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CC qwen3-tts

High-quality text-to-speech using Qwen3-TTS. 10 built-in speakers with emotional instruct control, voice cloning (3s of audio), natural-language voice design, 10+ languages, persistent named voices, and delivering audio via Telegram/WhatsApp as native voice messages. Auto-detects GPU hardware (CUDA, ROCm, Intel XPU, CPU).

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 24 files · 7 scripts body ≈ 3 252 tokens Open the sourcegithub.com↗ analyzed 2 d ago

High-quality text-to-speech using Qwen3-TTS.

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

ProcedureTelegramWhatsAppAI 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
C
65/100
safety, quality, tests
Safety 60%
62
Quality 40%
69
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Consistency w 8
40
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

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.

For the author

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

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 4

  • high Dangerous commands cmd-pipe-to-shell README.md:15
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    bash <(curl -fsSL https://raw.githubusercontent.com/daMustermann/claw…tts/main/install.sh)
  • high Dangerous commands cmd-pipe-to-shell SKILL.md:39
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    bash <(curl -fsSL https://raw.githubusercontent.com/daMustermann/claw…tts/main/install.sh)
Medium and low: 2
  • low Dangerous commands cmd-pipe-to-shell-known-host install.sh:4
    Pipe-to-shell installer from a well-known host (still executes remote code) (code comment)
    #   or:  curl -fsSL https://raw.githubusercontent.com/daMustermann/claw…tts/main/install.sh | bash
    comment
  • low Exfiltration exfil-webhook-url server/messaging/telegram_sender.py:64
    Webhook / callback URL commonly used for exfiltration (verify the destination) (the skill's own vendor host; quoted — discussed, not commanded)
    url = f"https://api.telegram.org/bot{bot_token}/sendVoice"
    vendor-hostquoted

Files scanned: 24. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "repository"
  • note frontmatter-key unknown frontmatter key "requires"

Process rating: all ten parameters 57/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (qwen3-tts) differs from the folder (claw-qwen3-tts)
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Failures and branches. 4 branches
  • 85Steps. 43 steps, 1 vague phrases
  • 100Execution cost. Instruction body is 3252 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (3 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)
  • -2localhost URLs: will not work for another user
  • -32 of 6 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 323: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 43 items
  • +3Output format is stated explicitly
  • +4Has examples (17 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.