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grahama1970/analyze-chatterbox-emotions

评估生成的 Chatterbox 语音文件作为语音质量制品:情感匹配度、唤醒度/效价代理、停顿位置、可懂度输入、削波、响度以及不连续性标志。用于审查 Chatterbox 情感标签、停顿、Turbo/基础情感传达、Persona Dream 话语渲染,或判断生成的语音是否与预期的面向产品的情感匹配。

analyze-chatterbox-emotions 是什么?

该技能是一个用于评估 Chatterbox 或 Persona Dream 系统生成的语音渲染质量的工具。它专注于将渲染结果作为语音质量制品进行评估,而非关于说话者真实情感的断言。其核心能力包括测量情感匹配度(基于声学唤醒度/效价代理)、韵律(语速、响度、基频)、停顿的准确性与规划、音频质量(削波、峰值幅度、不连续性)以及可懂度(基于提供的文本或转录)。该技能需要输入语音文件、目标情感标签以及可选的参考文本或渲染计划,并输出一个结构化的 JSON 报告和一个可读的 Markdown 总结。它旨在为开发者和测试者提供客观的、可量化的证据,以验证语音渲染是否符合预期的情感和技术规格。该工具强调其边界:它不推断说话者的真实情感,仅报告声学特征与请求目标的兼容性,并明确区分请求的停顿和实际检测到的停顿。

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

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打开一个已预加载此 Agent Skill 的新对话。

文档

分析 Chatterbox 情感

将生成的 Chatterbox 渲染作为语音质量制品进行评估,而不是关于说话者真实情感的断言。

当需要证据证明 Chatterbox 或 Persona Dream 渲染的情感标签、停顿、语速和音频质量按请求执行时,请使用此技能。

合同

输入:

./run.sh analyze --audio path/to/render.wav \
  --expected-text "optional source text" \
  --target-label reassuring \
  --target-arousal 0.35 \
  --target-valence 0.4 \
  --out /tmp/chatterbox_voice_eval.json \
  --report /tmp/chatterbox_voice_eval.md

输出 JSON schema:analyze_chatterbox_emotions.voice_eval.v1。

评估器报告:

  • affect:目标标签加上声学唤醒/效价代理以及兼容性评分。
  • prosody:时长、估计语速、RMS/响度代理、可用时的 F0 估计。
  • pauses:静音比、检测到的停顿区间,以及在提供 Chatterbox 渲染计划或对话轮次时的计划停顿与实际停顿比较。
  • quality:削波、峰值幅度、不连续性标志和低信号警告。
  • intelligibility:预期文本的词数和可选的转录字段;此技能不会静默地创造 ASR。

可选输入

  • --render-plan path.json:Chatterbox render_plan.json、Persona Dream journal receipt、动态对话 receipt,或包含 chatterbox_pause_plan 的对话轮次 JSON。
  • --transcript "text":由调用方提供的 ASR 转录。如果省略,则转录相似度为 null 并列为非声明项。

判定结果

  • pass:没有严重技术失败且得分 >= 75。
  • review:得分为 50-74 或缺少重要证据(如无 ASR 转录)。
  • fail:存在严重技术问题、音频不可用、严重削波、缺少音频或得分 < 50。

该分数是一个加权评估信号,而非真实情况:

0.35 affect_match + 0.25 intelligibility + 0.20 prosody + 0.20 technical_quality

重要边界

  • 不要断言这个人或角色“是悲伤/愤怒/快乐的”。要说明波形和可用的分类器/代理与请求的目标是兼容的还是不兼容的。
  • 语音情感识别模型是可选的信号。如果不可用,此技能仍会发出声学代理指标并记录 emotion_classifier.available=false。
  • 实际停顿尽可能从波形中验证;渲染计划中的 pause_after_ms 仅为请求的停顿。

维护

运行:

./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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