Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
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Analyzes legacy COBOL programs and JCL jobs to assist with migration to modern Java applications. Extracts business logic, identifies dependencies, generates migration reports, and creates Java implementation strategies. Use when working with mainframe migration, COBOL analysis, legacy system modernization, JCL workflows, or when users mention COBOL to Java conversion, analyzing .cbl/.CBL/.cob files, working with copybooks, or planning Java service implementations from COBOL programs.
Use this skill to build precise Compose UI test queries by composing `SemanticsMatcher` predicates with `infix and`, `infix or`, and `operator not`, plus the prebuilt filter library (`hasText`, `hasClickAction`, `isEnabled`, `isOn`, `hasTestTag`, `hasContentDescription`, `hasParent`, `hasAnyAncestor`, `hasAnyChild`, `hasAnySibling`, `hasAnyDescendant`, `hasImeAction`, `hasScrollToKeyAction`, `hasScrollToNodeAction`, `isDialog`, `isPopup`, `isRoot`, `isFocused`, `isEditable`, `isHeading`). Covers `SemanticsMatcher.expectValue` / `keyIsDefined` / `keyNotDefined` for custom semantics keys. Use when the developer asks how to find "an enabled button with text Submit", how to combine matchers, how to filter by a custom `SemanticsPropertyKey`, how to write a hierarchical predicate, or mentions `hasParent`, `hasAnyAncestor`, `expectValue`. If the developer wants one matcher instead of three chained assertions, use this skill.
Conclave — Multi-LLM Council with anonymized debate. Orchestrates multiple AI models (Claude, Gemini, GPT, Grok, Llama, etc.) in a Karpathy-style council with 3 depth levels: quick (parallel answers), standard (parallel + synthesis), deep (parallel + anonymized cross-critique with ranking + debate synthesis). Inspired by Karpathy's LLM Council. Use this skill whenever the user types /conclave, asks to 'consult multiple models', 'get council opinion', 'multi-model debate', 'ask the conclave', 'cross-validate with other AIs', or wants multiple LLMs to debate and synthesize an answer. Also trigger for architecture decisions, security reviews, code reviews from multiple perspectives, or when the user wants high-confidence answers by cross-referencing models.
Retrieves authoritative, up-to-date technical documentation, API references, configuration details, and code examples for any developer technology. Use this skill whenever answering technical questions or writing code that interacts with external technologies. This includes libraries, frameworks, programming languages, SDKs, APIs, CLI tools, cloud services, infrastructure tools, and developer platforms. Common scenarios: - looking up API endpoints, classes, functions, or method parameters - checking configuration options or CLI commands - answering "how do I" technical questions - generating code that uses a specific library or service - debugging issues related to frameworks, SDKs, or APIs - retrieving setup instructions, examples, or migration guides - verifying version-specific behavior or breaking changes Prefer this skill whenever documentation accuracy matters or when model knowledge may be outdated.
Critical rules and common mistakes to avoid in Convex development. Use when reviewing Convex code, debugging issues, or learning what NOT to do. Activates for code review, debugging OCC errors, fixing type errors, or understanding why code fails in Convex.
Create new UI elements for the tryelements.dev registry. Use when: (1) Adding new UI components (buttons, inputs, cards), (2) Building integration components (Clerk, Stripe, Uploadthing), (3) Creating theme-related elements, (4) Any shadcn-style registry component. IMPORTANT: For logo components with variants (icon/wordmark/logo + dark/light), use the logo-with-variants skill instead. This skill includes scaffolding, registry schema, and component patterns. ALWAYS use Context7 MCP to fetch latest dependency docs before implementing.
Author FigJam boards with granular control — sticky notes (single + batch), connectors between nodes, shapes-with-text, sections, tables, code blocks, and auto-arrange — plus read back board contents and the connection graph. Use when the user wants to build or edit a FigJam board element-by-element. Triggers: 'add stickies to FigJam', 'create a flowchart/diagram in FigJam', 'connect these nodes', 'make a section/table/code block in FigJam', 'auto-arrange the board', 'read what's on the FigJam board', 'map the connections'. More granular than the native generate_diagram (which produces a whole diagram in one shot). FigJam files only (figma.editorType === 'figjam'). Requires the Figma Desktop app (Plugin API).
Conduct a literature review and develop a CS research proposal. Use when asked to review a research area, find gaps in existing work, and propose a novel research contribution. The output is a research proposal identifying an assumption to challenge (the "bit flip") and how to validate it.
Confirmation gate before any bulk delete, cleanup, or destructive operation that could result in data loss — shell-level (rm -rf, git rm, bulk sed) or brain-level (bulk forget, delete sweeps, purge-deleted, source removal, raw-SQL truncation). Presents a recoverability card and requires an explicit "yes" from the user before proceeding. Routing convention, not an operation-boundary enforcement.
Corrects the wrong defaults a model has when building Datadog dashboards, verified against Datadog's docs in July 2026. Use when creating, editing, or reviewing a Datadog dashboard — choosing widgets, writing metric/log/span queries, or emitting widget JSON. Covers the queries that render a plausible number and are still wrong — `.as_count()` is appended automatically in the graph editor but never through the API, so programmatic counts silently average; `p95` resolves only on distribution metrics, and averaging one is an average of averages; ratios need `.as_count()` on both sides or they divide interpolated averages. Also covers grounding queries in metrics that exist rather than invented names, wire type strings that diverge from UI names (Pie Chart is `sunburst`, Table is `query_table`), and Datadog's own layout standard. Assumes the Datadog MCP server is connected. NOT for Terraform or raw Dashboard API management, monitor and SLO authoring, or non-Datadog observability tools.
Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
Use when the user wants to design a new domain model from scratch — phrases like "help me model X", "what's the bounded context for Y", "design a domain for this feature", "we need a domain model". Walks the user through ubiquitous language, bounded context, aggregate identification, and then routes to the relevant pattern skills. Do NOT use for code generation (route to ddd-implement) or refactoring existing code (route to ddd-refactor).
Use when modeling significant domain occurrences — phrases like "domain event", "OrderPlaced", "PaymentSettled", "something the business cares happened", "decouple these two aggregates", "eventual consistency across aggregates". Defines domain events, their naming (past tense, business-meaningful), and how they coordinate work across aggregates without violating aggregate boundaries
Use when the user is modeling something whose identity matters beyond its attributes — phrases like “entity”, “this thing has an ID”, “two of these are the same if their IDs match”, “tracked over time”, “lifecycle”. Distinguishes entities from value objects by identity continuity. Pairs with ddd-value-object (the opposite distinction).
Use when establishing or auditing a domain's shared vocabulary—phrases like “ubiquitous language”, “what should we call this concept”, “the team uses different words for the same thing”, “the code uses different names than the business”. Defines what a ubiquitous language is, how to grow one, and the smell tests that reveal a missing one. Do NOT use for code generation; this is a process/language skill.
Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources. Uses file-based research tracking, parallel investigation threads, and context-efficient patterns for deep investigations. ALL MEDICAL CITATIONS FROM PUBMED MCP ONLY.
Automatic compliance audit for the Hexagone design system (PrimeVue 4 + Tailwind CSS 4 + Hexagone tokens). Analyses the source code and the rendered UI of a page or component, detects violations, fixes them automatically and produces a report — all without user intervention. Use when the user wants to audit a page or component's design system compliance, fix PrimeVue/Tailwind/Hexagone tokens deviations, or mentions "design compliance" / "conformité design".
Ensures modern, professional UI design across SwiftUI, Android, and web platforms. Use when building ANY user interface components including buttons, forms, cards, layouts, navigation, or complete screens. Enforces clean minimal design, neutral color palettes with one accent color, 8px grid spacing system, proper typography hierarchy, and clear interactive states. Always reference before creating or modifying UI elements.
Select a consistent visual design system for PPT slides using radius/spacing style recipes. Use when users ask for overall style direction or component styling consistency. Includes Sharp/Soft/Rounded/Pill recipes, component mappings, typography/spacing rules, and mixing guidance. Triggers: 风格, style, radius, spacing, 圆角, 间距, PPT风格, 视觉风格, design style, component style.
Provides design direction and visual system for web UI. Defines layout, typography, colour, and overall aesthetic for product (SaaS/admin/data-heavy) or marketing/brand experiences. Use when choosing visual direction, selecting colour palettes and fonts, establishing layout patterns, or starting a new UI design system.
Detect data exfiltration via DNS tunneling (tools like iodine, dnscat2, dns2tcp) by analyzing query entropy, subdomain length, query volume to single domains, TXT/CNAME/NULL record abuse, and oversized response payloads using passive DNS monitoring and statistical/ML methods. Use when hunting for covert DNS-based data exfiltration or building a passive DNS anomaly detection capability.
Use this skill to make `@Preview` functions the primary feedback loop for Jetpack Compose UI work — preview-driven development. Covers hoisting state out of composables so a preview never needs a `ViewModel` or DI graph, `LocalInspectionMode` for conditional preview rendering (e.g. placeholder instead of Coil/Glide), `@PreviewParameter` + `PreviewParameterProvider` for rendering every UI state side by side, multi-configuration previews (`uiMode`, `fontScale`, `device`, `widthDp`, `locale`), `@Preview`-annotated annotation classes for reusable preview sets, and the anti-patterns that make previews rot (screen-level previews, unwrapped theme, zero-width `fillMaxWidth` previews, Lorem-ipsum data). Use when the user mentions "preview won't render", "preview needs a ViewModel", "@PreviewParameter", "LocalInspectionMode", "preview out of date", "preview-driven development", "previews keep breaking", or "how to structure Compose previews".
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
Parse, convert, chunk, and analyze documents using Docling. Use this skill when the user provides a document (PDF, DOCX, PPTX, HTML, image) as a file path or URL and wants to: extract text or structured content, convert to Markdown or JSON, chunk the document for RAG ingestion, analyze document structure (headings, tables, figures, reading order), or run quality evaluation with iterative pipeline tuning. Triggers: "parse this PDF", "convert to markdown", "chunk for RAG", "extract tables", "analyze document structure", "prepare for ingestion", "process document", "evaluate docling output", "improve conversion quality".
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
Programmatically edit Word documents (.docx) with live preview and track changes via SuperDoc VS Code extension. Use when editing DOCX files, making tracked changes, redlining, marking up contracts, or when the user wants to modify Word documents with insertions/deletions visible. Triggers on docx, Word, track changes, redline, markup.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
