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grahama1970/analyze-chatterbox-emotions

評估生成的 Chatterbox 語音檔案作為語音品質製品:情感匹配、喚醒/效價代理、停頓位置、清晰度輸入、削波、響度及不連續性標記。用於審查 Chatterbox 情感標籤、停頓、Turbo/基礎情感傳遞、Persona Dream 語句渲染,或判斷生成的語音是否符合產品面向的情感要求。

analyze-chatterbox-emotions 是什麼?

這個技能專門評估由 Chatterbox 或 Persona Dream 生成的語音渲染檔案,將其視為語音品質製品進行多維度分析。它不僅檢查情感標籤是否準確,還透過聲學代理指標(如喚醒度與效價)來量化匹配度,並評估停頓位置、語速、清晰度、削波、響度及音訊不連續性等技術品質。支援多平台,包括 Chatterbox、Persona Dream 及動態對話腳本,並允許使用可選的渲染計畫或 ASR 轉錄來增強評估。評估結果以通過、審查或失敗的判定形式呈現,並基於加權分數(情感匹配 35%、清晰度 25%、韻律 20%、技術品質 20%)給出總體評分。此技能有明確界限:它不宣稱說話者真實情感,而是報告波形與請求目標的相容性;語音情感辨識模型為可選信號,即使不可用也能輸出代理指標。

相容平台~Claude Code~Codex CLI~Cursor✓Antigravity
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/analyze-chatterbox-emotions

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

analyze-chatterbox-emotions 是做什麼的?

將生成的 Chatterbox 渲染視為語音品質製品進行評估,而非關於說話者真實情感的主張。

當 Chatterbox 或 Persona Dream 渲染需要證明情感標籤、停頓、語速及音訊品質如預期運作時,使用此技能。

合約

輸入:

./run.sh analyze --audio path/to/render.wav \
  --expected-text "可選的原始文字" \
  --target-label reassuring \
  --target-arousal 0.35 \
  --target-valence 0.4 \
  --out /tmp/chatterbox_voice_eval.json \
  --report /tmp/chatterbox_voice_eval.md

輸出 JSON 架構:analyze_chatterbox_emotions.voice_eval.v1。

評估器報告以下項目:

  • affect:目標標籤加上聲學喚醒/效價代理及相容性評分。
  • prosody:時長、估計語速、RMS/響度代理、F0 估計值(如有)。
  • pauses:靜音比例、偵測到的停頓區段,以及當提供 Chatterbox 渲染計畫或對話輪次時的計畫與測量停頓比較。
  • quality:削波、峰值振幅、不連續性標記及低訊號警告。
  • intelligibility:預期文字的詞數及可選轉錄欄位;此技能不會自行發明 ASR。

可選輸入

  • --render-plan path.json:Chatterbox render_plan.json、Persona Dream 日誌收據、動態對話收據,或帶有 chatterbox_pause_plan 的對話輪次 JSON。
  • --transcript "文字":呼叫者提供的 ASR 轉錄。若省略,則轉錄相似度為 null,並列為非主張。

判定

  • pass:無重大技術失敗且分數 >= 75。
  • review:分數 50-74,或缺少重要證據(如無 ASR 轉錄)。
  • fail:重大技術問題、音訊無法使用、嚴重削波、缺少音訊,或分數 < 50。

分數是加權評估訊號,非真實值:

0.35 affect_match + 0.25 intelligibility + 0.20 prosody + 0.20 technical_quality

重要界限

  • 不要說該人物或角色「是悲傷/生氣/快樂」。應說明波形與可用分類器/代理與請求目標相容或不相容。
  • 語音情感辨識模型為可選訊號。若不可用,此技能仍會輸出聲學代理指標,並記錄 emotion_classifier.available=false。
  • 確切停頓盡可能從波形驗證;渲染計畫中的 pause_after_ms 僅為請求的停頓。

維護

執行:

./sanity.sh
../agentic-evals/run.sh run fixtures/agentic_eval.json --output /tmp/analyze-chatterbox-emotions-agentic-eval.json

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

grahama1970/acceptance-contract

Turn a client brief, zip bundle, directory, or single requirements file into a typed acceptance-contract bundle with extracted requirements, acceptance checks, open questions, an immutable-goal draft, and a create-report-backed decision report. Use when users say acceptance contract, brief to requirements, freeze the goal, create immutable goal, amend immutable goal, build a Battle requirements bundle, or extract requirements from this bundle.

grahama1970/agent-ecosystem

Canonical map and shared contracts for the agent-governance ecosystem: the pi.receipt_envelope.v1 boundary envelope, the component graph, and the rules for which component owns which schema. Use when wiring a skill or extension into the shared receipt world, when asking how shame, triage-error, tau, ask, project-watchdog, ops-herdr, ponytail, and Memory fit together, or when validating an envelope.

grahama1970/agentic-evals

Agentic evaluation of skills using multi-trial fixtures, deterministic command assertions, trajectory checks, safety constraints, and evidence-backed readiness scoring. Use when users ask for agentic evals, multi-trial skill evaluation, skill trajectory validation, or readiness scoring for a skill workflow.

grahama1970/agent-inbox

File-based inter-agent messaging with headless dispatch. Check inbox, send bugs/requests to other projects, automatically spawn headless agents to fix bugs, and track progress via task-monitor.

grahama1970/agents-registry

Generate and query the centralized agent identity registry. Scans .pi/agents/*/AGENTS.md, parses frontmatter, outputs agents-registry.json and optionally syncs to /memory for semantic search.

grahama1970/agent-status

Artifact-driven status surfaces for long-running project-agent work. Maintains status.json, events.jsonl, proof manifests, and a stale-aware STATUS.html so humans can tell where the agent is, what passed, what is still unproven, and what decision or action is next — without dashboard theater.

grahama1970/align

Round-based context alignment before execution. Use when the human, project agent, WebGPT, scillm, ask, dogpile, memory, or project-knowledge may each hold different facts about a task; especially before ambiguous design, infographic, product workflow, high-stakes implementation, plan-iterate, project-infographic, or multi-review work.

grahama1970/analytics

Flexible data science analytics for any dataset. Auto-discovers schema, recommends charts, exports to create-figure. Works with JSONL, JSON, CSV from any source.

grahama1970/analyze-elf

Reverse-engineer features from ELF binaries. Extracts CLI commands, state machines, protocols, Zod schemas, and data models. Automatically generates a /create-walkthrough prosecution brief with Mermaid diagrams. Uses /treesitter for AST analysis of bundled JS/TS source.

grahama1970/animation-vocabulary

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

grahama1970/anonymize-data

Anonymize supported CSV, JSON, UTF-8 text, and SQLite files using an explicit policy through the oai-trial project. Use for anonymize data, pseudonymize exports, redact policy literals, or discover and explicitly approve fuzzy name aliases. The skill is a thin CLI/Docker interface, not another engine.

grahama1970/anvil

Heavy-duty "No-Vibes" debugging and hardening orchestrator. Use this for complex, stubborn bugs where `review-code` has failed, or for "Red Teaming" (hardening) a codebase. Runs multiple agents in parallel (Thunderdome) using git worktree isolation.

grahama1970/apple-design

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

grahama1970/argue

Multi-persona structured debate orchestrator. Personas research via /dogpile, consult colleagues via /ask, and argue toward nuanced synthesis on complex questions.

grahama1970/arxiv

Search arXiv for papers and extract knowledge into memory. Use `search` to find papers, `learn` to extract knowledge.

grahama1970/ask

Use when the user asks to query project memory, ask an oracle, use supported browser-backed reviewers, run Tau roundtable/single-handler workflows, ask Pi-native subagents from within Pi, run persona/deep-review workflows, generate image prompts, check OS/project health through composed skills, or run an ask DAG. This skill is the executable /ask runtime; do not replace it with an informal subagent, plain web search, or hand-written review; inside Pi, explicit Pi-native subagent targets are routed through the pi-subagents tool as an Ask target type.

grahama1970/assess

Step back and critically reassess project state. Use when asked to "assess", "step back", "fresh eyes", "check alignment", "sanity check", "health check", "prune documentation", or "evaluate what's working". Offers documentation pruning and doc-code alignment analysis. Offer to run after major changes (don't auto-run).

grahama1970/assistant

Shared GPT + classifier inference gateway for persona monitor tasks. Routes validation and classification through a 4-tier cascade: heuristic → classifier → local GPT → scillm.

grahama1970/assistant-lab

Self-improvement workbench for /assistant. All the tools needed to diagnose, train, evaluate, and promote models in a continuous loop. The "warm pond" where /assistant evolves its own inference stack.

grahama1970/batch-quality

Pre-flight validation and quality gates for batch LLM operations. ACTUALLY tests samples through LLM before burning tokens. Uses SPARTA contracts for DuckDB validation queries. Integrates with task-monitor for enforced quality gates.

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