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JS/TS/JSX toolkit with Bun awareness. Use when using Bun as a runtime, test runner, or package manager in JavaScript and TypeScript projects.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | bun-toolkit |
| description | JS/TS/JSX toolkit with Bun awareness. Use when using Bun as a runtime, test runner, or package manager in JavaScript and TypeScript projects. |
Bun is a fast, all-in-one JavaScript runtime and toolkit.
bun install is significantly faster than npm or yarn.bun test provides a fast, Jest-compatible testing environment.bun build for bundling JS/TS for the web or other runtimes.bun init # Start a new project
bun install # Install dependencies
bun add <package> # Add a package
bun run <script> # Run a package.json script
bun <file.ts> # Run a TS/JS file directly
bun test # Run tests
bun build ./index.ts --outdir ./build # Build for production
.ts and .tsx files out of the box. No separate compilation step needed..env files. Access via process.env or Bun.env.Bun.file(path) and Bun.write(path, content) for high-performance file operations.Bun.serve({ fetch(req) { ... } }) for an extremely fast built-in web server.import { Database } from "bun:sqlite".bun.lockb file.Route durable personal context and reusable knowledge between augmentedcode-configuration and the personal knowledge vault. Use when the user asks to remember, persist, capture, retrieve, or organize personal knowledge; when deciding whether information belongs in rules, skills, portfolio, notes, maps, or inbox; or when using personal-knowledge-vault as a low-context reference layer.
Use when running git clone, git remote add, git remote set-url, or suggesting GitHub SSH commands to ensure all GitHub access uses the saski SSH host alias.
Build modern data apps, dashboards, and interactive reports using either React + Vite or Streamlit. Includes optional Gemini Data Analytics chat integration for an AI powered "chat with your data" experience. Relevant when any of the following conditions are true: 1. User explicitly requests to build a data dashboard, data application, or visualization UI, and the UI pulls data from a GCP database (defaulting to BigQuery unless otherwise specified). 2. You need to generate a frontend web application to interact with, query, and visualize data from GCP data sources. 3. User wants to build a "chat with your data" experience or integrate the Gemini Data Analytics chat API into a web interface. Do NOT use when any of the following conditions are true: 1. The request is for building backend-only services. 2. The request is for simple CLI scripts or command-line applications. 3. The web application is not data-centric or does not involve visualizing/querying data from GCP sources.
Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.
Use when the user wants to clean up AI slop, harden a codebase, or run technical due diligence: review code for security flaws and vulnerable or wrongly-licensed dependencies, find and remove dead code, audit dependency health, run and triage linters, type checkers, test suites and formatters, evaluate design principles (SOLID and the cohesion/coupling equivalents for non-OO stacks), audit logging and observability, find performance smells, check documentation and CI health, scaffold baseline configs for the detected stack, do a structured code review, guide a one-major-at-a-time dependency migration, fix project structure, or produce one aggregated health report across all of these. Triggers on phrases like clean this up, find security issues, remove dead code, check dependencies, lint, typecheck, run the tests, format, review my code, check the design, check logging, is this slow, migrate a dependency, fix the project structure, set up the project, audit everything, due diligence, or this looks like AI slop
Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.