BB deapi-audio
Text-to-speech, voice cloning, voice design, and transcribe audio files via deAPI GPU network. Trigger on 'text to speech', 'TTS', 'generate voice', 'read aloud', 'voice clone', 'clone voice', 'voice design', 'design voice', 'custom voice', 'transcribe audio', 'STT'. For video/YouTube transcription use deapi-video instead.
As a process B 73/100 · Nearly there — weak spots: inputs and preconditions, progress reporting
How to improve
- 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 · 10
✓ No critical or high findings
Medium and low: 10
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low Secrets in code
secret-high-entropy-tokenreferences/api.md:56High-entropy token-like string (may be an id, hash or a credential)### Model: Qwen…ice
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low Secrets in code
secret-high-entropy-tokenreferences/api.md:93High-entropy token-like string (may be an id, hash or a credential)### Model: Qwen…ign (Voice Design)
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low Secrets in code
secret-high-entropy-tokenreferences/api.md:106High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"model": "Qwen…ign",
quoted -
low Secrets in code
secret-high-entropy-tokenreferences/voices.md:102High-entropy token-like string (may be an id, hash or a credential)- **Qwen…ice** — custom voice TTS (`--model Qwen3` in text-to-speech.sh)
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low Secrets in code
secret-high-entropy-tokenreferences/voices.md:104High-entropy token-like string (may be an id, hash or a credential)- **Qwen…ign** — voice design (voice-design.sh)
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low Secrets in code
secret-high-entropy-tokenscripts/text-to-speech.sh:46High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)MODEL="Qwen…ice"
quoted -
low Secrets in code
secret-high-entropy-tokenscripts/text-to-speech.sh:73High-entropy token-like string (may be an id, hash or a credential)Qwen…ice)
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low Secrets in code
secret-high-entropy-tokenscripts/voice-design.sh:46High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)echo "Model: Qwen…ign | Lang: $LANG" >&2
quoted -
low Secrets in code
secret-high-entropy-tokenscripts/voice-design.sh:53High-entropy token-like string (may be an id, hash or a credential)\"model\": \"Qwen…ign\",
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low Secrets in code
secret-high-entropy-tokenscripts/voice-design.sh:77High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)--arg model "Qwen…ign" \
detector
Files scanned: 9. 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 73/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 55Failures and branches. 1 branches
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1010 tokens
- 100Running it twice. No mutating operations
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)
- -31 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 324: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 15 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.