Claude Code Skills · page 122

Generate speech from text via POST /audio/speech. Covers TTS models (Kokoro, Qwen 3, xAI, Inworld, Chatterbox, Orpheus, ElevenLabs Turbo, MiniMax, Gemini Flash), voices per family, output formats (mp3/opus/aac/flac/wav/pcm), streaming, prompt/emotion styling, temperature/top_p, and language hints.

Transcribe audio files to text via POST /audio/transcriptions. Covers supported models (Parakeet, Whisper, Wizper, Scribe, xAI STT), supported formats (wav/flac/m4a/aac/mp4/mp3/ogg/webm), response formats (json/text), timestamps, and language hints. OpenAI-compatible multipart.

Venice augmentation endpoints for agent pipelines. Covers POST /augment/text-parser (extract text from PDF/DOCX/XLSX/plain text, multipart, up to 25MB, JSON or plain text response), POST /augment/scrape (fetch a URL and return markdown; blocks X/Reddit), and POST /augment/search (Brave ZDR or anonymized Google; structured title/url/content/date results, up to 20 per query). Privacy (zero data retention), rate limits, and error shapes.

Authenticate to the Venice API with a Bearer API key or with an x402 / SIWE wallet. Covers header formats, the SIWE message fields, TTL and nonce rules, the venice-x402-client SDK, and how to choose between the two modes.

Venice billing and usage analytics - GET /billing/balance, GET /billing/usage (paginated per-request ledger, JSON or CSV), and GET /billing/usage-analytics (aggregated by date/model/key). Covers the DIEM/USD/BUNDLED_CREDITS consumption priority and building dashboards. (Beta)

Discover and use Venice public characters (persona-driven system prompts with a bound model). Covers GET /characters (search/filter/sort), /characters/{slug}, /characters/{slug}/reviews, the Character schema, and how to apply a character via venice_parameters.character_slug in chat completions.

Call POST /chat/completions on Venice. Covers the OpenAI-compatible request shape, Venice-only venice_parameters (web search, E2EE, characters, thinking control, X search), multimodal inputs (images/audio/video), tool calls, reasoning controls, streaming, prompt caching, structured output, and model feature suffixes.

Use Venice as a pay-per-call JSON-RPC proxy to 20+ EVM and Starknet networks. Covers GET /crypto/rpc/networks, POST /crypto/rpc/{network}, the 1×/2×/4× method-tier pricing model, per-minute + 24-hour credit rate limits, idempotency keys for safe retries, single vs batch requests, and the unsupported stateful/WebSocket methods (eth_subscribe, eth_newFilter, etc.).

Call POST /embeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), OpenAI compatibility, response compression (gzip/br), and practical usage for retrieval, clustering, and RAG.

Handle Venice API errors correctly. Covers the StandardError / DetailedError / ContentViolationError / X402InferencePaymentRequired body shapes, every meaningful status code (400, 401, 402, 403, 415, 422, 429, 500, 503, 504), the 402 PAYMENT-REQUIRED header used by x402 inference, 422 content-policy suggested_prompt retry pattern, 429 rate-limit headers, and an exponential-backoff retry strategy with idempotency.

Transform existing images with Venice. Covers POST /image/edit (prompt-driven single-image edit), /image/multi-edit (compose 1-3 images), /image/upscale (2-4x upscale + enhance), and /image/background-remove. Accepts base64, file upload, or HTTPS URL.

Generate images with Venice. Covers POST /image/generate (Venice-native), POST /images/generations (OpenAI-compatible), GET /image/styles (style presets), request fields (prompt, dimensions, cfg_scale, seed, variants, style_preset, aspect_ratio, resolution, safe_mode, watermark), and response formats.

Discover Venice models, their capabilities, constraints, and pricing. Covers GET /models (with ?type filter), /models/traits, /models/compatibility_mapping, the ModelResponse schema (capabilities, constraints, pricing per type), and how to use this to pick the right model programmatically.

Use Venice's Alpha POST /responses endpoint - an OpenAI-compatible Responses API with typed output blocks (reasoning, message, function_call, web_search_call). Covers request shape, streaming, differences from /chat/completions, supported venice_parameters subset, and E2EE behavior.

Generate and transcribe videos via Venice. Covers the async /video/quote + /video/queue + /video/retrieve + /video/complete loop, text-to-video, image-to-video, video-to-video (upscale), audio input, reference images, scene and element support, plus /video/transcriptions for YouTube URLs.

Manage Venice x402 wallet credits. Covers POST /x402/top-up (payment discovery + signed USDC settlement), GET /x402/balance/{walletAddress}, GET /x402/transactions/{walletAddress}, USDC on Base (chain 8453), minimum $5 top-up, transaction types TOP_UP/CHARGE/REFUND, and the x402 v2 PAYMENT-REQUIRED response shape returned by all inference endpoints.

Read all atomic guidelines in wiki-twobatch/guidelines/ and propose themed clusters that group near-duplicates. Writes cluster pages and updates _config.yaml; originals are preserved with a `superseded_by:` backref.

Consult an agent-wiki for guidelines relevant to the task at hand. The wiki itself documents how to retrieve from it (AGENTS.md). Use this skill once you know what task or sub-task you're about to do — not at session start.

Read a normalized Claude Code trajectory JSON and extract reusable guidelines into wiki-twobatch/guidelines/. Use when mining saved trajectories for reusable lessons.

Ingest one or more agent trajectories (raw bob/claude traces or normalized JSON) into an agent-wiki end-to-end — convert, summarize, extract guidelines, synthesize skills, consolidate into clusters, and catalog. Use when you have a batch of traces to turn into a wiki in one pass.

Read a normalized Claude Code trajectory JSON and write an episodic summary page to wiki-twobatch/summaries/. Use when summarizing one or more saved trajectories into the agent wiki.

Read a normalized Claude Code trajectory JSON and produce a wiki-resident SKILL.md page that future agents can invoke. Use when a trajectory captured a non-trivial successful workflow worth promoting from a free-text guideline to an executable, callable artifact.

Discover task families across summaries and write per-family comparison pages with findings narrative. Updates wiki-twobatch/_config.yaml task definitions and writes tasks/<slug>__task.md.

Must be used near the end of any non-trivial turn that produced potentially reusable tools, guidance, errors, workarounds, or workflows, so those lessons are saved for future turns.

Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions.

Publish a private guideline to a configured write-scope repo.

Must be used at the start of any non-trivial task involving code changes, debugging, repo exploration, file inspection, or environment/tooling investigation to surface stored guidance before analysis or tool use.

Save the current conversation as a trajectory JSON file in OpenAI chat completion format for analysis and fine-tuning

Captures the current session's successful workflow and saves it as a reusable skill with SKILL.md and helper scripts

Add a shared guidelines repo (read-scope subscription or write-scope publish target) to the unified repos list.

Pull the latest guidelines from every configured repo (read- and write-scope).

Remove a repo from the unified repos list and delete its local clone.

Must be used near the end of any non-trivial turn that produced potentially reusable tools, guidance, errors, workarounds, or workflows, so those lessons are saved for future turns.

Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions.

Publish a private guideline to a configured write-scope repo.

Must be used at the start of any non-trivial task involving code changes, debugging, repo exploration, file inspection, or environment/tooling investigation to surface stored guidance before analysis or tool use.

Save the current conversation as a trajectory JSON file in OpenAI chat completion format for analysis and fine-tuning

Captures the current session's successful workflow and saves it as a reusable skill with SKILL.md and helper scripts

Add a shared guidelines repo (read-scope subscription or write-scope publish target) to the unified repos list.

Pull the latest guidelines from every configured repo (read- and write-scope).

Remove a repo from the unified repos list and delete its local clone.

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Validates cross-artifact consistency and detects breaking changes during feature analysis. Use when running /analyze command, validating spec-plan alignment, checking task-implementation consistency, or identifying API/database/UI breaking changes before deployment. (project)

Before implementing new code (endpoints, components, services, models), search the codebase for existing patterns to reuse. Prevent code duplication by finding and suggesting similar implementations. Auto-trigger when user asks to create, implement, add, or build new functionality.

Detect and warn about breaking API/schema changes before implementation. Auto-trigger when modifying API routes, database schemas, or public interfaces. Validates changes against api-strategy.md versioning rules. Suggests migration paths for breaking changes. Prevents removing endpoints, changing request/response formats, dropping database columns, modifying function signatures without deprecation.

Cache expensive operations to avoid redundant work across workflow phases. Caches project docs (15min TTL), npm info (60min), grep results (30min), token counts (until file modified), web searches (15min). Auto-triggers when detecting repeated reads of same files or repeated API calls. Saves 20-40% execution time.

Executes the /clarify phase using AskUserQuestion tool to resolve ambiguities through structured questions (≤3), prioritization, and answer integration. Use when spec.md contains [NEEDS CLARIFICATION] markers, when requirements need disambiguation, or when running /clarify command to resolve critical scope/security/UX ambiguities before planning. (project)

Expert guidance for creating, writing, building, and refining Claude Code Skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure, progressive disclosure, workflows, validation patterns, and XML formatting.

Expert guidance for creating, configuring, and using Claude Code hooks. Use when working with hooks, setting up event listeners, validating commands, automating workflows, adding notifications, or understanding hook types (PreToolUse, PostToolUse, Stop, SessionStart, UserPromptSubmit, etc).

Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).

Expert guidance for creating Claude Code slash commands. Use when working with slash commands, creating custom commands, understanding command structure, or learning YAML configuration.

Expert guidance for creating, building, and using Claude Code subagents and the Task tool. Use when working with subagents, setting up agent configurations, understanding how agents work, or using the Task tool to launch specialized agents.

Systematic debugging techniques including error classification, root cause analysis (5 Whys), reproduction strategies, and error documentation. Use when debugging errors, investigating failures, analyzing stack traces, fixing bugs, or documenting errors in error-log.md. (project)

Detect and resolve package dependency conflicts before installation across npm/yarn/pnpm, pip/poetry, cargo, and composer. Auto-trigger when installing/upgrading packages. Validates peer dependencies, version compatibility, security vulnerabilities. Auto-resolves safe conflicts (patches, dev deps), suggests manual review for breaking changes. Prevents conflicting versions, security vulnerabilities, broken builds.

Preview command effects without making changes. Simulates file writes, git operations, agent spawns, and state changes. All reads execute normally for accurate preview. Use --dry-run flag on any command.

Classify workflow failures and attempt automatic recovery. Use when sprint/feature fails during implementation to determine if auto-fix is possible or manual intervention required.

Completes feature/epic workflows after deployment with comprehensive walkthrough generation for epics (v5.0+), roadmap updates, artifact archival, documentation, and branch cleanup. Use after /ship-prod, /deploy-prod, or /build-local completes, or when user asks to finalize. (project)

Enforce git commits after every phase and task to enable rollback and prevent lost work. Auto-trigger when completing phases, tasks, or when detecting uncommitted changes. Auto-commit with Conventional Commits format. Verify branch safety, check for merge conflicts, enforce clean working tree. Block completion if changes not committed.

Detect and prevent hallucinated technical decisions during feature work. Auto-trigger when suggesting technologies, frameworks, APIs, database schemas, or external services. Validates all tech decisions against docs/project/tech-stack.md (single source of truth). Blocks suggestions that violate documented architecture. Requires evidence/citation for all technical choices. Prevents wrong tech stack, duplicate entities, fake APIs, incompatible versions.

Executes implementation tasks using Test-Driven Development, prevents code duplication through anti-duplication checks, and maintains quality through continuous testing. Use when implementing features from tasks.md, during the /implement phase, or when the user requests TDD-based implementation. (project)

Extract reusable components from approved HTML mockups during implementation. Identifies patterns, maps CSS to Tailwind, and populates prototype-patterns.md for visual fidelity. Use at start of /implement for UI-first features.

Validates production readiness through performance benchmarking, accessibility audits, security reviews, and code quality checks. Use after implementation phase completes, before deployment, or when conducting quality gates for features. (project)

Identify and execute independent operations in parallel for 3-5x speedup. Auto-analyzes task dependencies, groups into batches, launches parallel Task() calls. Applies to /optimize (5 checks), /ship pre-flight (5 checks), /implement (task batching), /prototype (N screens). Auto-triggers when detecting multiple independent operations in a phase.

Generates implementation plans with code reuse analysis, architecture design, and complexity estimation during the /plan phase. Use when planning feature implementation, analyzing code reuse opportunities, or designing system architecture after specification phase completes. Integrates with 8 project documentation files for constraint extraction. (project)

Executes production deployment workflow by promoting validated staging builds to production with semantic versioning, health checks, and release tagging. Use when running /ship-prod command, deploying to production after staging validation, or promoting staging builds to production environment.

Orchestrates /init-project command execution through interactive questionnaire (15 questions), brownfield codebase scanning (tech stack detection, ERD from migrations), and 8-document generation (overview, architecture, tech-stack, data, API, capacity, deployment, workflow). Use when user runs /init-project, requests project documentation generation, or asks about architecture setup for greenfield/brownfield projects. (project)

Generate regression tests when bugs are discovered during /debug or continuous checks. Auto-detects test framework, creates Arrange-Act-Assert tests, and links to error-log.md entries. (project)

Manages product roadmap via GitHub Issues (brainstorm, prioritize, track). Auto-validates features against project vision (from overview.md) before adding to roadmap. Use when running /roadmap command or mentions 'roadmap', 'add feature', 'brainstorm ideas', or 'prioritize features'.

Integration guide for shadcn/ui components with OKLCH design tokens. Use when setting up shadcn, customizing themes, or adding components to Next.js projects. Auto-trigger on /init --tokens --shadcn flag.

为 AI Agent 友好的代码库搭建和改进 Harness 工程（包括 AGENTS.md、docs/、Lint 规则、Eval 系统、项目级 Prompt 工程）。触发场景：为 AI Agent 设置新项目/空项目，创建 AGENTS.md 或 CLAUDE.md，关于 Harness 工程的问题，让 Agent 在代码库上更高效地工作。当用户感到沮丧或抱怨 Agent 质量时也会触发（例如：'Agent 总是无视规范'、'它从不听从指令'、'为什么它总是做错 X'、'Agent 坏了'）— 因为 Agent 输出质量差几乎总是意味着 Harness 缺失，而不是模型问题。涵盖：Context 工程、架构约束、多 Agent 协作、评估、长运行任务 Harness 以及 Agent 质量问题诊断。

Set up and improve harness engineering (AGENTS.md, docs/, lint rules, eval systems, project-level prompt engineering) for AI-agent-friendly codebases. Triggers on: new/empty project setup for AI agents, AGENTS.md or CLAUDE.md creation, harness engineering questions, making agents work better on a codebase. ALSO triggers when users are frustrated or complaining about agent quality — e.g. 'the agent keeps ignoring conventions', 'it never follows instructions', 'why does it keep doing X', 'the agent is broken' — because poor agent output almost always signals harness gaps, not model problems. Covers: context engineering, architectural constraints, multi-agent coordination, evaluation, long-running agent harness, and diagnosis of agent quality issues.

测试 use-persona 的角色扮演一致性。给定 persona + 10 个对话场景，生成回复并按 5 个维度评分，输出一致性报告。

测试 use-self 替身会议的辩论质量。给定 persona + 3 个决策场景，运行完整三阶段辩论并按 5 个维度评分，输出质量报告。

蒸馏一个你身边的人。通过聊天记录、朋友圈、描述等素材，生成 ta 的人格档案，让 ta 以自己的方式和你对话。

蒸馏你自己的数字替身。通过多轮对话和素材导入，生成你的人格底座，用于私人决策辅助。

以某个人的身份和你对话。用 ta 的语气、习惯、互动方式回应你。

召唤你的数字替身进行决策辅助。多个版本的你同时分析一个决定，帮你看清局中看不清的自己。

Use when user requests diagrams, flowcharts, architecture charts, or visualizations. Also use proactively when explaining systems with 3+ components, complex data flows, or relationships that benefit from visual representation. Generates .excalidraw files and exports to PNG/SVG via Kroki API or locally using excalidraw-brute-export-cli.

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数学建模竞赛论文写作全流程指导。覆盖国赛(CUMCM)和美赛(MCM/ICM)，从论文结构规划、各章节撰写、模型检验、参考文献规范到最终格式检查。与math-modeling-solver形成"解题→写作"配对——可接收solver输出的论文草稿片段直接展开写作。当用户提及数学建模论文写作、建模比赛、国赛/美赛/电工杯/亚太杯/深圳杯/华为杯论文、CUMCM、MCM/ICM、数模论文结构、摘要写作、模型检验、灵敏度分析、latex建模模板、word建模排版、Memo/Letter写作、模型命名、Our Work流程图，或需要写/修改/优化/检查建模论文的任何部分时，都必须使用此 skill。

数学建模竞赛解题全流程指导。覆盖国赛(CUMCM)和美赛(MCM/ICM)全部题型(A-F)，提供12种问题本质分析、95+场景模型决策矩阵、5本算法Cookbook、11本完整例题Playbook、22个Python+7个MATLAB可运行代码模板。与math-modeling-paper形成"解题→写作"配对。当用户提及建模思路、选什么模型、怎么建模、赛题求解、粘贴赛题文本、美赛/国赛题目分析、CVaR/NSGA-II/Monte Carlo/时间序列/ANOVA/灰色关联、网络流/图论/生态建模、模型命名/Memo/Letter/Our Work流程图时，使用此skill。