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Found 755 Skills
Forensic diagnostic report for LOCI — collects environment state, runs health checks, and writes a timestamped report when analysis fails or doesn't trigger. Invoke when: "bug report", "LOCI isn't working", "exec-trace didn't run", "skill didn't trigger", "MCP not connecting", "results are wrong", "results missing", "generate diagnostic", "something is broken", "debug LOCI", or any LOCI failure the user wants investigated.
Convert legal texts (legal provisions or legal cases) into standardized Markdown format and remove promotional redundant information. This skill shall be used when users need to process legal provisions (such as the Civil Code, Criminal Law, etc.), organize legal cases (such as typical cases of the Supreme People's Court, judgment documents, etc.), or format legal documents from pasted text. Note: This skill is only responsible for formatting and content cleaning, and does not have content crawling capability. Content acquisition shall be completed by other skills (such as wechat-article-fetch), and AI will automatically determine the skill collaboration sequence.
OpenAI Codex Rust coding patterns distilled from the codex-rs workspace. Use this skill whenever writing, reviewing, or refactoring Rust code — especially for async agents, CLI tools, sandboxing, Ratatui TUIs, JSON-RPC protocols, tokio-based services, or any codebase that needs defensive panic discipline. Trigger even when the user does not explicitly mention Codex, because the patterns generalize to any production Rust workspace. Covers async cancellation, error enum design, process sandboxing, Cargo workspace architecture, wiremock-based fakes, insta snapshot testing, OpenTelemetry tracing, and Ratatui rendering.
Produces a design-plan (living document like an exec-plan) that maps an app domain to feature groups using Apple Design DNA patterns. Each feature group becomes a milestone buildable in one ios-taste session. Use when the user describes an app idea, domain, or workflow and needs a structured plan before building. Triggers on "plan this app", "what features does X need", "design plan", "feature breakdown", "what screens do I need", or any pre-build planning question. Also trigger when the user provides workflow notes or user interview results. CRITICAL: This skill produces a DESIGN-PLAN document only. It does NOT generate SwiftUI code, layouts, or visual design.
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
Design-driven development methodology. The design/ directory is the single source of architectural truth — read it before coding, stay within its boundaries, and when the system's shape needs to change, update the design first. Use this skill whenever starting any development work on this project. Also use when the user asks to: create or update architecture docs, add a new module or feature that might cross existing boundaries, refactor system structure, or understand the codebase architecture. Trigger on phrases like "design first", "update the design", "does this change the architecture", "write a design for", "what's the current design", or when onboarding to understand a codebase's shape. Supports arguments: `/design-driven init` to configure a project for design-driven development, `/design-driven bootstrap` to generate design from an existing codebase.
Atlas Cloud API integration skill — quickly call 300+ AI image generation, video generation, and LLM models through a unified API. Use this skill when the user needs to integrate AI image generation (e.g., Flux, Seedream, DALL-E), AI video generation (e.g., Kling, Sora, Seedance), or call LLM APIs (OpenAI-compatible format) into their project. Applicable scenarios include: generating images, generating videos, calling large language models, using Atlas Cloud API, configuring ATLASCLOUD_API_KEY, querying available model lists, searching models by keyword, uploading local images/media files, one-step quick generation, image-to-video, text-to-image, text-to-video, AI content creation tool integration. Even if the user doesn't explicitly mention Atlas Cloud, this skill should be considered whenever AI media generation API integration development is involved.
Apply Cialdini's six principles of persuasion — Reciprocity, Commitment/Consistency, Social Proof, Liking, Authority, and Scarcity — to analyze or design influence strategies. Use this skill when the user needs to make messaging more persuasive, analyze why a campaign works or doesn't, design sales or marketing copy, or understand social influence tactics — even if they say 'how do we convince people', 'why is this ad effective', or 'make this more persuasive'.
Apply signaling theory (Spence, 1973) to analyze how agents communicate private information through costly, credible signals under information asymmetry. Use this skill when the user needs to evaluate whether a corporate action serves as a credible signal, analyze dividend or IPO signaling, assess separating vs pooling equilibria, or when they ask 'why do firms pay dividends', 'is this signal credible', or 'how does underpricing signal quality'.
Apply Affordance Theory (Gibson, 1979; Norman, 1988) to analyze the action possibilities that an artifact provides to an actor. Use this skill when the user needs to evaluate technology design from an affordance perspective, identify why users struggle with an interface, analyze IT-enabled organizational change through affordance actualization, or when they ask 'what does this technology afford', 'why can't users figure out this feature', or 'how does technology enable new practices'.
Apply narrative research methods to understand human experience through stories, analyzing narrative structure, temporality, and meaning-making in life stories and oral histories. Use this skill when the user needs to analyze how people construct meaning through storytelling, examine narrative structure and plot, conduct life story or oral history research, or when they ask 'how do stories shape identity', 'how do I analyze a life narrative', or 'what does this story reveal about experience'.
Phase 1 of the Issue Workflow - Translate the user's problem into a reproducible, traceable {slug}-report.md through conversation. The AI only asks "what you saw, how to reproduce it, what should happen" here, and does not guess the root cause for the user (that's Phase 2's responsibility). This phase is also the only official decision point for determining whether to take the fast track or the standard path: first read the relevant code based on the user's description, and if the root cause can be identified at a glance and the changes required are minor, directly inform the user to take the fast track. Trigger scenarios: The user says "file an issue", "log this bug", "I found a problem". This is the starting point of the issue workflow with no pre-requisites.