Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/wittyreference/twilio-claude-plugin/conversational-intelligencenpx skills add wittyreference/twilio-claude-plugin --skill conversational-intelligencegit clone --depth 1 https://github.com/wittyreference/twilio-claude-pluginWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00014 | $0.03589 |
| Opus 5 | $0.00007 | $0.01795 |
| Sonnet 5 | $0.00003 | $0.00718 |
| Haiku 4.5 | $0.00001 | $0.00359 |
Grade B, and why
conversational-intelligence scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
const response = await fetch('https://intelligence.twilio.com/v3/ControlPlane/Operators', { method: 'POST', How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: conversational-intelligence description: Twilio Conversational Intelligence v3 development guide. Use when building real-time or post-call conversation analysis with Language Operators, creating custom operators, or choosing between Intelligence v2 and v3. Do NOT use for recording-based transcription (use Voice Intelligence v2 via /recordings skill). allowed-tools: mcp__twilio__*, Read, Grep, Glob
Twilio Conversational Intelligence v3
Real-time and post-conversation GenAI analysis engine. Processes conversations captured by Conversations Orchestrator (v2) using Language Operators — Twilio-authored or custom. Produces structured results (sentiment, summaries, classifications, extractions) delivered via webhook or queryable via API.
Generally available — HTTP-only (no SDK). Requires Conversations Orchestrator for conversation capture. AI model: OpenAI GPT-4o.
Evidence: Claims sourced from live testing (2026-03-26). MCP tool implementations verified in intelligence-v3.ts of the Twilio MCP server.
Scope
CAN
- Run Language Operators per-message (COMMUNICATION trigger) for real-time analysis (~4s latency)
- Run Language Operators post-conversation (CONVERSATION_END trigger) for full-context analysis
- Run Language Operators on inactivity (CONVERSATION_INACTIVE trigger)
- Use 4 Twilio-authored operators: Sentiment, Summary, Next Best Response, Script Adherence
- Create custom Language Operators with free-form prompts and structured output (TEXT, JSON, CLASSIFICATION, EXTRACTION)
- Deliver results via webhook (POST) with full execution context
- Query results via REST API (filter by configuration or conversation)
- Link up to 5 Intelligence Configurations per Conversation Configuration
- Define up to 5 rules per configuration, each with up to 5 operators and 2 webhook actions
- Use Enterprise Knowledge context (knowledge bases) in operator rules
- Access Intelligence-processed conversations read-only via v3 Conversations endpoint
CANNOT
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 267 lines · 14 tokens per session scan B f3594ba54acd
conversational-intelligence is a skill published in the GitHub repository wittyreference/twilio-claude-plugin (2 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 3,589 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (sends data to an external url). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…