autonoannounce

Build, operate, and troubleshoot Autonoannounce local speaker text-to-speech using the queued pipeline (enqueue to worker to ElevenLabs to playback backend). Requires ELEVENLABS_API_KEY for network synthesis, with optional ELEVENLABS_VOICE_ID and ELEVENLABS_MODEL_ID for voice/model selection. Writes local runtime state to config/tts-queue.json, .openclaw/*, and audio/earcons/*. Uses https://api.elevenlabs.io for synthesis/preflight calls. Use when creating or improving low-latency fire-and-forget TTS flows, tuning burst behavior, validating queue performance, enforcing local-only speech policy, or debugging queue/worker playback failures.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

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Install skill "autonoannounce" with this command: npx skills add ironystock/autonoannounce

Local TTS Queue

Requirements

  • Required credential: ELEVENLABS_API_KEY
  • Recommended env vars: ELEVENLABS_VOICE_ID, ELEVENLABS_MODEL_ID
  • Runtime tools: python3, curl, and one local playback backend (mpv, ffplay, afplay, paplay, or PowerShell sound player)
  • Network destination for synthesis/preflight: https://api.elevenlabs.io

Overview

Use this skill to keep local speech fast, reliable, and policy-compliant by treating enqueue as fire-and-forget and isolating synthesis/playback inside the queue worker.

Quick start workflow

  1. Confirm queue health with scripts/tts-queue-status.sh.
  2. Enqueue speech with scripts/speak-local-queued.sh "text".
  3. If audio does not play, inspect worker logs and runbook steps in references/runbook.md.
  4. For latency tuning, run scripts/benchmark-autonoannounce.sh and compare against SLOs in references/perf-slos.md.

Operating rules

  • Keep the producer path non-blocking: enqueue then return immediately.
  • Keep synthesis/playback in worker-only execution paths.
  • Prefer fewer larger writes to the queue (coalesce bursty traffic when possible).
  • Use policy-safe output lanes: local speaker for protected users; no Discord voice-file fallback.
  • Treat one failed item as isolated: retry with bounds, then dead-letter; do not stall entire queue.

Commands

  • Enqueue: scripts/speak-local-queued.sh "<text>"
  • Worker (foreground): scripts/tts-queue-worker.sh
  • Worker daemon: scripts/tts-queue-daemon.sh
  • Status: scripts/tts-queue-status.sh
  • Benchmark harness: scripts/benchmark-autonoannounce.sh
    • Fast foreground benchmark: scripts/benchmark-autonoannounce.sh 5
    • Full diagnostic benchmark: scripts/benchmark-autonoannounce.sh 5 --status both --output full
  • First-run interactive setup: skills/autonoannounce/scripts/setup-first-run.sh (shell wrapper)
  • Cross-platform first-run CLI: skills/autonoannounce/scripts/setup_first_run.py
  • Backend detection (OS-aware): skills/autonoannounce/scripts/backend-detect.sh
  • ElevenLabs capability preflight: skills/autonoannounce/scripts/elevenlabs-preflight.sh (includes short 429 retry/backoff for SFX probe)
  • Earcon library manager (durable categories/cache): skills/autonoannounce/scripts/earcon-library.sh
  • Cross-platform playback runner: skills/autonoannounce/scripts/play-local-audio.sh
  • Playback backend/device probe: skills/autonoannounce/scripts/playback-probe.sh
  • Playback backend startup validator: skills/autonoannounce/scripts/playback-validate.sh
  • Playback confirmation tone: skills/autonoannounce/scripts/playback-test.sh
  • v0.2 smoke tests: skills/autonoannounce/scripts/test-v0.2.sh
  • Race/concurrency stress checks: skills/autonoannounce/scripts/race-stress.sh

References map

  • Runbook: references/runbook.md
  • Config contract: references/config-contract.md
  • Performance SLOs and interpretation: references/perf-slos.md
  • Foreground-latency optimization: references/front-path-optimization.md
  • Durable earcon categories and cache: references/earcon-library.md

Execution checklist

  • Verify prerequisites (ELEVENLABS_API_KEY, ELEVENLABS_VOICE_ID, mpv).
  • Validate queue paths and lock behavior before tuning performance.
  • Measure baseline before making queue/worker changes.
  • Re-run benchmark after each material change.
  • Record final p50/p95 latency and queue-wait deltas in the task summary.

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