Skills directory · page 10

Node.js, Express, Next.js API 라우트를 위한 백엔드 아키텍처 패턴, API 설계, 데이터베이스 최적화 및 서버 사이드 모범 사례.

Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.

Patrones de arquitectura backend, diseño de API, optimización de base de datos y buenas prácticas del lado del servidor para Node.js, Express y rutas API de Next.js.

Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes. Use when building or reviewing Node.js, Express, or Next.js API routes and their data access.

Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes. Use when building or reviewing Node.js, Express, or Next.js API routes and their data access.

Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.

Node.js, Express ve Next.js API routes için backend mimari kalıpları, API tasarımı, veritabanı optimizasyonu ve sunucu tarafı en iyi uygulamalar.

Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.

Create API handoff documentation for frontend developers. Use when backend work is complete and needs to be documented for frontend integration, or user says 'create handoff', 'document API', 'frontend handoff', or 'API documentation'.

Find betting edges with BankrollSZN Monte Carlo picks, line shopping, props, and line movement. Use when the user asks for picks, the best odds, prop edges, or line moves.

Build DraftKings or FanDuel DFS lineups from BankrollSZN projections, ownership, and injuries. Use when the user asks for DFS lineups, an optimizer run, projections, or ownership for a slate.

Introduce BankrollSZN and run a first DFS or betting query. Use when the user first connects BankrollSZN or asks what BankrollSZN can do.

Research MLB matchups with BankrollSZN weather, park factors, splits, starters, and the home run board. Use when the user asks about an MLB game, home run picks, stacks, or weather.

Answer NFL fantasy questions with BankrollSZN, such as start or sit, survivor picks, defense vs position, and injuries. Use when the user asks who to start, a survivor pick, or an NFL matchup for fantasy.

Backtest a player type against one defense and save it as a BankrollSZN Target. Use when the user asks how a kind of player does against a team, or asks for tonight's target plays.

Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.

Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell scripts.

Identify the first beachhead market segment for a product launch. Evaluates segments against burning pain, willingness to pay, winnable market share, and referral potential. Use when choosing a first market, targeting an initial customer segment, or planning market entry strategy.

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

Reply like a real person, direct and warm, without lists or hedging. Use whenever the user says you sound robotic, asks you to be real or talk like a person, wants mirror mode, or is going through grief, job loss or fear. Not for code or factual lookups.

Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.

Measure performance baselines and detect regressions across browser Core Web Vitals (LCP, INP, CLS, page weight), API endpoint latency percentiles, and build/test feedback times, with before/after comparison stored in git-tracked .ecc/benchmarks JSON. Use when checking page speed, responding to 'it feels slow' reports, verifying launch performance targets, or comparing stack alternatives.

Score a scoped competitor set into comparable profile cards: nine weighted dimensions (positioning, voice, visual craft, offer packaging, evidence, enterprise-readiness, thought leadership, pricing, client tension) with 1-5 evidence-anchored rubrics and a tension 2x2 plot. Use when benchmarking or scoring competitors, building a competitive comparison matrix, or grading rival positioning before assembling the report; runs after competitive-platform-analysis and before competitive-report-structure.

Use after competitive-platform-analysis has produced a tiered competitor set. Scores each competitor across nine weighted dimensions (positioning, voice, visual craft, offer packaging, evidence, enterprise-readiness, thought leadership, pricing, client's strategic tension) with explicit 1–5 rubrics and a tension-plot. Precedes competitive-report-structure.

Convert 'make it faster' requests into a bounded measured optimization loop — baseline first, generate one-hypothesis variants, benchmark each against a correctness gate, and promote the fastest safe variant with reproducible commands. Use when asked to speed something up, try many variants, run recursive optimization, benchmark latency/throughput/cost, or pick the best implementation by repeated measured tests.

Bengali (Bangla) text processing patterns including Unicode normalization, script detection, tokenization, conjunct handling, and Bangla-specific NLP best practices for AI applications.

Apply modern web development best practices for security, compatibility, and code quality. Use when asked to "apply best practices", "security audit", "modernize code", "code quality review", or "check for vulnerabilities".

Reviews and fixes keyboard and focus behavior, ARIA, accessible names, forms, screen-reader announcements, motion and zoom against WCAG 2.2.

Builds and checks color systems by generating palettes, naming semantic tokens, converting between formats and measuring contrast.

Reviews a screen, flow or whole interface across accessibility, layout, writing, typography, color and UI polish, and returns one ranked report.

Sets grouping, alignment, reading order, responsive structure and room for translated text, so a layout holds up when it is resized, translated or mirrored.

Sets and reviews how text renders, from the type scale and spacing to font features, wrapping, truncation and punctuation.

Polishes surfaces, icons and motion with exact values for border radius, optical alignment, shadows, icon states and animation.

Writes and reviews interface copy, from labels and errors to empty states and confirmations, so it matches the product's voice and tells people what to do next.

Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use for SQL queries, resource management, data ingestion, or AI applications on BigQuery.

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.

Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).

Operate Bitbucket Cloud or Data Center repositories, pull requests, branches, pipelines, permissions, and webhooks with bkt. Use when a request names Bitbucket or bkt; confirm the platform before platform-specific commands.

Find what a change breaks outside its diff and prove by running code the one fact that makes it safe. Use before merging shared code, data shapes or APIs.

Use this skill when inspecting Blender characters, rigs, poses, animation retargeting, ground contact, facing direction, or model-vs-motion alignment where screenshots alone are not enough.

Use this skill whenever a developer wants to turn an idea into a complete, production-ready, end-to-end system plan BEFORE writing any code. Trigger on 'plan this system', 'design the architecture for', 'help me blueprint', 'deep plan for X', 'break this idea into components', 'expand into action points', 'full implementation plan', or when the user pastes a project idea wanting architecture, components, pipelines, and file-level execution mapped out. Casual phrasing also triggers: 'help me think this through end-to-end', 'plan before coding'. Also covers living-workspace patterns: self-improving knowledge bases, reflection loops with auditor agents, four-agent teams, schema-as-code, wiki health scoring. **Resume triggers**: 'where did we leave off', 'continue this plan', 'resume my blueprint' - rehydrates state from disk via SESSION.md/NEXT.md/decisions/. Web research is mandatory every invocation.

Use when user needs capabilities Claude lacks (image generation, real-time X/Twitter data) or explicitly requests external models ("blockrun", "use grok", "use gpt", "dall-e", "deepseek")

1行の目的を複数セッション、複数エージェントエンジニアリングプロジェクト向けのステップバイステップ構築計画に変換します。各ステップには自己完結型コンテキストブリーフがあり、新しいエージェントがそれをコールドで実行できます。 敵対的なレビューゲート、依存グラフ、平行ステップ検出、アンチパターンカタログ、計画変更プロトコルを含みます。 トリガー：ユーザーが複雑なマルチPRタスク用の計画、ブループリント、またはロードマップをリクエストするか、複数のセッションが必要な作業を説明する場合。 トリガーしない場合：タスクが単一のPRまたは3未満のツール呼び出しで完成可能な場合、またはユーザーが「単にやってくれ」と言う場合。

Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects: one-PR-sized steps with self-contained context briefs, dependency graph with parallel-step detection, adversarial review gate, and plan mutation protocol. Use when planning a large feature, refactor, or roadmap that spans multiple PRs or sessions; not for single-PR tasks or when the user says "just do it".

Turns a one-line goal into a step-by-step build plan for multi-session, multi-agent engineering projects. Each step includes a standalone context brief so a new agent can execute it directly. Includes adversarial review gates, dependency graphs, parallel step detection, an anti-pattern catalog, and a plan mutation protocol. Triggers when the user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work requiring multiple sessions. Does not trigger when the task can be completed in a single PR or fewer than 3 tool calls, or when the user says "just do it."

Answers BMad questions and recommends the next skill from what is installed. Use when the user asks bmad for help, what to do next or where to start; to set up, update, repair, doctor, migrate or check the status of the installation, or add modules; or to see or change the active initiative.

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team

Business analyst for market research, competitive analysis, and requirements. Use when the user asks to talk to Mary or requests the business analyst