BF kesha-voice-kit
Local multilingual voice toolkit — speech-to-text (STT), text-to-speech (TTS), speaker diarization, and language detection, over a CLI or an MCP server. Runs entirely offline on Apple Silicon, Linux, and Windows. No API keys, no cloud. NVIDIA Parakeet TDT for STT across 25 European languages, Kokoro-82M + Vosk-TTS for TTS in 9 languages, plus macOS AVSpeechSynthesizer for ~180 system voices with zero install.
As a process F 43/100 · Will not run — References files that are not bundled: docs/mcp.md, docs/tts.md, docs/tts.md
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- The text references files that are not there: add them or drop the references.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
medium Dangerous commands
cmd-pipe-to-shell-known-hostREADME.md:31Pipe-to-shell installer from a well-known host (still executes remote code)curl -fsSL https://bun.sh/install | bash # or: brew install oven-sh/bun/bun
-
low Secrets in code
secret-high-entropy-tokenREADME.md:8High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)<a href="https://flakiness.io/Laputa/kesha-voice-kit"><img src="https://img.shields.io/endpoint?url=…
quoted
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: docs/mcp.md - warning
missing-refreference to a missing file: docs/tts.md - warning
missing-refreference to a missing file: docs/tts.md - note
frontmatter-keyunknown frontmatter key "emoji" - note
frontmatter-keyunknown frontmatter key "requires" - note
frontmatter-keyunknown frontmatter key "install"
Process rating: all ten parameters 43/100
- 0Tools and files. 3 referenced file(s) missing: docs/mcp.md, docs/tts.md, docs/tts.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 3 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3728 tokens
- 100Progress reporting. Reports progress
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 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
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 412: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (13 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.