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BC voice-assistant

Windows voice companion for OpenClaw. Custom wake word via Porcupine, local STT via faster-whisper, streamed responses over the gateway WebSocket, and ElevenLabs TTS with natural chime/thinking sounds. Supports multi-turn conversation with automatic follow-up listening, mic suppression to prevent feedback, and a system tray with pause/resume. Recommended voices: Matilda (XrExE9yKIg1WjnnlVkGX, free tier) or Ivy (MClEFoImJXBTgLwdLI5n, paid tier). Fully customizable wake word, voice, hotkey, and silence thresholds.

modbender/skill-library-mcp Agent Skills author: modbender MIT 12 files body ≈ 1 020 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Windows voice companion for OpenClaw.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

IntegrationSoftware developmenttype 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
B
88/100
safety, quality, tests
Safety 60%
98
Quality 40%
73
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration read-dotenv SKILL.md:53
    Reads a .env file
    # 3. Copy .env.example to .env and fill in your keys
  • low Exfiltration read-dotenv SKILL.md:54
    Reads a .env file
    copy .env.example .env

Files scanned: 12. 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")

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 40Consistency. Frontmatter name (voice-assistant) differs from the folder (openclaw-voice-assistant)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 14 steps
  • 100Execution cost. Instruction body is 1020 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
  • -31 of 3 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 517: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.