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audio-transcriber

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

45

Quality

48%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/audio-transcriber/SKILL.md

The canonical home for this skill is audio-transcriber in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

42%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.

The body offers concrete bash commands for the mechanical transcription steps but is padded with mock-terminal output and placeholder Python, has broken step numbering, and fails to route readers to the existing bundle scripts and references. Batch/destructive operations also lack validation checkpoints.

Suggestions

Replace the long mock-terminal example output blocks (Examples 1/3/5) with concise expected-result summaries to cut token bloat.

Fix the step numbering (currently 0, 1, 3, 5) and add explicit validation checkpoints for batch processing — e.g., verify each transcript file exists and is non-empty before declaring success.

Reference the existing bundle files: point readers to scripts/transcribe.py for the transcription implementation and references/tools-comparison.md for engine selection, instead of inlining that logic.

DimensionReasoningScore

Conciseness

The 500+ line body is noticeably verbose: long mock-terminal emoji output blocks in Examples 1/3/5, placeholder Python functions (call_ai_model, cluster_by_topic, extract_action_items), and redundant explanation of concepts Claude already knows add padding that could be trimmed.

2 / 5

Actionability

Provides concrete, executable bash for discovery, validation, and format conversion, but the core summarization path is placeholder Python (call_ai_model is undefined) and step numbering jumps (0, 1, 3, 5), leaving key execution details incomplete.

3 / 5

Workflow Clarity

Steps are sequenced but with gaps (steps 2 and 4 are missing) and the batch-processing and file-overwriting operations lack validation/verification checkpoints; the rubric caps batch/destructive workflows without validation at 3.

3 / 5

Progressive Disclosure

Bundle files exist (scripts/transcribe.py, references/tools-comparison.md) but are never referenced or signaled from the body — content that belongs in those files (full transcription logic, tool comparison) is instead inlined, so references are present but not clearly signaled.

3 / 5

Total

11

/

20

Passed

Description

53%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.

The description conveys a clear purpose but lacks an explicit trigger ('when to use') clause and the natural keyword 'transcribe', capping completeness and trigger quality. It is specific enough to be distinct but not comprehensive enough to surface all capabilities.

Suggestions

Add an explicit 'Use when...' clause listing concrete triggers like 'Use when the user asks to transcribe audio, generate meeting minutes, or produce subtitles from recordings.'

Include natural trigger terms and file extensions (transcribe, transcription, speech-to-text, .mp3, .wav, .m4a) directly in the description rather than only in tags.

Mention the additional capabilities the body covers (speaker diarization, SRT/VTT subtitles, batch processing) to improve specificity and completeness.

DimensionReasoningScore

Specificity

Names the domain ('audio recordings' to 'Markdown documentation') and 1-2 concrete actions ('Transform...', 'intelligent summaries'), but omits meeting minutes, diarization, and subtitle generation that the body actually covers, so it is not comprehensive.

3 / 5

Completeness

Has a clear 'what' but no 'Use when...' clause or explicit trigger guidance; per the rubric a missing 'when' caps completeness at 3.

3 / 5

Trigger Term Quality

Includes relevant terms ('audio recordings', 'Markdown documentation', 'summaries', 'LLM') but omits the most natural user phrases such as 'transcribe'/'transcription' and file extensions like .mp3/.wav, which only appear in tags.

3 / 5

Distinctiveness Conflict Risk

The audio-to-Markdown transcription niche is mostly distinct with clear triggers; only minor overlap risk with general document or summarization skills.

4 / 5

Total

13

/

20

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (555 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

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