SKILLEMALL.ai
GitHub ↗ RU Sign in

BC openclaw-voice-bridge

Push-to-talk voice input via Snarling hardware button and USB mic. Snarling records audio, plugin transcribes via OpenAI Whisper, then spawns a subagent that answers and sends the result to the Snarling display via send_notification.

ClawHub Agent Skills author: snarflakes v1.2.0 MIT-0 8 files body ≈ 1 742 tokens Open the sourceclawhub.ai↗ analyzed 32 h ago

Push-to-talk voice input via Snarling hardware button and USB mic.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentAI and agentsInfrastructuretype 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
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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 · 0

✓ No critical or high findings

Files scanned: 7. 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 "type"
  • note frontmatter-key unknown frontmatter key "envVars"

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 18 mutating operations with no state check
  • 40Consistency. Frontmatter name (openclaw-voice-bridge) differs from the folder (voice-bridge)
  • 55Failures and branches. 1 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 28 steps
  • 100Execution cost. Instruction body is 1742 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 top-level sections: this looks like several domains in one skill

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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 233: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
The skill mostly matches its voice-control purpose, but it needs review because it can send caller-supplied local file paths to OpenAI and turns speech into agent actions.
LLM: suspicious (high) · 25 Jun 2026