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voice-agents

You are a voice AI architect who has shipped production voice agents handling millions of calls. You understand the physics of latency - every component adds milliseconds, and the sum determines whether conversations feel natural or awkward.

Quality

42%

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

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

SKILL.md
Quality
Evals
Security

Quality

Content

26%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 reads like a corrupted or unfinished stub: a truncated intro, a keyword list masquerading as capabilities, empty anti-pattern sections, and a Sharp Edges table whose Issue and Solution columns contain placeholder text. It provides almost no actionable guidance for a domain that needs concrete latency budgets, architecture decision criteria, and implementation steps.

Suggestions

Fill in the Sharp Edges table with real issues and concrete solutions (e.g., specific latency budget numbers per component, jitter thresholds, actual semantic-VAD and barge-in implementation approaches), and complete the truncated architecture paragraph.

Give each Pattern and Anti-Pattern at least a few lines of executable guidance — decision criteria for choosing S2S vs pipeline, and what to do instead of 'Silence-Only Turn Detection' or 'Long Responses'.

Remove the duplicated persona preamble and the boilerplate 'When to Use' sentence, and either write real content for the empty sections or move detailed material (e.g., per-provider comparison, latency measurement scripts) into reference files with clearly signaled links.

DimensionReasoningScore

Conciseness

The body is short and avoids explaining basics Claude already knows, but the opening persona paragraph duplicates the frontmatter description verbatim and the 'When to Use' line ('This skill is applicable to execute the workflow or actions described in the overview') is pure boilerplate filler.

3 / 5

Actionability

There are high-level hints ('Direct audio-to-audio processing for lowest latency', 'Separate STT → LLM → TTS for maximum control', 'Use semantic VAD:') but no steps, numbers, code, or commands; the Sharp Edges table's 'Solutions' column contains only colon-terminated labels like '# Measure and budget latency for each component:' with no actual guidance.

2 / 5

Workflow Clarity

No sequence or workflow exists anywhere — the architecture section is truncated mid-sentence ('but add latency. Mos'), the Anti-Patterns section is three headers with no content, and there are no validation or measurement checkpoints for a multi-step domain like voice-agent construction.

1 / 5

Progressive Disclosure

Section headers exist (Capabilities, Patterns, Anti-Patterns, Sharp Edges) but they are hollow placeholders with no substantive content and no references to deeper material — the bundle has no reference files at all, so there is nothing to navigate to.

2 / 5

Total

8

/

20

Passed

Description

31%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.

This is a persona preamble rather than a skill description: it sets a character and attitude but never says what the skill does or when to invoke it. It needs third-person capability verbs and an explicit 'Use when...' clause with natural trigger terms.

Suggestions

Rewrite in third person with concrete capability verbs, e.g., 'Designs and debugs production voice agents: architecture selection (speech-to-speech vs STT/LLM/TTS pipeline), latency budgeting, VAD, and barge-in handling.'

Add an explicit trigger clause such as 'Use when building voice agents, real-time speech interfaces, or telephony/conversational AI, or when the user mentions latency, barge-in, or turn-taking.'

Include natural synonyms and jargon users actually say (speech-to-speech, STT, TTS, realtime API, voice bot) to improve trigger coverage and distinctiveness from general LLM-agent skills.

DimensionReasoningScore

Specificity

The description names the voice-agent domain ("production voice agents", "physics of latency") but states no actions the skill performs at all, and its second-person framing ("You are a voice AI architect") triggers the rubric's 1-point penalty, dropping it from a base of 2 to 1. It is a persona statement, not a capability list.

1 / 5

Completeness

There is no 'Use when...' or equivalent trigger clause, which caps completeness at 3, and the 'what' is only a vague persona ("voice AI architect who has shipped production voice agents") rather than a statement of what the skill does — matching the 'vague what and no when' anchor.

2 / 5

Trigger Term Quality

"voice AI", "voice agents", "latency", and "calls" are natural terms a user might say, but common variations like speech-to-speech, STT/TTS, realtime, or barge-in are absent, matching the 'some relevant keywords but missing common variations' anchor.

3 / 5

Distinctiveness Conflict Risk

The voice-agent niche is somewhat specific, but with no capability or trigger language the persona framing could overlap with general agent-building or LLM-architecture skills (e.g., the referenced llm-architect), matching 'somewhat specific but could still overlap with similar skills'.

3 / 5

Total

9

/

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

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

plugins/AI-Agents-Safe-Coding-Skills-claude/skills/voice-agents/SKILL.md

Reviewed

Table of Contents

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