name: autonoannounce description: 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. metadata: {"openclaw":{"skillKey":"autonoannounce","homepage":"https://github.com/ironystock/autonoannounce","requires":{"env":["ELEVENLABS_API_KEY"],"bins":["python3","curl"],"anyBins":["mpv","ffplay","afplay","paplay","powershell"]},"primaryEnv":"ELEVENLABS_API_KEY"}}

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>"

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• 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.

这个 Skill 质量良好，完成度高，专门用于优化本地语音播报的响应速度和稳定性。它最大的优点是把文字转语音的工作放到后台异步处理，不会让用户等待；网络出问题时有多种降级方案，体验比较可靠。文档清晰易懂，性能目标明确。不过它依赖 ElevenLabs 云服务，网络不好或账户欠费时功能会受影响，而且只支持英文语音。整体而言，这是一个工程化程度较高、专为解决实际痛点设计的 Skill。