Skills
101,174 packaged capabilities and playbooks for agents. Install any of them with one command.
All
Triggered when an agent needs to retrieve auxiliary legal information beyond legal provisions, judicial interpretations and typical cases. This includes, but is not limited to: legislative background materials (draft explanations, deliberation reports, legislative body press conferences), regulatory cases and administrative penalty decisions, local guidance documents and meeting minutes, drafting explanations and policy interpretations of departmental rules, industry standards and technical specifications, law implementation reports, NPC Standing Committee law enforcement inspection reports, white papers and policy documents, prevailing academic views and authoritative textbook opinions, and comparative foreign legal materials. This skill should be triggered when the user's question involves contexts such as "why is it regulated this way", "what is the legislative purpose", "how do regulators handle this in practice", "what special local rules exist" and "what are the industry conventions".
Search context data (memories, skills and resources) from OpenViking Context Database (aka. ov). Trigger this tool when 1. you need information that might be stored as memories, skills or resources on OpenViking; 2. searching files or knowledge is explicitly requested; 3. you see a `search context`, `search openviking`, `search ov` request.
Build React web UIs with PanelUI's web collection — adding its components from the shadcn registry or from npm, colouring them from the theme, and composing them into framed panels. Applies when working in a React project that has shadcn/ui set up with a @panelui registry or has panelui-web installed, when the user mentions PanelUI on the web, or when they ask for a dotted globe, a KPI card, a kanban board, a node graph, an activity grid or a feedback dialog in React.
Use when generating, modifying, or reviewing PinMe Worker (Cloudflare Worker TypeScript) code that accepts payments through UniwebPay — payment links, products/prices, checkout sessions, payment status reads, refunds, subscriptions, or handling UniwebPay webhooks with @uniwebpay/sdk in a PinMe project.
Reproduce a UI as exactly as possible from a reference (Figma, live source, screenshot, or image). TRIGGER when: user wants to copy a design exactly, reproduce a screen from a screenshot, clone a Figma design, match spacing/colors/typography in detail, or port a design to another framework without changing the look. DO NOT TRIGGER for redesigns, loose "something like this" requests, or UX improvement tasks.
Transform podcast transcripts into comprehensive content marketing suites including blog posts, social media content, newsletters, SEO-optimized articles, and timestamps. Use when user provides podcast transcripts or wants to repurpose podcast content.
Polishes LaTeX tables and figures to publication quality. Use when a researcher mentions table formatting, booktabs, figure layout, subfigures, captions, colorblind-safe colors or palettes, cref/cleveref consistency, overfull hbox, column widths, or asks to make tables and figures look professional or camera-ready for a venue (ACM, IEEE, NeurIPS, LNCS, CHI). Converts hline-and-vertical-rule tables to booktabs, sizes columns within the page budget, lays out subfigures with subcaption, applies caption conventions per venue family (table captions above, figure captions below, ACM Description alt-text), fixes ref/cref inconsistencies, and recommends colorblind-safe palettes (Okabe-Ito, Paul Tol) using bundled stdlib-Python linters and palette generators. Edits presentation only — never alters data values, units, or results.
PostgreSQL best practices: multi-tenancy with RLS, schema design, Alembic migrations, async SQLAlchemy, and query optimization. Use when designing multi-tenant tables with Row-Level Security, debugging tenant isolation, creating/changing Alembic migrations, or optimizing PostgreSQL queries. Keywords: PostgreSQL, RLS, Alembic, SQLAlchemy, multi-tenancy.
Author, validate, and test Power Query M expressions in semantic model partitions. Automatically invoke when the user mentions "Power Query", "M code", "M expression", "partition expression", "query folding", or asks to "write Power Query", "fix Power Query", "test a partition", "preview partition data", "debug Power Query step", "optimize Power Query".
Professional typography guidance based on Matthew Butterick's Practical Typography. Use when evaluating, critiquing, or improving document formatting, text layout, font choices, punctuation, spacing, or any typography-related decisions for print or web content.
Ship gate after review→respond: setup, stack tests/build/analyze, engineering + prose, optional UI contrast, and with --signoff/--pr also git-clean, CI checks, and unresolved review threads. Any FAIL exits 1. Use before claiming merge-ready or merging to main.
Assign CPT and HCPCS Level II procedure codes from clinical documentation the way a professional coder builds the claim. Use when users say "code this encounter for procedures", "what CPT codes apply", "assign HCPCS codes", "code this op note", or when turning visit notes or operative reports into claim-ready procedure codes.
Prompt patterns for consistent UI generation: stating design intent precisely, component specification formats, few-shot examples, and structured refinement loops. Use when UI output from an LLM is inconsistent or generic, or when building reusable prompt templates for a design system.
This skill should be used when users request help optimizing, improving, or refining their prompts or instructions for AI models. Use this skill when users provide vague, unclear, or poorly structured prompts and need assistance transforming them into clear, effective, and well-structured instructions that AI models can better understand and execute. This skill applies comprehensive prompt engineering best practices to enhance prompt quality, clarity, and effectiveness.
Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs
Complete OpenCV computer vision system for Python. PROACTIVELY activate for: (1) Image loading with cv2.imread (BGR format gotcha), (2) Video capture with cv2.VideoCapture, (3) Color space conversion (BGR to RGB, HSV, grayscale), (4) Image filtering (GaussianBlur, medianBlur, bilateralFilter), (5) Edge detection (Canny), (6) Contour detection with cv2.findContours, (7) Image resizing with interpolation methods, (8) Template matching, (9) Feature detection (SIFT, ORB, AKAZE), (10) Drawing functions (rectangle, circle, text), (11) Video writing with cv2.VideoWriter, (12) Morphological operations, (13) Deep learning with cv2.dnn module, (14) GPU acceleration with cv2.cuda, (15) Coordinate system (x,y vs row,col) gotchas. Provides: Image processing patterns, video capture/writing, memory management, performance optimization, Jupyter notebook workarounds. Ensures correct BGR handling and memory-safe OpenCV usage.
Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate). Triggers on: "audit RAG pipeline", "RAG quality", "hallucination detection", "why is RAG failing", "grounding check". NOT for general architecture audits, use architecture-reviewer.
Automated iterative agent runner for spec-based development in Kiro. Wraps kiro-cli in a self-correcting bash loop that picks up tasks from a Kiro spec, implements them one at a time, verifies against exit criteria, and accumulates corrections and codebase patterns across iterations. Use this skill when the user mentions "ralph loop", "ralph", "spec loop", "iterative spec runner", "run my spec tasks automatically", "kiro spec automation", "self-correcting agent loop", "implement spec tasks in a loop", "run kiro-cli in a loop", "automated task implementation", or wants to drive a Kiro spec to completion through repeated agent iterations. Also use when the user wants to set up, configure, troubleshoot, or understand the Ralph Loop workflow — including progress tracking, corrections, codebase patterns, timing logs, and the summary dashboard.
Audit a codebase's tool and skill definitions against a failure-mode rubric and write a markdown tuning plan with per-definition before/after rewrites that make them retrievable by Ratel's hybrid lexical + semantic index and usable by the model. Use when the model picks the wrong tool, tools never get picked, descriptions are too long/vague/anemic, to fix near-duplicates, tighten schemas/enums/param names, clean up the tool catalog, or `/ratel-tune-definitions`. Fetches live Ratel docs; writes to <repo>/.ratel/ without editing tool or skill code. The fix /ratel-assessment's Definition Quality dimension routes to; runs after /ratel-decompose-prompt.
Keep README files current with project changes. Use when project structure changes, features added, or setup instructions modified. Suggests README updates based on code changes. Triggers on significant project changes, new features, dependency changes.
Create a research note from a rough question. Cleans the question, splits into separate files if multiple questions, records the source URL, and adds answers with Google search links per topic. Also covers adding follow-up questions to an active research note. Use when the user has a question they want documented as a note.
Internal scope/classification engine composed by review-change (and reused by the audit skills): consumes the synthesized findings table, verifies every applicable axis is represented, and classifies each finding into a decision table (fix-now / replan-in-unit / decision-required / proposal / ignore). Findings only — never refactors.
For UI/UX acceptance after interface implementation or a follow-up review after fixes; not for code diff reviews, direct fixes, or designing from scratch. Conduct an incremental review based on evidence from the actual interface, reusing screenshots for the same state; score independently and determine whether it passes.
Use when reviewing/judging a finished technical-design document (a TDD / engineering design doc / design RFC) for one feature, deciding whether an engineer can implement it without re-deriving the design. An acceptance gate, not authoring. Judges a single-sourced 11-condition implementability bar: every decision traces bidirectionally to a requirement; scoped to one feature; approach and decomposition implementable, diagram synced to narration; interfaces an api-spec or data-model owns are referenced, not duplicated; one real alternative with a decision criterion; failure modes carry handling; observability signals named; testing covers failures and contract conformance; rollout and rollback with measurable triggers; nothing fabricated; an amend reviewed delta-scoped. Emits exactly `VERDICT: approve|revise` plus findings, and does not false-revise a thin-but-complete TDD. Not for authoring, the upstream PRD or feature-spec, the api-spec or data-model, or generic design docs and RFCs.
Revolut X grid trading strategy. Use when the user asks to "backtest a grid strategy", "optimize grid parameters", "run a grid bot", "grid trading", "dry run grid", or runs revx strategy grid commands. Grid run is a long-running background process.
Chain-of-Verification (CoVe) prompting system. Converts lazy prompts into rigorous 4-stage verified output. Use for any code generation, debugging, or implementation task. Automatically invoked by wavybaby for medium/high complexity tasks. Reduces hallucinations and catches subtle bugs.
Use when working with Roblox animation systems including playing, stopping, or blending animations on Humanoid characters or non-Humanoid models, handling AnimationTrack events, replacing default character animations, or debugging animation priority and blending issues.
Pre-sales-call intelligence composite. Deep dives on the person AND their company before a sales call, then maps findings to your product to generate talking points, objection prep, and a call strategy. Goes beyond generic attendee research — this is sales-specific intelligence that helps SDRs, AEs, and founders walk into calls with a plan. Tool-agnostic — works with any CRM, research source, and calendar.
Periodic sales performance review composite. Aggregates ALL sales initiatives taken in a given period — outbound campaigns, inbound efforts, events, partnerships, content, referrals — and measures the impact of each on pipeline and revenue. Produces a team-presentable report covering initiative-level performance, cross-initiative comparisons, pipeline attribution, what's working, what's not, and where to invest next. Tool-agnostic — pulls data from any combination of CRM, outreach tools, and tracking systems.
Tunes vulnerability scanners to reduce false positives, optimize scan policies, and improve result accuracy. Covers false positive identification patterns, scan policy configuration, authenticated vs unauthenticated scanning tradeoffs, severity override criteria, plugin/check selection, scan scheduling, and result correlation across multiple scanners. Uses CVSS 4.0 for severity validation and CWE for vulnerability classification.
Scores Kubernetes resource manifests with Kubesec to flag misconfiguration and privilege-escalation risk before deployment, mapping each finding back to the securityContext change that fixes it. Use when gating manifests in CI, reviewing YAML or a rendered chart before it reaches a cluster, or explaining why a manifest scored negatively. Keywords: Kubesec, manifest score, securityContext, readOnlyRootFilesystem, runAsNonRoot, CI gate. Do not use for scanning built images for CVEs - use scanning-docker-images-with-trivy; for admission-time enforcement use implementing-opa-gatekeeper-for-policy-enforcement.
Comprehensive scientific research toolkit with 139 specialized skills for biology, chemistry, medicine, data science, and computational research. Transforms Claude into an AI research assistant with access to scientific databases, analysis tools, and domain-specific workflows.
Use when storing credentials in OCI Vault, troubleshooting secret retrieval failures, implementing secret rotation, or setting up application authentication to Vault. Covers vault hierarchy confusion, IAM permission gotchas, cost optimization, temp file security, and audit logging.
Performs a structured network segmentation review against NIST SP 800-207 (Zero Trust Architecture) and CIS Controls v8 (Control 12 -- Network Infrastructure Management). Auto-invoked when reviewing network architecture, VLAN configurations, micro-segmentation policies, or DMZ designs. Produces a segmentation maturity assessment with zone mapping, trust boundary analysis, and remediation guidance.
Strategic SEO planning for new or existing websites. Industry-specific templates, competitive analysis, content strategy, and implementation roadmap. Use when user says "SEO plan", "SEO strategy", "content strategy", "site architecture", or "SEO roadmap".
Coordinates end-to-end setup of an Agentforce Contact Center voice channel: gathers country and phone-number-type selections, procures a chosen number, resolves its CommunicationChannelLine, verifies the number is live, and creates an active PstnVoice MessagingChannel. The user picks the routing model: Omni Queue (route to the contact center queue) or Omni Flow (route to an Agentforce agent, creating or reusing the agent plus its routing flows). Use when the user asks to set up an Agentforce Contact Center, provision a contact center phone number and voice channel together, or put an Agentforce agent on native voice. TRIGGER when the user says set up agentforce contact center, configure contact center voice, or create a voice channel with omni flow and an agentforce agent. DO NOT TRIGGER when only wiring an existing channel to an existing agent with no number to procure, or creating a non-voice messaging channel.
Initialize the repository’s writing standards and engineering skills: add writing requirements and coding guidelines to AGENTS.md, and configure the issue tracker, triage labels, and domain documentation. Run manually when setting up a repository for the first time.
Handle Salesforce data operations including data migration, sandbox seeding, bulk data loading, data export, and data cleanup. Use when asked about data migration, sandbox seeding, bulk data loading, CSV import/export, or moving data between orgs. Activate on mentions of "data migration", "sandbox seed", "bulk load", "CSV import", "data export", "upsert", or "data plan".
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.