,"slug":21,"type":15},{"name":10532,"slug":10533,"type":15},"CLI","cli",{"name":10535,"slug":10536,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":10539,"name":10539,"fn":10540,"description":10541,"org":10542,"tags":10543,"stars":10504,"repoUrl":10505,"updatedAt":10554},"cloudflare-deploy","deploy projects to Cloudflare","Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10544,10547,10550,10551],{"name":10545,"slug":10546,"type":15},"Cloudflare","cloudflare",{"name":10548,"slug":10549,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":10396,"slug":10397,"type":15},{"name":10552,"slug":10553,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":10556,"name":10556,"fn":10557,"description":10558,"org":10559,"tags":10560,"stars":10504,"repoUrl":10505,"updatedAt":10573},"define-goal","define and set measurable project goals","Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10561,10564,10567,10570],{"name":10562,"slug":10563,"type":15},"Productivity","productivity",{"name":10565,"slug":10566,"type":15},"Project Management","project-management",{"name":10568,"slug":10569,"type":15},"Strategy","strategy",{"name":10571,"slug":10572,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":10575,"name":10575,"fn":10576,"description":10577,"org":10578,"tags":10579,"stars":10504,"repoUrl":10505,"updatedAt":10585},"figma","translate Figma designs into code","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10580,10581,10583,10584],{"name":10358,"slug":10359,"type":15},{"name":10582,"slug":10575,"type":15},"Figma",{"name":10415,"slug":10416,"type":15},{"name":10520,"slug":10521,"type":15},"2026-04-12T05:06:47.939943",{"slug":10587,"name":10587,"fn":10588,"description":10589,"org":10590,"tags":10591,"stars":10504,"repoUrl":10505,"updatedAt":10599},"figma-code-connect-components","connect Figma designs to code components","Connects Figma design components to code components using Code Connect mapping tools. Use when user says \"code connect\", \"connect this component to code\", \"map this component\", \"link component to code\", \"create code connect mapping\", or wants to establish mappings between Figma designs and code implementations. For canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10592,10593,10596,10597,10598],{"name":10358,"slug":10359,"type":15},{"name":10594,"slug":10595,"type":15},"Design System","design-system",{"name":10582,"slug":10575,"type":15},{"name":10415,"slug":10416,"type":15},{"name":10424,"slug":10425,"type":15},"2026-05-10T05:59:52.971881",{"slug":10601,"name":10601,"fn":10602,"description":10603,"org":10604,"tags":10605,"stars":10504,"repoUrl":10505,"updatedAt":10613},"figma-create-design-system-rules","generate design system rules from Figma","Generates custom design system rules for the user's codebase. Use when user says \"create design system rules\", \"generate rules for my project\", \"set up design rules\", \"customize design system guidelines\", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10606,10607,10608,10611,10612],{"name":10358,"slug":10359,"type":15},{"name":10594,"slug":10595,"type":15},{"name":10609,"slug":10610,"type":15},"Documentation","documentation",{"name":10582,"slug":10575,"type":15},{"name":10415,"slug":10416,"type":15},"2026-05-16T06:07:47.821474",{"slug":10615,"name":10615,"fn":10616,"description":10617,"org":10618,"tags":10619,"stars":10504,"repoUrl":10505,"updatedAt":10625},"figma-implement-design","translate Figma designs into application code","Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions \"implement design\", \"generate code\", \"implement component\", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10620,10621,10622,10623,10624],{"name":10358,"slug":10359,"type":15},{"name":10582,"slug":10575,"type":15},{"name":10415,"slug":10416,"type":15},{"name":10424,"slug":10425,"type":15},{"name":10502,"slug":10503,"type":15},"2026-05-16T06:07:40.583615",{"slug":10627,"name":10627,"fn":10628,"description":10629,"org":10630,"tags":10631,"stars":10504,"repoUrl":10505,"updatedAt":10640},"hatch-pet","create animated pets for Codex","Create, repair, validate, visually QA, and package Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker Codex pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10632,10635,10636,10639],{"name":10633,"slug":10634,"type":15},"Animation","animation",{"name":10535,"slug":10536,"type":15},{"name":10637,"slug":10638,"type":15},"Creative","creative",{"name":10358,"slug":10359,"type":15},"2026-05-02T05:31:48.48485",{"slug":10642,"name":10642,"fn":10643,"description":10644,"org":10645,"tags":10646,"stars":10504,"repoUrl":10505,"updatedAt":10656},"imagegen","generate and edit raster images","Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG\u002Fvector\u002Fcode-native assets, extending an established icon or logo system, or building the visual directly in HTML\u002FCSS\u002Fcanvas.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[10647,10648,10649,10652,10655],{"name":10637,"slug":10638,"type":15},{"name":10358,"slug":10359,"type":15},{"name":10650,"slug":10651,"type":15},"Image Generation","image-generation",{"name":10653,"slug":10654,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675]