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Found 452 Skills
Root-cause-driven solution decision framework for the hardest problems across any domain. This is the nuclear option — it consumes significant tokens through exhaustive multi-branch root cause analysis, MECE solution enumeration, and domain-adaptive external validation. Use ONLY for genuinely difficult problems: recurring failures that resist repeated fix attempts, complex systemic issues with no clear solution path, decisions where multiple approaches exist and the wrong choice has high cost, problems with multiple interacting causes spanning components or teams. Trigger when: the user says 'what's the best way to fix X', 'why does this keep happening', 'how should we approach this', 'find the root cause', 'what are my options for fixing X', 'analyze this problem systematically', 'evaluate our options for X', 'what's the right approach and why', or expresses frustration that previous solutions didn't stick. Do NOT use for: problems where the answer is already obvious or requires no analysis, straightforward issues with clear solutions, or routine investigation. If the problem can be solved in 5 minutes of investigation, this skill is overkill.
Manage Microsoft Teams bot infrastructure using the Teams CLI. Use when the user wants to create, configure, or troubleshoot Teams bot apps and registrations. Does not cover building or hosting bot application code.
Progressive Domain Crystallization (PDC) — a skill for building and maintaining a living domain knowledge base for any custom business application. Use this skill whenever the user is developing a business application and wants the AI to accumulate understanding of internal terminology, entities, relationships, and business rules over time — especially when that knowledge is not fully defined upfront and grows across sessions. Trigger on any of: "remember how our system works", "learn our domain", "track business entities", "build domain knowledge", "understand our terminology", "grow AI context over time", "domain model", "business rules documentation", or whenever a user says the AI doesn't understand their business-specific language or data model. Also use at the start of any session where a DOMAIN.md file exists in the project — always read it before doing any work.
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 reviewing, auditing, or grading a command-line tool for agent-friendliness - it runs a black-box test suite against a target CLI and reports per-rule pass/fail from the cli-for-agents 45-rule catalog. Trigger even if the user doesn't explicitly say "agent-friendly" - apply whenever they ask "is mycli good for agents?", "review this CLI", "grade my cli against the rules", "check if this tool is safe to automate", or "audit command-line design". Companion to the cli-for-agents distillation skill.
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 agenda-setting theory (McCombs & Shaw) to analyze how media salience transfers to public perception. Use this skill when the user needs to study media influence on public opinion priorities, evaluate issue salience transfer across media and public agendas, or design communication strategies that leverage agenda-setting effects — even if they say 'why is everyone talking about this topic', 'how does media shape public priorities', or 'which issues get attention and why'.
Apply Consumer Culture Theory to analyze consumption as a cultural practice shaped by identity, marketplace cultures, and ideology. Use this skill when the user needs to interpret consumer behavior through cultural lenses, analyze brand communities or subcultures of consumption, decode marketplace ideologies, or when they ask 'why do consumers behave this way culturally', 'what does this consumption mean', or 'how does identity shape buying'.
Apply behavioral finance theory to identify systematic investor biases and their impact on asset prices. Use this skill when the user needs to analyze irrational market behavior, explain pricing anomalies through cognitive biases, diagnose investor decision errors, or when they ask 'why do investors hold losers too long', 'how does loss aversion affect pricing', or 'what biases drive this market pattern'.