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voice-ai-engine-development

Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support

52

Quality

58%

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SecuritybySnyk

Low

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/voice-ai-engine-development/SKILL.md

The canonical home for this skill is voice-ai-engine-development in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

57%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 content is rich with executable code and a clear implementation sequence, but it is verbose in places and structurally misaligned: dedicated reference files exist yet go unreferenced while their content is inlined. Validation within the build workflow is implicit rather than checkpointed.

Suggestions

Link the existing references/common_pitfalls.md and references/provider_comparison.md from the body and move the inlined pitfalls and provider lists into those files instead of duplicating them.

Trim concept re-explanations Claude already knows (producer-consumer basics, 'Why streaming?'/'Why rate limiting?' prose) to tighten token efficiency.

Add explicit validation checkpoints to the Implementation Workflow (e.g., 'Unit-test each worker before wiring the pipeline; only proceed when tests pass') to turn it into a feedback-loop sequence.

DimensionReasoningScore

Conciseness

The ~700-line body is mostly efficient and code-heavy, but padded 'Why Streaming Responses?', 'Why Rate Limiting?', and 'Producer-Consumer with Queues' sections re-explain basic async concepts Claude already knows; not a 4 because those over-explanations are clearly trimmable, not a 2 because most code earns its place.

3 / 5

Actionability

Concrete, near copy-paste-ready code is provided throughout (BaseWorker, transcriber/agent/synthesizer interfaces, factory pattern, websocket endpoint, rate-limiting loop, interrupt system, tests); not a 5 because several snippets are interface scaffolding with NotImplementedError bodies rather than fully executable implementations.

4 / 5

Workflow Clarity

The 11-step 'Implementation Workflow' is clearly sequenced and includes testing steps, but validation checkpoints are implicit and no fail→fix→retry feedback loop is wired into the sequence; fits the 'steps listed but checkpoints missing or implicit' anchor rather than 4.

3 / 5

Progressive Disclosure

The body has good section structure, but bundle files references/common_pitfalls.md and references/provider_comparison.md exist and are never linked from the body, while overlapping pitfalls and provider lists are inlined; matches 'references present but not clearly signaled; content that should be separate is inline.'

3 / 5

Total

13

/

20

Passed

Description

58%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, specific capability set for voice AI engines but lacks an explicit 'Use when...' trigger clause and several natural synonyms users would actually say. It is distinct enough to avoid most conflicts but reads as a single packed sentence.

Suggestions

Append an explicit 'Use when...' clause naming trigger phrases (e.g., 'Use when building real-time voice assistants, voice bots, or conversational AI with speech-to-text and text-to-speech').

Add natural synonyms users say, such as 'speech-to-text', 'text-to-speech', and 'voice assistant', alongside the existing 'transcription'/'TTS' jargon.

Split the run-on description into a concise capability list so each action is distinct and scannable.

DimensionReasoningScore

Specificity

The description lists several concrete capabilities ('async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support'), matching the 'several specific actions; minor gaps' anchor; not a 5 because it is one run-on sentence rather than a crisp comprehensive action list.

4 / 5

Completeness

A clear 'what' is present, but there is no 'Use when...' clause or equivalent explicit trigger guidance, so completeness is capped at 3 per the rubric guideline; it is not a 4 because the 'when' is entirely absent rather than weakly implied.

3 / 5

Trigger Term Quality

It surfaces relevant terms ('voice engines', 'conversational AI', 'TTS', 'transcription') but omits common natural synonyms users say ('speech-to-text', 'text-to-speech', 'voice assistant', 'voice bot'), fitting the 'some relevant keywords but missing common variations' anchor.

3 / 5

Distinctiveness Conflict Risk

The 'real-time conversational AI voice engines' framing is a clear niche with distinct triggers and only minor overlap risk with general LLM/agent skills; not a 5 because broad terms like 'LLM agents' and 'multi-provider support' still risk some overlap.

4 / 5

Total

14

/

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 (725 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

Table of Contents

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