Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
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Scan an experiment repo and generate a complete paper outline (H1/H2/H3) with user approval checkpoints at each level, then generate body text with evidence annotations, citations, and bilingual output. Python ML repos. Scan an experiment repo and generate a paper outline level by level (H1/H2/H3), advancing after user approval at each level, then generate body text with evidence annotations, citations, and bilingual output.
When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' or 'monetization.' This skill covers pricing research, tier structure, and packaging strategy.
Build Progressive Web Apps (PWAs) with offline support, installability, and caching strategies. Trigger whenever the user mentions PWA, service workers, web app manifests, Workbox, 'add to home screen', or wants their web app to work offline, feel native, or be installable.
Manage projects, tasks, and priorities effectively as a solopreneur. Use when organizing work, tracking progress, managing deadlines, coordinating with contractors or clients, or building project management systems. Covers task management methods, prioritization frameworks, project planning templates, tool selection, and personal productivity workflows. Trigger on "project management", "manage my work", "organize tasks", "project planning", "task management", "prioritization", "stay organized".
Audit a codebase for dead and otherwise safe-to-remove code, and report each finding for approval. Use when asked to find dead or unused code, deduplicate repeated constants, replace magic literals that duplicate a named constant, or remove arbitrary caps, thresholds, or timeouts.
Manages cloud infrastructure through natural language conversations with Pulumi Neo, an AI agent for platform engineers. Enables infrastructure analysis, resource provisioning, stack deployment, and configuration management via conversational AI. Use when creating Neo tasks, requesting infrastructure analysis, automating cloud deployments, managing infrastructure as code (IaC), provisioning AWS/Azure/GCP resources, managing infrastructure through natural language prompts, reviewing PRs with Neo, handling Neo approval workflows, or checking Neo task status and events. Also use when the user mentions "Pulumi Neo", "Neo task", "Neo agent", or wants AI-assisted infrastructure management.
Analyzes, validates, converts, and transforms crystal structures and molecules with pymatgen. It covers CIF/POSCAR/XYZ/JSON conversion, symmetry-tolerance sweeps, local phase diagrams from computed entries, VASP and Q-Chem I/O, band structure and DOS parsing, and bounded Materials Project (mp-api) queries. Use when validating a CIF or checking disorder and oxidation states. Use when assigning space groups and testing symprec sensitivity. Use when converting structure files and checking for representation loss. Use when building a convex hull from total energies. Use when querying Materials Project with explicit fields and limits. Not for general molecular dynamics or non-materials cheminformatics.
MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics — mzML/mzXML I/O, signal processing (smoothing, peak picking, centroiding), feature detection/linking, peptide/protein ID with FDR, untargeted metabolomics. Use matchms for simple spectral matching.
Complete Python Cloudflare deployment system. PROACTIVELY activate for: (1) Python Workers with Pyodide, (2) FastAPI on Workers, (3) Cloudflare Containers for heavy compute, (4) Service bindings (RPC), (5) Environment variables in Workers, (6) Cold start optimization, (7) Workflows for durable execution, (8) GPU containers for AI. Provides: Worker code patterns, wrangler config, Dockerfile templates, FastAPI integration. Ensures edge deployment with proper architecture choices.
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
Anti-analysis countermeasure gateway. Orchestrates: packer identification → simple unpacking → advanced unpacking → deobfuscation. Sub-skills: [[re-packer-id]] [[re-unpack-simple]] [[re-unpack-advanced]] [[re-deobfuscate]] [[re-evasion]]. Triggers: unpacking, packer detection, packer identification, packer, UPX, VMProtect, Themida, anti-debugging, deobfuscation, junk code, unpack, anti-analysis.
Use whenever the user mentions a Jira issue key and wants more than a surface-level lookup — "Read PROJ-123", "What's PROJ-123 about?", "Give me context on PROJ-123", "Deep dive PROJ-123", "What's blocking PROJ-123?", "Summarize PROJ-123 and its dependencies", "I need to work on PROJ-123, what should I know?", or any request to understand an issue's purpose, scope, or requirements. Thoroughly researches and synthesizes a Jira issue including all linked issues, sub-tasks, blocked dependencies, and supporting Confluence documentation.
Diagnoses and resolves PostHog ingestion warnings — problems recorded while ingesting events (dropped events, rejected person merges, oversized payloads, invalid data). Use when a user asks why events are missing, dropped, or undercounted, why identify/alias calls don't work or accounts stay duplicated, why person or group properties aren't updating or profiles look inflated, why recordings have gaps, heatmaps are empty, LLM token counts are missing, why cookieless events vanish, or whenever the `ingestion_warning` health check fires. Explains severity triage (error = dropped, warning = modified, info = intentional) and routes all warning types — size limits and enrichment, merges and distinct IDs, high-volume distinct IDs, `$process_person_profile`, timestamps, cookieless, heatmaps, transformations, session replay — to per-issue reference files with code-level causes and per-SDK fixes.
Responsive web design sub-skill. Provides mobile-first layout patterns, CSS Grid/Flexbox strategies, responsive images, fluid typography, container queries, and breakpoint systems. Pull this in whenever building or reviewing UI that must work across mobile, tablet, desktop, and large screens. Triggers on: "make it responsive", "responsive design", "mobile-first", "breakpoints", "fluid layout", or any request involving cross-device layout.
Analyzes a codebase's modularity imbalances using the Balanced Coupling model and produces a review of design issues. Use when reviewing existing code for coupling problems, assessing architecture quality, identifying distributed monolith risks, or finding areas where changes are unexpectedly expensive.
Use ProviderScope in Flutter and ProviderContainer in Dart for Riverpod; where provider state is stored, overrides, observers, testing with ProviderContainer.test. Use when setting up Riverpod in Flutter or pure Dart, understanding where state lives, or writing tests with a fresh container. Use this skill when the user asks about ProviderScope, ProviderContainer, or where Riverpod stores state.
Implement pull-to-refresh with Riverpod using RefreshIndicator and ref.refresh; show spinner on initial load, show previous data during refresh, AsyncValue pattern matching. Use when the user asks about pull-to-refresh, RefreshIndicator with Riverpod, or refreshing async providers.
Scope Riverpod providers to a subtree using overrides and dependencies; page-specific behavior, ListView optimization, avoiding passing family parameters. Use when changing provider behavior for one page or widget, or when optimizing rebuilds. Use this skill when the user asks about scoping providers or provider scope.
Use when building custom chat for a Roblox game — multiple named chat channels shown as switchable TABS (e.g. "Local"/"Global"), cross-server chat or a cross-server event feed, or piping game events (big pulls, kills, announcements) into chat. Covers modern TextChatService + ChannelTabsConfiguration + MessagingService.
RouterOS command tree introspection via /console/inspect API. Use when: building tools that parse RouterOS commands, generating API schemas from RouterOS, working with /console/inspect, mapping CLI commands to REST verbs, traversing the RouterOS command hierarchy, or when the user mentions inspect, command tree, RAML, or OpenAPI generation for RouterOS.
Translate structured documents (DOCX) to RTL languages (Arabic, Hebrew, Urdu) while preserving exact formatting, table structures, colors, and layouts. Handles quote normalization, multi-pass translation matching, and RTL-specific formatting patterns.
Use this skill to run instrumented Android tests on Gradle Managed Devices (GMD) — emulators that Gradle provisions, boots, runs tests on, and tears down, so CI and every developer get the same device without managing an emulator by hand. Covers the `android.testOptions.managedDevices { localDevices { create(...) { device; apiLevel; systemImageSource } } }` DSL, `ManagedVirtualDevice`, device `groups`, the generated tasks (`<deviceName>DebugAndroidTest`, `<groupName>GroupDebugAndroidTest`, `allDevicesCheck`), Automated Test Device (ATD) images (`aosp-atd`/`google-atd`) for lighter headless CI, test sharding across managed-device copies, where the HTML report and extra outputs land, and how this differs from `connectedAndroidTest` and `adb shell am instrument`. Use when the user mentions "Gradle Managed Devices", "managedDevices", "allDevicesCheck", "ManagedVirtualDevice", "pixel2api30DebugAndroidTest", "ATD image", "automated test device", or "reproducible emulator for tests".
Designs reusable Qwilr proposal templates for your whole sales team. Use when every rep's proposals look different, reps waste hours building proposals from scratch, your templates are outdated and no one uses them, proposals lack consistent branding, or you need vertical-specific templates your whole team can reuse. Do NOT use for a one-off proposal (use /sales-proposal-page), a complex multi-party deal room (use /sales-deal-room), or CRM/automation wiring (use /sales-qwilr-automation).
Detect business logic vulnerabilities in a codebase using a three-phase approach: threat modeling (domain analysis and attack scenarios), batched verify (check exploitable gaps in parallel subagents, 3 scenarios each), and merge (consolidate batch results). Covers price manipulation, workflow bypass, limit violations, race conditions, reward abuse, etc. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/businesslogic-results.md. Use when asked to find business logic, logic flaws, or abuse-of-function bugs.
Hedera Schedule Service (HSS) smart contract development. Use when creating or interacting with scheduled transactions from Solidity via the Schedule Service system contract at 0x16b (e.g. scheduleNative for token creation, scheduleCall for generalized contract calls, authorizeSchedule, signSchedule, deleteSchedule, or querying scheduled token info).
Use when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion, Abstract, and Title, with evidence-preserving and target-journal-aware guidance.
Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.
Machine learning in Python with scikit-learn. Use for supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Hub for page scroll effects — 64 verbatim ORIGINALS (freefrontend/CodePen, MIT) in the repo Rheinmir/uiux-asset for copying files as-is, plus 7 light vanilla effects: reveal-on-scroll, staggered text, scroll highlight, sticky shrink nav, scroll-to-top, scrollspy TOC, light data-speed parallax. Vanilla HTML/CSS/JS, respects prefers-reduced-motion. Call when the user says "hiệu ứng scroll", "scroll reveal", "scroll to top", "scrollspy", "parallax nhẹ", "highlight khi cuộn", or /scroll-effects. DIFFERENT from `blur` (heavy GPU WebGL image transition) and `timeline` (time milestone axis) — these are everyday page scroll effects, light, no dependency.
Validates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role. Use when the user says "set up", "getting started", "check my environment", "configure SDK", or as the first step in any plan involving SageMaker/Bedrock training, evaluation, or deployment.
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.