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Found 1,564 Skills
Builds production-ready REST API endpoints with validation, error handling, authentication, and documentation. Follows best practices for security and scalability.
MUST be used whenever reviewing a Dune app for security issues, or before shipping any feature that handles credentials, user input, or external data. Do NOT skip this when the user asks for a security review, security audit, or vulnerability check — run every step in order. Triggers: security, security review, security audit, vulnerability, XSS, injection, credentials, secrets, auth, authentication, authorization, token, sensitive data, input validation, CORS, CSP, dependency audit.
Use when writing server-side code with Supabase — Edge Functions, Hono apps, webhook handlers, or any backend that needs Supabase auth and client creation. Trigger whenever the user imports from `@supabase/server`, mentions `supabase/server`, Supabase Edge Functions, or needs server-side auth (JWT verification, API key validation, CORS handling) with Supabase. Also trigger when you see legacy patterns in existing code — `Deno.serve`, `createClient(Deno.env.get('SUPABASE_URL'))`, imports from `esm.sh/@supabase`, `deno.land/std` serve, or usage of `SUPABASE_ANON_KEY` / `SUPABASE_SERVICE_ROLE_KEY` — these indicate code that should be migrated to this package.
Use this skill when users ask about form validation in SGDS, hasFeedback prop, constraint validation, custom validation, noValidate, setInvalid, form submission, or reading FormData from SGDS form components.
Create validated LLM-as-a-Judge evaluators following best practices — binary Pass/Fail judges with TPR/TNR validation for measuring specific failure modes. Use when you need to automate quality checks, build guardrails, or measure a specific failure mode identified during trace analysis. Do NOT use when failures are fixable with prompt changes (use optimize-prompt) or when failure modes are unknown (use analyze-trace-failures first).
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Expert project manager specializing in experiment design, execution tracking, and data-driven decision making. Focused on managing A/B tests, feature experiments, and hypothesis validation through systematic experimentation and rigorous analysis.
Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.
Goose-native software delivery command suite for product validation, scope challenge, planning, TDD implementation, debugging, review, QA, and release handoff
Node.js/Bun backend reference skill: TypeScript-first, structured error handling, pino logging, Zod validation, async patterns, HTTP server conventions, database access, auth, queues, caching, testing, security, CLI tooling, and observability. Covers both Node.js and Bun runtimes. Use when the task touches server-side TypeScript/JavaScript code and should follow the project's backend conventions.
Converts Opus-quality skills into deterministic Haiku-executable workflows via trace-driven distillation and cross-model validation. Triggers on: "distill this skill", "make this skill work on Haiku", "cross-model optimization", "optimize skill for cost". NOT for code simplification, use code-refiner.
Guides edge and tactical autonomous systems—perception-planning-control under latency and safety constraints; behavior trees/state machines vs learned policies; human-on-the-loop; geofencing, no-strike rules, mission abort; sim and field testing; ROS2/middleware patterns; sensor fusion; degraded modes; autonomy audit logging. Use for UAS/autonomous stacks, safety rules, HITL, sim-to-field validation, fail-safe—not LLM products (ai-engineer), LLM red team (ai-redteam), safeguard serving (ml-infrastructure-engineer-safeguards), governance only (ai-risk-governance), MCU firmware without autonomy (embedded-real-time-software-engineer), plant PLC/DCS (control-software-developer), HIL security bench (hardware-in-the-loop-security-tester).