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Found 1,359 Skills
MySQL relational database. Covers queries, indexes, and optimization. Use when working with MySQL databases. USE WHEN: user mentions "mysql", "mariadb", asks about "AUTO_INCREMENT", "ON DUPLICATE KEY UPDATE", "GROUP_CONCAT", "mysql specific syntax" DO NOT USE FOR: PostgreSQL - use `postgresql` instead, MongoDB - use `mongodb` instead, Oracle - use `oracle` instead, SQL Server - use `sqlserver` instead
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.
Validate environment variables on server start and before builds. Catch missing or invalid variables early with clear error messages.
Use when you want to retrieve quantitative RNA expression data and variant eQTL information from the GTEx (Genotype-Tissue Expression) Project across 54 non-diseased tissue sites.
Connect GWAS variants to biological pathways for drug target discovery. Maps disease-associated SNPs to causal genes via eQTL colocalization (GTEx), links genes to enriched pathways (Reactome, KEGG, MetaCyc), and identifies druggable targets within disease-relevant pathways. Use when asked to translate GWAS findings into mechanistic insights, find pathways enriched for disease genes, discover drug targets from genetic evidence, or answer questions like "What pathways are disrupted in type 2 diabetes based on GWAS data?"
Risk-return optimisation for investment portfolios via Longbridge — builds risk-adjusted return-optimal portfolios based on fund size, risk preference (conservative / balanced / aggressive), and investment horizon. Asset allocation across equities / bonds / cash / commodities / alternatives. Evaluates current portfolio efficiency versus the efficient frontier. Triggers: "风险收益优化", "组合效率", "有效前沿", "风险偏好配置", "最优组合", "风险调整收益", "大类资产配置", "投资组合优化", "風險收益優化", "組合效率", "有效前沿", "風險偏好配置", "最優組合", "risk-return optimization", "portfolio efficiency", "efficient frontier", "risk preference", "optimal portfolio", "risk-adjusted return", "asset class allocation", "portfolio optimisation", "mean variance".
Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.
Multi-agent trading analysis for a stock ticker. Runs technical, news, fundamentals, and macro analysts in parallel, then an adversarial bull/bear debate, then Research Manager, Trader, and Portfolio Manager to produce a final BUY/SELL/HOLD decision with entry, stop, and sizing.
How to handle "why did this work stop / why is this looping?" assignments. Forensics first on the named tree, surface the exact stop-point, frame the fix as a general product rule that respects three invariants (productive work continues, only real blockers stop work, no infinite loops), and deliver a plan — no code changes — gated by board/CTO approval before child issues are created. Use whenever the issue title or body asks for forensics on a stalled, looping, or "went too deep" tree.
A structured root-cause investigation protocol for complex, ambiguous, or multi-layer technical problems. Activate this skill whenever: a problem has resisted two or more fix attempts; the root cause is unknown or assumed; you are tempted to try a variation of something that already failed; a system has multiple interacting layers (hardware, OS, runtime, middleware, config, network); the user says "ultrathink", "think deeper", "figure out why", "stop guessing", "find the root cause", or "it's still broken after your fix". Also activate proactively when you catch yourself about to write a fix before you have verified the cause — that instinct is the signal the protocol is needed. The protocol enforces three disciplines that distinguish root-cause investigation from trial-and-error: (1) explicit THOUGHT/ACTION/OBSERVATION cycles, (2) a hard gate that blocks implementation until the cause is verified by direct evidence, and (3) structured escalation when in-process diagnostic tools are exhausted.
Use when writing, reviewing, or refactoring Go code; when handling errors (fmt.Errorf, %w, sentinel, errors.Is, errors.As); when deciding between panic, error return, and log.Fatal; when writing tests (table-driven, t.Helper, t.Fatal vs t.Error, goroutines); when designing API surface (option struct, variadic options, channel direction, context); when naming functions, methods, packages, receivers, or test doubles; when laying out packages and imports; when initializing variables or building strings.