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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".

68

Quality

82%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

64%

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, actionable skill with excellent executable code examples covering REST, file, WSS, and diarization workflows. Its main weaknesses are moderate verbosity (the diarization section is extensive and some sections are boilerplate) and the lack of error handling/validation guidance for operations like PII redaction. The feature availability table and gotchas section are particularly valuable additions.

Suggestions

Add error handling examples or validation steps, especially for redaction (verify PII was actually redacted) and for cases where unsupported features are passed to incompatible models.

Trim the diarization section — the per-word field table and groupby example could be moved to a separate reference file, keeping just the basic diarization example inline.

Remove or condense the 'Central product skills' section, which is boilerplate that doesn't add skill-specific value.

DimensionReasoningScore

Conciseness

Generally efficient with good use of tables and code examples, but includes some unnecessary explanation (e.g., the 'When to use this product' section rehashes routing logic Claude can infer, the 'Central product skills' section is boilerplate, and the diarization word-level example is quite lengthy). The feature availability table and gotchas are valuable, but overall could be tightened.

2 / 3

Actionability

Provides fully executable, copy-paste-ready code for REST URL transcription, REST file transcription, WSS streaming, and diarization with word-level timings. Parameters are concrete with specific valid values (e.g., redact=['pci', 'pii'], summarize='v2'). The gotchas section adds critical implementation-specific details.

3 / 3

Workflow Clarity

The quick-start examples are clear single-step operations, but there's no validation or error-handling guidance. For operations like redaction of PII or batch audio processing, there are no verification steps (e.g., checking that redaction actually occurred, handling API errors, validating response structure). The skill also doesn't address what to do when features are unsupported by a model.

2 / 3

Progressive Disclosure

References to external docs and related skills are well-organized in the 'API reference (layered)' and 'Related skills' sections. However, no bundle files are provided, so references to 'reference.md' and example files cannot be verified. The skill itself is quite long (~180 lines) and the diarization deep-dive section could arguably be split into a separate file.

2 / 3

Total

9

/

12

Passed

Description

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, includes natural trigger terms, clearly delineates both what the skill does and when to use it, and proactively distinguishes itself from adjacent skills. The boundary guidance for related skills is particularly strong, reducing conflict risk. The description is information-dense without being padded.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: summarize, topics, intents, sentiment, diarize, redact, detect_language, entity detection. Also specifies the exact endpoint (`/v1/listen`), REST and WSS methods with exact client paths.

3 / 3

Completeness

Clearly answers both 'what' (audio analytics overlays on Deepgram's /v1/listen endpoint covering summarize, topics, intents, sentiment, etc.) and 'when' (explicit 'Use when writing or reviewing Python code... that calls Deepgram audio analytics overlays'). Also includes boundary guidance distinguishing from related skills.

3 / 3

Trigger Term Quality

Excellent coverage of natural trigger terms users would say: 'diarize', 'summarize audio', 'sentiment from audio', 'redact PII', 'topic detection audio', 'audio intelligence', 'detect language audio'. These are realistic phrases a developer would use.

3 / 3

Distinctiveness Conflict Risk

Explicitly distinguishes itself from related skills (`deepgram-python-speech-to-text` for plain transcription, `deepgram-python-text-intelligence` for text analytics), creating clear boundaries. The niche of audio analytics overlays on the STT endpoint is well-defined and unlikely to conflict.

3 / 3

Total

12

/

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.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
deepgram/deepgram-python-sdk
Reviewed

Table of Contents

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