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daymade/claude-code-skills

>- Generates Chinese/Japanese speech with StepFun's Contextual TTS — default stepaudio-2.5-tts, stepaudio-3-tts for whisper/inline-() prosody. Replaces step-tts-2's voice_label with natural-language instruction. Use for emotional/prosody-controlled synthesis, batch voice lines, migrating from step-tts-2, or cloned voices (2.5/step-tts-2, not v3). Triggers on 阶跃 TTS, 语音合成, 配音. Not for transcription (use stepfun-asr).

¿Qué es claude-code-skills?

claude-code-skills is a Claude Code agent skill that >- Generates Chinese/Japanese speech with StepFun's Contextual TTS — default stepaudio-2.5-tts, stepaudio-3-tts for whisper/inline-() prosody. Replaces step-tts-2's voice_label with natural-language instruction. Use for emotional/prosody-controlled synthesis, batch voice lines, migrating from step-tts-2, or cloned voices (2.5/step-tts-2, not v3). Triggers on 阶跃 TTS, 语音合成, 配音. Not for transcription (use stepfun-asr).

Compatible con✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/daymade/claude-code-skills/tree/HEAD/daymade-audio/stepfun-tts

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Documentación

StepFun Contextual TTS (default stepaudio-2.5-tts)

Generate Chinese / Japanese speech with StepFun's Contextual TTS — emotion and prosody go through natural-language description, not fixed labels. Default model is stepaudio-2.5-tts (what the bundled script uses): in a 2026-09-16 blind A/B on our own cases, 2.5 won the neutral / jiao / lively-girl pairs 3:2. Pick stepaudio-3-tts when the line is whisper or heavy inline-() prosody — v3 won exactly those two pairs; v3 also raises the instruction cap to 500 chars (2.5 is 200). ⚠️ Never synthesize cloned voices with v3 — v3 speech 接受复刻音色 ID 不报错但静默回退默认女声——根因 = 阶跃复刻链路整体停在 2.5 家族(复刻创建 API 只收 2.5/step-tts-2/step-tts-mini,v3 不在列;v3 合成侧复刻未发布)。两代创建的克隆在 v3 上全丢:step-tts-2 克隆 SIM 0.272、2.5 创建的新克隆 SIM 0.195(锚 0.773),2.5 同 ID 0.743/0.678。

Companion: for transcription with stepaudio-3-asr-max (the sibling model), use the stepfun-asr skill — they share an API key but live on different endpoints with different body shapes.

Why this skill exists — two non-obvious pitfalls that cost hours if you don't know them:

  1. stepaudio-3-tts rejects voice_label (the step-tts-2 way) — verified on v3 2026-09-16: HTTP 400 voice_label is not supported for this model. Emotion/prosody goes through instruction (natural-language description, ≤500 chars on v3 — 200 was the 2.5 limit) and inline () parentheses inside the text itself.
  2. Censorship behavior is model-version-specific — the 2.5-era trigger list (死 / 消失 / sensitive political terms → censorship_block) did not fire on v3 in a 2026-09-16 single-sample probe; treat censorship as present but re-verify per trigger before building rewrite maps. The 2.5-era options are in references/migration_from_v2.md.

Config and auth

API key lives in $STEPFUN_API_KEY (preferred) or ${CLAUDE_PLUGIN_DATA}/config.json (fallback for cross-session persistence). All bundled scripts try env first, then config.

First-time setup (one-liner):

mkdir -p "${CLAUDE_PLUGIN_DATA}" && cat > "${CLAUDE_PLUGIN_DATA}/config.json" <<EOF
{"api_key": "<paste key here>"}
EOF

If the user hasn't set a key, ask them to paste it (don't guess / don't use a placeholder). StepFun API keys are available at https://platform.stepfun.com/ → API Keys. Use a Normal key, not a Plan key (Plan keys are restricted to text models and silently fail on audio endpoints).

Common tasks — decision tree

User wants...ScriptKey detail
Synthesize 1–500 char Chinese with emotionscripts/tts_generate.pyUse instruction for mood, () for inline prosody
Synthesize long text (500–1000 char)scripts/tts_generate.py1000 char is the hard cap; split at semantic boundaries above that
Batch-generate game/app voice linesscripts/tts_generate.py --batch <jsonl>Handle censorship_block fallback individually
A/B compare two TTS modelsscripts/ab_compare.shCompares duration/size across two directories
Migrate from step-tts-2 / stepaudio-2.5-ttssee references/migration_from_v2.mdvoice_label.emotion → instruction rewrite + 2.5-era censorship list

Starting points

  • Synthesize a single line: Run python3 scripts/tts_generate.py --text "你好" --out /tmp/hello.mp3 --instruction "温暖的希望感". For fine-grained control read the "Contextual TTS" section below.
  • From code: this script is the endpoint's wrapper in llm-registry — llmreg.wrapper_for("stepfun-tts").tts_generate.synthesize(api_key=…, text=…, model=…, extra={…}). extra is merged into the request body as-is; with {"timestamp": True, "return_url": True} the server answers a JSON envelope ({"data": {"url", "subtitles"}}) that comes back under json instead of audio_bytes (verified 2026-09-19). Parameters are not billed — send what you need. Direct /v1/audio/speech calls elsewhere are blocked by the llm-entry-guard hook.
  • A full migration from step-tts-2 → Contextual TTS: read references/migration_from_v2.md end-to-end before touching code. It has the INSTRUCTION_MAP, the SKIP_CENSORED list pattern, and the output-directory-strategy for non-destructive A/B (written for 2.5; the migration mechanics are identical on v3).

Contextual TTS — beyond emotion labels

The headline feature of stepaudio-3-tts is that you stop mapping emotions to fixed tags and start describing what you want in natural language. Two layers:

Global context (instruction parameter) — sets the overall tone for the entire utterance. ≤500 chars on v3 (2.5 was 200; a 300-char instruction verified accepted on v3 2026-09-16). Think of it like giving stage direction to a voice actor.

instruction: "克制的悲伤,语气低沉柔弱,像快要消失一样"

Inline context (() parentheses inside input) —句内 directives. Parenthesised content is consumed as directions and is NOT read aloud. Use for precise control of pauses, breath, emphasis, or mid-sentence emotion shifts.

input: "(试探着问)你好吗?(开心地)太好了!(突然沉下来)不过...我快要消失了。"

Examples that worked in practice (verified 2026-04-23 on 2.5; all five re-verified on v3 2026-09-16, including these instruction and inline-prosody cases):

  • instruction: "活泼俏皮,像是在撒娇,带点嘴硬" — visibly speeds up delivery vs neutral
  • instruction: "耳语声,气声很重,几乎听不清" — produces audible whisper/breath
  • input: "你好(停顿一下)我是蕾格(轻声)今天(加重)的天气真不错。" — inline directives all respected

What stepaudio-3-tts will NOT accept — voice_label parameter. Error on v3: voice_label is not supported for this model (2.5 said ...for v2 models). This is the #1 migration gotcha from step-tts-2.

Common error patterns (real errors, real fixes)

Error responseActual causeFix
"voice_label is not supported for this model" (v3) / "...for v2 models" (2.5)Sent voice_label to a Contextual TTS modelRemove voice_label; put the same intent into instruction as natural language
"The content you provided or machine outputted is blocked." type: censorship_blockSensitive word (2.5-era: 死 / 消失 / etc.; v3 triggers unverified)Rewrite the phrase OR fall back to step-tts-2 for that specific line (mixed-model is fine)
Silent audio truncation (input > 1000 chars)Hard cap exceededSplit at semantic boundaries; don't truncate mid-sentence

More in references/known_issues.md.

When to read references

  • references/api_reference.md — exact request/response JSON for /v1/audio/speech, all fields, error responses. Read when writing raw HTTP calls instead of using the bundled scripts.
  • references/migration_from_v2.md — complete playbook for moving a step-tts-2 project to Contextual TTS. Has the emotion→instruction rewrite table, the A/B directory strategy, decision checkpoints, and the 2026-04 speed/quality trade-off data (written against 2.5). Read before any migration work.
  • references/known_issues.md — censorship patterns, TTS duration inflation, v2-family parameter naming gotcha, 1000-char hard cap. 2.5-era verification; re-verify on v3 before relying on a specific entry. Read when debugging anomalous output or evaluating whether to adopt.

Design invariants (don't break these)

  1. Non-destructive A/B output — when regenerating a corpus with a new model, write to a parallel directory (voice/zh_v3/), never overwrite the production corpus. The migration playbook shows why.
  2. Per-line censorship handling — if 2/29 lines get censorship_block, don't fail the batch. Log the skipped IDs, continue. Mixed-model fallback (step-tts-2 for the skipped 2) is normal.
  3. Don't duplicate voice_label logic in new code — any new TTS code targeting stepaudio-3-tts should only use instruction + inline (). Do not write a branch that conditionally emits voice_label.

v3-specific facts (verified 2026-09-16)

  • Official voices: 60+ via GET /v1/audio/system_voices?model=stepaudio-3-tts — the 2.5 list is fully inherited, plus new voices (e.g. English-named Lisa/Alfie, 上海话 shanghaifemale/shanghaimale).
  • Cloned (复刻) voices: do NOT use stepaudio-3-tts — root cause nailed 2026-09-16: StepFun's whole cloning stack is still 2.5-family. The creation API (POST /v1/audio/voices) only accepts stepaudio-2.5-tts / step-tts-2 / step-tts-mini, and v3 /v1/audio/speech silently falls back to a default female voice for ANY cloned ID — SIM 0.272 (step-tts-2 clone) and 0.195 (fresh 2.5-created clone) vs the 0.773 anchor; the same IDs on 2.5 score 0.743/0.678. Synthesize clones with stepaudio-2.5-tts (best SIM) or step-tts-2.
  • No WebSocket streaming for v3 (as of 2026-09-16): wss://api.stepfun.com/v1/realtime/audio?model=stepaudio-3-tts is rejected at handshake (404), while 2.5 still streams there. Need char-level subtitle timestamps on v3? Use REST timestamp:true + return_url:true (subtitles arrive in the response JSON data.subtitles[], char-level ms, accumulated absolute axis) — bare stream_format:"audio" cannot carry subtitles (server 400).

Pricing (verified 2026-09-16, volatile)

  • stepaudio-3-tts synthesis: 2.5 元 / 万字符 (official model page, 2026-09-16 — cheaper than the 2.5-era ~5.8)
  • Zero-shot voice cloning: 9.9 元 / 音色

Re-verify at https://platform.stepfun.com/docs/zh/guides/pricing/details before quoting to stakeholders.

Individual skills in this repo

This repo contains 9 individual skills — each has its own dedicated page.

daymade/claude-code-skills

This skill should be used when comparing two videos to analyze compression results or quality differences. Generates interactive HTML reports with quality metrics (PSNR, SSIM) and frame-by-frame visual comparisons. Triggers when users mention "compare videos", "video quality", "compression analysis", "before/after compression", or request quality assessment of compressed videos.

daymade/claude-code-skills

Download YouTube videos and HLS streams (m3u8) from platforms like Mux, Vimeo, etc. using yt-dlp and ffmpeg. Use this skill when users request downloading videos, extracting audio, handling protected streams with authentication headers, or troubleshooting download issues like nsig extraction failures, 403 errors, or cookie extraction problems.

daymade/local-conversation-history

Entry point for local AI conversation history across providers. Routes a request to the one skill that owns it, by platform (Claude Code, OpenAI Codex, Kimi CLI) and action (read evidence vs continue interrupted work), and owns the one job none of them own alone: a single inventory spanning all three providers. Use when the provider is unknown or plural ("our history", "what have I been working on", "which chats did I have"), when the user wants Kimi CLI history at all, when it is unclear whether they need evidence or resumption, or when they ask for this skill by name. Vague recall that names no platform ("we discussed this once, when was it?") belongs here rather than to a single-provider reader, because a Claude-only answer to an unscoped question cannot support an absence claim. When the platform and the action are both already clear, load that executor skill directly instead — except Kimi CLI, which has no reader or continuation skill of its own and always routes through here.

daymade/ppt-creator

Create professional slide decks from topics or documents. Generates structured content with data-driven charts, speaker notes, and complete PPTX files. Applies persuasive storytelling principles (Pyramid Principle, assertion-evidence). Supports multiple formats (Marp, PowerPoint). Use for presentations, pitches, slide decks, or keynotes.

daymade/prompt-optimizer

Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents. Triggers include requests to "optimize my prompt", "improve this requirement", "make this more specific", or when raw requirements lack detail and structure.

daymade/qa-expert

This skill should be used when establishing comprehensive QA testing processes for any software project. Use when creating test strategies, writing test cases following Google Testing Standards, executing test plans, tracking bugs with P0-P4 classification, calculating quality metrics, or generating progress reports. Includes autonomous execution capability via master prompts and complete documentation templates for third-party QA team handoffs. Implements OWASP security testing and achieves 90% coverage targets.

daymade/read-claude-code-history

Reads, searches, and exports local Claude Code history without resuming work. Covers recent session inventory, exact session timelines, verbatim human input including queued mid-turn prompts, full-event keyword search, hybrid recall when wording changed, end-state triage, and deleted-file recovery across active Claude homes plus registered archives. Use whenever the user asks what they or Claude said, wants a Claude Code session ID or original context, remembers prior work vaguely, needs an old file from a transcript, or must prove what a Claude session contained before continuing it. Also owns the only Kimi CLI surface, via its Kimi inventory and search flags. For Codex history use read-codex-history; when the request names no platform at all or spans providers, start at local-conversation-history.

daymade/read-codex-history

Reads, searches, and exports local OpenAI Codex history without continuing the old task. Lists recent Codex sessions, extracts exact prompt-ledger inputs by Session, locates a rollout by verified session_meta identity, reconstructs one chronological user/assistant timeline with fork and compaction lineage, and performs bounded keyword search across live and archived rollouts. Use whenever the user asks what they told Codex, wants recent original inputs, a Codex Session ID, full prior context, fork ancestry, or evidence of what a Codex run did. For Claude Code history use read-claude-code-history.

daymade/twitter-reader

Fetch Twitter/X post content including long-form Articles with full images and metadata. Use when Claude needs to retrieve tweet/article content, author info, engagement metrics, and embedded media. Supports individual posts and X Articles (long-form content). Automatically downloads all images to local attachments folder and generates complete Markdown with proper image references. Preferred over Jina for X Articles with images.

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