SKILLEMALL.ai

AD stt-simple

Local speech-to-text using OpenAI Whisper. Use when the user needs to: (1) transcribe audio files to text, (2) convert voice messages to written content, (3) process recordings in 99+ languages. Supports tiny/base/small/medium/large models. One-command installation with auto model download. Multi-Agent support with session isolation.

ClawHub Agent Skills author: Kuikui v1.0.2 MIT-0 4 files · 1 script body ≈ 1 711 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, consistency

GeneratorAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
D
48/100
Unfinished process
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 48/100

    • 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 (stt-simple) differs from the folder (sst-simple)
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 21 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 1711 tokens
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (7 tags): a typed call is more reliable

    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
    • -241 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 335: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 21 items
    • +4Has examples (15 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    This local transcription skill appears legitimate, but it needs Review because installation can change system packages and transcript files are not safely constrained to the documented output folder.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026