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Found 588 Skills
Builds robust, tool-specific prompts from user intent using a structured extraction and routing engine. Use when the user asks for prompt creation, prompt repair, prompt decomposition, or adapting prompts across Claude, GPT, reasoning models, Gemini, coding IDEs, autonomous agents, and image tools.
Professional-grade contract review skill that adds comment-based issue annotations without changing original text. Enforces a four-layer review (entity verification, basic, business, legal), writes structured comments (issue type, risk reason, revision suggestion) with risk level encoded via reviewer name, and generates a contract summary, consolidated opinion, and Mermaid business flowchart (with rendered image). Output language must follow the contract’s language.
Provides image recognition capabilities for non-multimodal models (such as pure text models like deepseek-v4-pro, GLM-5.1, mimo-v2.5-pro, etc.). This skill is automatically triggered when the main model cannot recognize images, when users send screenshots/design drafts/UI screenshots for analysis, or when users say 'Look at this image', 'Analyze this screenshot', 'What's wrong with this image'. It also applies to any scenario where users paste images but the current model does not support image input. Supports simultaneous recognition of multiple images, with primary-backup fallback achieved by configuring multiple image recognition models. It can also be manually triggered using the commands /skill:vision-support or /vision. Iron Rule: The models configured for this skill are only used for image content recognition and will never participate in main logical reasoning. Note: If the current model is itself a multimodal model (such as Claude Sonnet 4, GPT-4o, Gemini, etc. that can directly recognize images), do not use this skill; let the main model recognize directly.
Router skill for LLMQuant investor-lens workflows. Use when the user wants an investor-style reasoning overlay grounded in LLMQuant Data evidence.
Build and operate a live game using Unity Services. Use when the user needs to implement, connect, or debug backend-driven features — battle passes, achievements, player progression, cloud saves, leaderboards, matchmaking, virtual economies, server-authoritative logic, anti-cheat, player accounts and authentication, remote configuration, feature flags, A/B testing, analytics, or cloud resource deployment. Triggers on live-ops, live service, backend, server authority, cloud code, cloud save, remote config, player data, retention, monetization loop, season pass, ranking, multiplayer sessions, lobbies, or any Unity Services integration.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
Meta-skill for improving and optimizing prompts using Anthropic's prompt engineering best practices. Provides the 4-step improvement workflow (example identification, initial draft, chain of thought refinement, example enhancement), keyword registries for documentation lookup, and decision trees for improvement strategies. Use when improving prompts, optimizing for accuracy, adding chain of thought reasoning, structuring with XML tags, enhancing examples, or iterating on prompt quality. Delegates to docs-management skill for official prompt engineering documentation.
Generate platform-specific social post variants (Twitter/X, LinkedIn, Reddit) from one source input. Works with or without Node.js script. Includes platform reasoning, quality review, and guardrails against cross-posting spam.
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".
Design, critique, and revise UML diagrams from a modeling and communication perspective. Use when model is asked to create or improve UML; when the user asks for a graphical representation, diagram, schema, visual model, process map, state view, architecture view, or system representation of software/system behavior or structure; when the user does not explicitly choose UML but needs a model-like visual explanation; when model must autonomously choose the right UML diagram type or split across multiple UML diagrams. Use for reasoning about diagram form, abstraction level, boundaries, grouping, lifecycle/state design, behavior vs structure, interaction design, or diagnosing why a diagram feels wrong at the modeling level. This skill treats notation as the final representation, not as the core task.
Hypothesis-driven debugging with ranked hypotheses, git bisect strategy, instrumentation planning, and minimal reproduction design. Triggers on: "debug this systematically", "root cause analysis", "bisect this bug", "rank hypotheses", "isolate this issue", "minimal reproduction". NOT for general reasoning.
Use when the user wants to teach / learn an English word as a video — turn a single English word into a self-contained HLS "supercut" lesson built from the mira video base. Stitches every season2 clip where the word is spoken (via the search-app API) into one .m3u8, prepended with a Claude-written bilingual word-intro card (word + IPA + 中文 gloss + usage, Volcano TTS) and appended with a 关注王建硕 CTA card. No MP4 burn. Triggers — "teach <word>", "讲讲 <word>", "学英语 <word>", "把 <word> 做成视频", "/wjs-teaching-english <word>".