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AC voice-recognition

Intelligent speech-to-text using local OpenAI Whisper (no API key needed, fully private). Use when you need to transcribe audio files, convert voice messages to text, recognize spoken content, or process speech input in any of 99+ languages. Key differentiator: smart auto-model selection analyzes audio length and complexity to choose the optimal Whisper model — short clean clips use the fast base model, long or mixed-language clips automatically upgrade to small/medium for accuracy.

ClawHub Agent Skills author: 08Jacky04 v1.1.0 MIT-0 6 files body ≈ 1 571 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 60/100 · Has gaps — weak spots: when it triggers, consistency, running it twice

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
60/100
Has gaps
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (voice-recognition) differs from the folder (smart-voice-recognition)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 14 steps
    • 100Execution cost. Instruction body is 1571 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • -237 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 487: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 14 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This is a local speech-to-text skill, but its installer can unexpectedly modify the host Python environment and its offline/privacy claims are overstated.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026