deepgram-js-audio-intelligence
Use when writing or reviewing JavaScript/TypeScript in this repo that calls Deepgram audio analytics overlays on `/v1/listen` - summarize, topics, intents, sentiment, diarize, redact, detect_language, and entity detection. Same endpoint as plain STT, different params. Covers REST via `client.listen.v1.media.transcribeUrl` / `transcribeFile` and the WebSocket-supported subset on `client.listen.v1.createConnection()` / `connect()`. Use `deepgram-js-speech-to-text` for plain transcription and `deepgram-js-text-intelligence` for analytics on already-transcribed text. Triggers include "audio intelligence", "summarize audio", "diarize", "sentiment from audio", "redact PII", and "detect language audio".
71
86%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that is highly specific, well-scoped, and clearly distinguishable from related skills. It provides concrete API methods, explicit trigger terms, and clear disambiguation guidance. The 'Use when' clause is present and the description efficiently communicates both what the skill does and when to select it.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and features: summarize, topics, intents, sentiment, diarize, redact, detect_language, entity detection. Also specifies exact API methods like `client.listen.v1.media.transcribeUrl`, `transcribeFile`, and `createConnection()`/`connect()`. | 3 / 3 |
Completeness | Clearly answers both 'what' (audio analytics overlays on /v1/listen including summarize, topics, intents, sentiment, etc.) and 'when' (explicit 'Use when' clause at the start, plus explicit trigger terms and disambiguation guidance for related skills). | 3 / 3 |
Trigger Term Quality | Includes a rich set of natural trigger terms: 'audio intelligence', 'summarize audio', 'diarize', 'sentiment from audio', 'redact PII', 'detect language audio'. Also mentions related skills by name for disambiguation, which helps with routing. | 3 / 3 |
Distinctiveness Conflict Risk | Explicitly distinguishes itself from `deepgram-js-speech-to-text` (plain transcription) and `deepgram-js-text-intelligence` (analytics on already-transcribed text), creating a clear niche for audio analytics overlays specifically. The trigger terms are highly specific to this domain. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, well-organized skill that provides actionable code examples and clear feature availability guidance for Deepgram audio intelligence. Its main strengths are the executable REST/WSS examples and the practical gotchas section. Minor weaknesses include some unnecessary cross-referencing to other skills and lack of error handling or response validation examples.
Suggestions
Add a brief example showing expected response structure or how to verify that intelligence features (e.g., sentiment, topics) are present in the response object.
Trim the 'Central product skills' section to a single line reference — the npm install instructions and explanation are not essential for this skill's purpose.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Generally efficient but includes some unnecessary content like the 'Central product skills' section and the lengthy product docs links list. The feature availability table and gotchas are valuable, but the 'When to use this product' section with cross-references to other skills adds moderate bloat. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples for both REST and WSS paths, with concrete parameter names, real URLs, and specific SDK method calls. The authentication setup, quick start examples, and parameter listings give Claude everything needed to write working code. | 3 / 3 |
Workflow Clarity | The skill clearly separates REST vs WSS paths and provides a feature availability table, but there's no validation or verification step after making API calls — no example of checking response structure, handling errors, or verifying that intelligence features returned expected data. For an API integration skill this is acceptable but not exemplary. | 2 / 3 |
Progressive Disclosure | Well-structured with clear sections progressing from overview to quick starts to detailed parameters to references. The API reference section provides layered one-level-deep references (in-repo, OpenAPI, AsyncAPI, Context7, product docs) with clear signaling. Example files are listed for further exploration. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- deepgram/deepgram-js-sdk
- Commit
bcffba7
- Reviewed
Table of Contents
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