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azure-ai-transcription-py

Azure AI Transcription SDK for Python. Use for audio-file transcription with timestamps; distinguish this client from streaming and asynchronous batch Speech APIs.

66

Quality

83%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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 tight, well-structured skill with a genuinely executable code example and good safety posture around credentials and consent. Its main weaknesses are the absence of any code showing how to retrieve the promised timestamped segments, a vague error-handling directive, and one line of revision-history noise that should be trimmed.

Suggestions

Add a short example accessing the timestamped segments of `result` (e.g., iterating result's segments and their start/duration) — the description promises timestamps but the body never shows how to get them.

Replace the comment "handle empty output and service errors explicitly" with a concrete pattern, such as the specific exception type to catch from the SDK and a retry or empty-result check.

Delete the historical line "The previous begin_transcription and begin_stream_transcription examples did not match this client" — it describes prior revisions of the skill, not guidance for the current task.

DimensionReasoningScore

Conciseness

The body is lean with well-scoped sections and no explanation of concepts Claude already knows, but a few tokens don't earn their place — e.g., "The previous begin_transcription and begin_stream_transcription examples did not match this client" is historical editorial about past revisions, not guidance. Fits 'efficient; minor instances of over-explanation that could be trimmed' rather than the fully lean anchor 5.

4 / 5

Actionability

The code example is complete and executable (imports, env-based credentials, transcribe call with locales), matching 'concrete code or commands with minor gaps'. Not a 5: the description's core promise — timestamped output — is never demonstrated (no code accesses timestamps on `result`), and error handling is a comment ("handle empty output and service errors explicitly") rather than shown, so common cases are not fully covered.

4 / 5

Workflow Clarity

For a simple single-action skill the flow (configure env → open audio → transcribe → inspect result) is unambiguous via the code, but checkpoints are only implicit: "Inspect result locally; handle empty output and service errors explicitly" names validation without any concrete check or retry pattern. This is 'clear sequence with most checkpoints present; minor validation gaps' — anchor 4, not the fully unambiguous simple-skill 5.

4 / 5

Progressive Disclosure

The skill is under 50 lines with no bundle files, and per the rubric's simple-skill guidance it scores 5 with well-organized sections — which it has (When to Use, Prerequisites, File Example, Authentication, Example Request, Limitations). The single external link (Microsoft's SDK guide) is clearly signaled and one level deep.

5 / 5

Total

17

/

20

Passed

Description

82%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 strong description that clearly states what the skill covers, when to use it, and — unusually well — explicitly separates this client from the streaming and batch Speech APIs it could be confused with. The only meaningful gap is coverage of common user synonyms like 'speech-to-text'.

DimensionReasoningScore

Specificity

Names the domain ("Azure AI Transcription SDK for Python") and one concrete action ("audio-file transcription with timestamps"), which matches the anchor for 1-2 concrete actions without comprehensive coverage. It is not a 4 because 'distinguish this client from streaming and asynchronous batch Speech APIs' is a boundary statement, not an additional capability.

3 / 5

Completeness

Explicitly answers both: what ("Azure AI Transcription SDK for Python") and when ("Use for audio-file transcription with timestamps") with a concrete trigger phrase. Not below 5 because the 'Use for...' clause is explicit and specific, satisfying the judging guideline's requirement for explicit trigger guidance.

5 / 5

Trigger Term Quality

Good natural keyword coverage: "transcription", "audio-file", "timestamps", "Python", "Speech" are phrases a user would plausibly say. Not a 5 because common synonyms like "speech-to-text", "captions", or audio extensions (.wav, .mp3) are missing.

4 / 5

Distinctiveness Conflict Risk

Clear niche (this specific transcription client) with distinct triggers, and it actively disambiguates: "distinguish this client from streaming and asynchronous batch Speech APIs". Minimal conflict risk with adjacent Speech skills.

5 / 5

Total

17

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

sickn33/agentic-awesome-skills

Path

skills/azure-ai-transcription-py/SKILL.md

Reviewed

Table of Contents

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