SKILLEMALL.ai
GitHub ↗ RU Sign in

AC mlx-whisper

Set up mlx-whisper as the local audio transcription engine for OpenClaw on Apple Silicon Macs (M1/M2/M3/M4). Automatically transcribes voice notes sent via Telegram or WhatsApp before the agent processes them. Use when the user wants to enable voice message transcription locally without any API key, or asks to set up Whisper, mlx-whisper, or local speech-to-text in OpenClaw. Apple Silicon only (macOS darwin). Note: Requires internet for initial model download (~465MB), but runs inference locally.

ClawHub Agent Skills author: YinghaoJia v1.0.7 MIT-0 4 files body ≈ 824 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationTelegramWhatsAppInfrastructureAI and agentstype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 4. 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 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (mlx-whisper) differs from the folder (jimmy-claw-mlx-whisper)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 10 steps
    • 100Execution cost. Instruction body is 824 tokens

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 501: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 10 items
    • +4Has examples (9 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This appears to be a local voice-note transcription skill whose sensitive behavior is aligned with its stated purpose, with only documentation clarity issues to consider.
    LLM: benign (medium) · VirusTotal: · 29 May 2026