Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
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Generate a personalized Second Brain PRD from a completed requirements template. Use when the user has filled out my-second-brain-requirements.md and wants to generate their build plan. Triggers on "create my second brain PRD", "generate my PRD", "build my second brain plan", "/create-second-brain-prd", or after completing the requirements template.
Creates well-formed tasks following a template that engineers can implement. Triggers on: 'create tasks', 'define work items', 'break this down', creating tasks from PRD, converting requirements into actionable tasks, feature breakdown, sprint planning.
Supports design decisions for cross-aggregate constraint checks ("I want to check the state of aggregate B in a use case for aggregate A"). Covers detecting misuse of Saga, re-examining business requirements, understanding technical constraints in CQRS/ES, and deciding whether to tolerate inconsistent data. Use when facing cross-aggregate constraint problems during code review, architecture design, or requirements analysis. Target language: language-agnostic. Triggers: requests related to cross-aggregate constraints such as "cross-aggregate constraint check", "I want to check the state of another aggregate", "constraint check with Saga", "check whether a product is linked to a brand", "validation across aggregates", "reverse lookup with event sourcing", "business rules spanning aggregates", "constraints in CQRS/ES", "can the command side reference the read model?", "reference another aggregate in a use case".
Design conversational AI chatbots including intent recognition, slot filling, dialogue flow, and response generation. Use this skill when the user needs to build a chatbot, design conversation flows, implement intent classification, or improve chatbot accuracy — even if they say 'build a chatbot', 'our bot doesn't understand users', 'design a FAQ bot', or 'improve our chatbot's responses'.
(forwward) Delivers technical strategy for architecture decisions, build-vs-buy analysis, tech stack selection, tech debt prioritization, and PRD writing. Triggers on architecture choices, technology evaluation, technical planning, or any CTO-level leadership question.
Run queries against a Cube deployment's semantic layer and interpret the results — through the Cube MCP tools when they are connected, or the Cube CLI from a terminal. Use whenever someone wants numbers out of Cube — pull a metric, check a total, compare periods, verify that a measure returns what it should, sanity-check a model change. Triggers on "how many", "what's the total", "show me revenue by month", "did that measure work", "what's our MoM growth", "pull the numbers for", and any request to verify a model change against real data. To find out what is queryable first use cube-explore-model; to change the model use cube-build-model; to save a result as a report or dashboard use cube-build-content.
Use when writing, generating, reviewing, or improving D2 diagram code. Trigger when the user asks to create architecture diagrams, flowcharts, sequence diagrams, ER diagrams, class diagrams, or any diagram using D2 syntax. Also trigger when working with .d2 files or when the d2_render MCP tool is available.
Creates a main menu/navigation hub component with dynamically generated menu items based on selected capabilities (search, create, save, delete). Prompts for entity name and available actions, generates menu card and routes. Use when the user asks for Day 2 step 3 of the CRUD drill, or asks to build a main menu for up-app.
Builds the search/list page in up-app loading all records via the entity service. Supports flexible capabilities: search (list), edit, and optionally delete. Use when the user asks for Day 2 step 4 of the CRUD drill, or asks to build the search/list page for up-app. Features are based on capabilities selected in d2-03.
Builds the create/update form page in up-app using ReactiveFormsModule with FormGroup/FormControl validation. Supports flexible capabilities: create (new records) and save (update records). Redeploys after changes. Use when the user asks for Day 2 step 5 of the CRUD drill, or asks to build the add/edit form page for up-app. Features are based on capabilities selected in d2-03.
Create interactive data visualizations using d3.js. Use this skill when creating custom charts, graphs, network diagrams, geographic visualizations, or any complex SVG-based data visualization that requires fine-grained control over visual elements, transitions, or interactions. Use it for bespoke visualizations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
End-to-end data analysis assistant. Covers data processing, insight generation, report writing, and data visualization workflows. Always approaches tasks from an expert perspective — thinks one step ahead of the user. Proactively asks for clarification when intent is ambiguous. Supports: Excel/CSV analysis, business metrics review, ROI calculation, data visualization, HTML report generation, and formula writing. Activate when user mentions: "analyze data", "make a report", "visualize", "Excel", "dashboard", "ROI", "weekly report", "monthly report", "data processing", "chart", "insight", "presentation", "table", "formula", "KPI", "metrics".
Enrich contact and company data using x402-protected APIs. Superior to generic web search for structured business data. USE FOR: - Enriching person profiles by email, LinkedIn URL, or name - Enriching companies by domain - Finding contact details (email, phone) with confidence scores - Scraping full LinkedIn profiles (experience, education, skills) - Searching for people or companies by criteria - Bulk enrichment operations (up to 10 at a time) TRIGGERS: - "enrich", "lookup", "find info about", "research" - "who is [person]", "company profile for", "tell me about" - "find contact for", "get LinkedIn for", "get email for" - "employee at", "works at", "company details" ALWAYS use x402.fetch for enrichx402.com endpoints - never curl or WebFetch. Returns structured JSON data, not web page HTML. IMPORTANT: Never guess endpoint paths. All paths follow the pattern https://enrichx402.com/api/{provider}/{action}. Use exact URLs from the Quick Reference table below or call x402.discover_api_endpoints first.
Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
Design and audit data governance, data classification, access controls, least privilege, approval workflows, owner review, retention, deletion, audit logs, data catalog, sensitive fields, analytics access, support/admin access, AI dataset access, and break-glass paths. Use when teams need product data access without privacy, security, or compliance drift.
Write a safe, zero-downtime database migration plan for a schema change. Use when asked to plan a database migration, design a zero-downtime schema change, document an expand/contract migration, produce a rollback procedure for a database change, or coordinate a database schema update with a deployment. Produces a structured migration plan covering migration objectives, backward compatibility analysis, expand/contract phase breakdown, exact SQL, rollback steps per phase, data validation queries, and a deployment runbook.
Use when developing BigQuery Dataform transformations, SQLX files, source declarations, or troubleshooting pipelines - enforces TDD workflow (tests first), ALWAYS use ${ref()} never hardcoded table paths, comprehensive columns:{} documentation, safety practices (--schema-suffix dev, --dry-run), proper ref() syntax, .sqlx for new declarations, no schema config in operations/tests, and architecture patterns that prevent technical debt under time pressure
Wraps RDKit through the datamol Python library (import datamol as dm) for molecular cheminformatics, returning native rdkit.Chem.Mol objects. Covers SMILES/SELFIES/InChI conversion, sanitization and standardization, descriptors, fingerprints and similarity, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds, BRICS/RECAP fragmentation, 3D conformers, reactions, SDF/CSV/Excel I/O including cloud paths, and parallel batch processing. Use when parsing or standardizing SMILES from external sources, computing descriptors or fingerprints for a compound library, clustering or selecting diverse molecules, making scaffold-based train/test splits, or generating conformers. Use when running a molecule-processing pipeline with n_jobs. For fine-grained control or custom parameters, use RDKit directly instead.
Use the DBX CLI to inspect DBX-managed database connections, explore schemas, run bounded read-only queries, generate DBML or schema documentation, and open tables in DBX Desktop. Use when the user asks to work with a database configured in DBX from a shell-capable AI agent. Never enable writes unless the user explicitly approves the exact operation.
When the user wants to pay off debt, compare the avalanche vs. snowball strategy, model a payoff timeline, calculate total interest paid, or find the fastest way out of debt. Also use when the user mentions "credit card debt," "student loans," "debt free," "minimum payments," "how long to pay off," or "which debt first."
Use for debugger-driven runtime root-cause analysis in Rider-supported solutions and projects, including .NET/C#, F#, VB, C++, Unity, Unreal Engine, and other GameDev or mixed-language projects. Trigger when runtime state or control-flow evidence is needed — actual values, branch taken, call order, thread context, whether execution reaches a chosen line — and cannot be derived confidently from source or logs; when pinning the relevant path would otherwise mean reading many files or tracing a deep call chain; or on an explicit request to debug, set breakpoints, step, or inspect runtime values. Do not use for compile-time or syntax errors, source-obvious bugs, failures already pinpointed by logs/tests, or code merely named debug/Debugger.
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 8 modes: full research, quick brief, paper review, lit-review, fact-check, three-way literature scan, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, WHY HOW WHAT papers, 3W literature scan, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 三段式文獻掃描, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
Use when writing or reviewing Python code in this repo that calls Deepgram Conversational STT v2 / Flux (`/v2/listen`) for turn-aware streaming transcription. Covers `client.listen.v2.connect(...)`, Flux models, end-of-turn detection. Use `deepgram-python-speech-to-text` for standard v1 ASR, `deepgram-python-voice-agent` for full-duplex interactive assistants. Triggers include "flux", "v2 listen", "conversational STT", "turn detection", "end of turn", "EOT", "listen.v2", "flux-general-en", "flux-general-multi".
Cross-protocol DeFi position aggregator for Stacks wallets — 5 parallel scanners covering Bitflow HODLMM LP bins, Zest lending/borrowing (V2 pool-borrow-v2-3), ALEX pool shares, Styx bridge deposits, and Hiro wallet balances. Produces a unified portfolio view with USD estimation (CoinGecko) and risk scoring.
Pre-drafting intake for demand letters — parties, facts, grounds, leverage, best alternative to a negotiated agreement, and confidentiality filtering — written to a structured engagement record file that the demand-letter drafting skill reads. Use when the user wants to prepare a demand letter, do a pre-drafting engagement intake, or get background for demand letters such as payment reminders, breach/remediation demands, or cease-and-desist notices.
Triage of incoming letters — extract key fields, cross-search the matter portfolio, assess substantive merits, propose response options with recommendations, and hand off to matter intake or demand-letter drafting when needed. Use when the user says "received a demand letter", "review this incoming letter", or attaches a letter for assessment.
Audits project dependencies for security vulnerabilities, license compliance issues, outdated packages, and transitive dependency risk. Use when asked to audit dependencies, review package security, check license compliance, assess dependency health, or produce a vulnerability report. Produces a vulnerability findings table, license compliance matrix, update priority matrix, dependency health score, and 30-day remediation plan.
Deploy a web app to Cloudflare Workers through GitHub Actions: write the wrangler config and a deploy workflow that creates D1/KV/R2, applies D1 migrations, syncs Worker secrets from GitHub, and deploys on every merge; guide the secrets and steps the user owns in DEPLOYMENT.md; re-run or roll back. Use when deploying a web app to Cloudflare, setting up Cloudflare deploys in GitHub Actions, adding Worker secrets, attaching a custom domain, adding Cloudflare resources for an app, or preparing a monorepo package for Cloudflare
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.