wake-word-detection

Expert skill for implementing wake word detection with openWakeWord. Covers audio monitoring, keyword spotting, privacy protection, and efficient always-listening systems for JARVIS voice assistant.

Quality

58%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/wake-word-detection/SKILL.md

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a strong, actionable skill with excellent executable code examples and clear TDD workflow. The main weaknesses are verbosity in early sections (explaining concepts Claude knows) and the monolithic structure that could benefit from progressive disclosure to separate reference files. The performance patterns and security standards sections are particularly well-done with good/bad comparisons.

Suggestions

Remove or significantly condense sections 1-3 (Overview, Core Principles, Core Responsibilities) - these explain concepts Claude already understands and add ~50 lines of low-value content

Split detailed patterns (sections 6-8) into separate reference files (e.g., PATTERNS.md, PERFORMANCE.md) and keep only the primary SecureWakeWordDetector implementation in the main skill

Dimension	Reasoning	Score
Conciseness	The skill is reasonably efficient but includes some unnecessary sections like the overview explaining what wake word detection is and verbose explanations of principles Claude already understands. The 'Core Principles' and 'Core Responsibilities' sections could be condensed significantly.	2 / 3
Actionability	Excellent executable code examples throughout - complete Python implementations for the detector, tests, performance patterns, and security controls. All code is copy-paste ready with proper imports and realistic implementations.	3 / 3
Workflow Clarity	Clear TDD workflow with explicit steps (write failing test → implement minimum → verify). The pre-implementation checklist provides explicit validation checkpoints across three phases. Verification commands are concrete (pytest commands with coverage).	3 / 3
Progressive Disclosure	Content is well-organized with clear sections, but it's a monolithic document that could benefit from splitting detailed patterns into separate files. The 400+ lines of content would be better served with a concise overview linking to PATTERNS.md, TESTS.md, etc.	2 / 3
	Total	10 / 12 Passed

Description

40%

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 identifies a clear technical niche (openWakeWord for JARVIS) which provides good distinctiveness, but suffers from missing explicit trigger guidance and somewhat abstract capability descriptions. The lack of a 'Use when...' clause is a significant gap that would make it harder for Claude to know when to select this skill from a large pool.

Suggestions

Add a 'Use when...' clause with explicit triggers like 'Use when implementing wake word detection, setting up voice activation, configuring hotword recognition, or building always-listening features for JARVIS'.

Replace abstract terms like 'covers audio monitoring' with concrete actions like 'Configure microphone input streams, set detection thresholds, handle wake word callbacks'.

Include common user phrasings and synonyms like 'hotword', 'voice activation', 'hey jarvis', 'speech trigger' to improve trigger term coverage.

Dimension	Reasoning	Score
Specificity	Names the domain (wake word detection with openWakeWord) and lists some actions (audio monitoring, keyword spotting, privacy protection, always-listening systems), but these are more like feature areas than concrete specific actions like 'detect wake words', 'configure sensitivity thresholds', or 'train custom wake words'.	2 / 3
Completeness	Describes what the skill covers but completely lacks a 'Use when...' clause or any explicit trigger guidance. There's no indication of when Claude should select this skill, which per the rubric should cap completeness at 2, and since the 'when' is entirely missing, this scores a 1.	1 / 3
Trigger Term Quality	Includes relevant terms like 'wake word', 'openWakeWord', 'keyword spotting', 'voice assistant', and 'JARVIS', but misses common variations users might say like 'hotword', 'activation phrase', 'hey jarvis', 'voice activation', or 'speech detection'.	2 / 3
Distinctiveness Conflict Risk	The combination of 'openWakeWord', 'wake word detection', and 'JARVIS voice assistant' creates a very specific niche that is unlikely to conflict with other skills. The technology stack and use case are clearly defined.	3 / 3
	Total	8 / 12 Passed

Validation

75%

Warnings & errors only

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

Validation — 12 / 16 Passed

Validation for skill structure

Criteria	Description	Result
description_trigger_hint	Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...')	Warning
metadata_version	'metadata' field is not a dictionary	Warning
license_field	'license' field is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	12 / 16 Passed

Repository: martinholovsky/claude-skills-generator
Commit: 1086ef2

Reviewed: 4 months ago

Table of Contents

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