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deepgram-python-audio-intelligence

Use when writing or reviewing Python code in this repo that calls Deepgram audio analytics overlays on `/v1/listen` - summarize, topics, intents, sentiment, diarize, redact, detect_language, entity detection. Same endpoint as plain STT but with analytics params. Covers both REST (`client.listen.v1.media.transcribe_url`/`transcribe_file`) and the WSS-supported subset (`client.listen.v1.connect`). Use `deepgram-python-speech-to-text` for plain transcription, `deepgram-python-text-intelligence` for analytics on already-transcribed text. Triggers include "diarize", "summarize audio", "sentiment from audio", "redact PII", "topic detection audio", "audio intelligence", "detect language audio".

72

Quality

87%

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SecuritybySnyk

Low

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SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A dense, code-first reference whose examples are executable and parameter details are exact, with a well-signaled layered pointer to external API references. Its weaknesses are modest: a duplicated per-word diarization walkthrough, gotchas that restate the availability table, and no error-recovery guidance for failed API calls.

Suggestions

Collapse the per-word speaker iteration into the dedicated diarization quick start and trim it from 'Quick start — REST with full analytics', which only needs to show that results fields (r.summary, r.topics, ...) are available.

Delete Gotcha 2 ('Sentiment / topics / intents / summarize / detect_language are REST-only') or reduce it to one clause pointing at the REST vs WSS table, since the table already states this.

Add a short error-handling note (e.g., catching API errors / checking response.metadata on failure) to give the request→parse sequence a validation checkpoint.

DimensionReasoningScore

Conciseness

Mostly lean — code-first sections, terse tables, and no explanation of concepts Claude already knows — but 'Quick start — REST with full analytics' and 'Quick start — diarization with word-level timings' both iterate words with getattr(w, 'speaker', None), and Gotcha 2 restates the REST/WSS table. Not a 5 because of this duplicated per-word speaker iteration; not a 3 because the padding is minor, not whole unnecessary explanations.

4 / 5

Actionability

Fully executable, copy-paste-ready code for REST URL, REST file, and WSS paths with concrete params ('summarize="v2"', 'redact=["pci", "pii"]', model='nova-3'), a per-word field table, exact response access paths, and named in-repo example/test files. Matches the top anchor; common cases are covered end-to-end.

5 / 5

Workflow Clarity

Each quick start is a clear implicit sequence (auth via DeepgramClient() → request with params → parse named response fields), and gotchas flag failure modes ('Diarization is noisy on short / low-quality audio'). Not a 5: there are no error-recovery or validation checkpoints (e.g., what to do on API error or unsupported param/model rejection); not a 3: sequences are complete and unambiguous for each path.

4 / 5

Progressive Disclosure

Good section structure with quick starts inline and bulk detail delegated via a clearly layered 'API reference (layered)' list (in-repo reference.md, OpenAPI, AsyncAPI, Context7, product docs) — all one level deep. Not a 5: no bundle files exist to verify reference.md against, and its path is given without a link or confirmation of location; not a 3: references are well signaled and content that belongs elsewhere (full API surface) is not inlined.

4 / 5

Total

17

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20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A highly specific, well-scoped description that states what the skill does, when to use it, which SDK surfaces it covers, and how it differs from sibling skills. The only gap is a few missing natural trigger synonyms (e.g., 'diarization', 'speaker labels'), which keeps trigger term quality just short of the top anchor.

DimensionReasoningScore

Specificity

Enumerates concrete capabilities ('summarize, topics, intents, sentiment, diarize, redact, detect_language, entity detection') and pins exact SDK entry points ('client.listen.v1.media.transcribe_url'/'transcribe_file', 'client.listen.v1.connect'), matching the comprehensive-coverage anchor. Not a 4: coverage of actions is complete for the domain with no minor gaps identified.

5 / 5

Completeness

Explicitly answers 'what' (analytics overlays on /v1/listen with named features and methods) and 'when' ('Use when writing or reviewing Python code in this repo that calls Deepgram audio analytics overlays'), with concrete trigger phrases appended. Matches the top anchor exactly; a 4 would require the 'when' to be less explicit.

5 / 5

Trigger Term Quality

Trigger list ('diarize', 'summarize audio', 'sentiment from audio', 'redact PII', 'topic detection audio', 'audio intelligence', 'detect language audio') covers natural phrasings users would say. Not a 5: common synonyms such as 'diarization', 'speaker separation/labels', or 'sentiment analysis audio' are absent; not a 3: coverage goes well beyond a few keywords.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (audio analytics on /v1/listen) and actively disambiguates against adjacent skills ('Use deepgram-python-speech-to-text for plain transcription, deepgram-python-text-intelligence for analytics on already-transcribed text'), minimizing wrong-skill triggering. Not a 4: overlap with the plain-STT skill is explicitly resolved by routing guidance rather than left as a minor risk.

5 / 5

Total

19

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20

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
deepgram/deepgram-python-sdk
Reviewed

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