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Found 749 Skills
When the user wants to improve E-E-A-T, add trust signals, or optimize for expertise and authority. Also use when the user mentions "E-E-A-T," "E-E-A-T signals," "experience expertise authority trust," "author bio," "YMYL," "trust signals," "expertise signals," "authority signals," "citations," "references," or "credibility."
Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with standard unit tests).
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-analysis, write-judge-prompt, or validate-evaluator instead).
Skill for creating custom lint rules by leveraging the existing linter ecosystems of various programming languages. This is a linter designed for AI Agents rather than humans, and its error messages function as correction instruction prompts for AI. Create custom rules in the `lints/` directory using standard methods for each language, including Rust (dylint), TypeScript/JavaScript (ESLint), Python (pylint), Go (golangci-lint), etc. Use this skill in the following scenarios: (1) When you want AI to enforce project-specific coding rules; (2) When you want to create lint rules that output AI-readable correction instructions when violations occur; (3) When you want to enforce naming conventions, structural patterns, and consistency rules through AI-driven linting. Triggers: "Create a linter rule", "Add a lint rule", "Enforce this pattern", "AI linter", "Custom lint", "Code rules", "Naming rules", "Structural rules", "create a linter rule", "add a lint rule", "enforce this pattern", "AI linter".
Implement advanced SLAS authentication patterns in B2C Commerce. Use when implementing passwordless login (email OTP, SMS OTP, passkeys), session bridging between PWA and SFRA, hybrid authentication, token refresh, or trusted system authentication. Covers authentication flows, token management, and JWT validation.
Tauri 2.0 project setup, Rust backend + web frontend, plugin system, IPC commands, security model, auto-update, and mobile support. Use when building lightweight cross-platform desktop or mobile apps with Tauri.
Upgrade Stylus smart contracts using OpenZeppelin proxy patterns on Arbitrum. Use when users need to: (1) make Stylus Rust contracts upgradeable with UUPS or Beacon proxies, (2) understand Stylus-specific proxy mechanics (logic_flag, WASM reactivation), (3) integrate UUPSUpgradeable with access control, (4) ensure storage compatibility across upgrades, or (5) test upgrade paths for Stylus contracts.
Smart contract and secure API contract security analysis — invariant checking, access control, reentrancy, and integer overflow patterns. Implements Checks-Effects-Interactions pattern, formal invariant verification, and OpenSCV vulnerability taxonomy for Solidity/EVM and Rust/Solana contracts.
Scan untrusted external text (web pages, tweets, search results, API responses) for prompt injection attacks. Returns severity levels and alerts on dangerous content. Use BEFORE processing any text from untrusted sources.
Deep code simplification, refactoring, and quality refinement. Analyzes structural complexity, anti-patterns, and readability debt, then applies targeted refactoring preserving exact behavior. Language-agnostic: Python, Go, TypeScript/JavaScript, Rust. Use this skill when the goal is simplification and clarity rather than bug-finding. Triggers on: "simplify this code", "clean up my code", "refactor for clarity", "reduce complexity", "make this more readable", "code quality pass", "tech debt cleanup", "run the code refiner", "simplify recent changes", "this code is messy", "too much nesting", "this function is too long", "clean this up before I PR it", "tidy up my code", cyclomatic complexity, cognitive complexity, code smells.
Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
Forces exhaustive problem-solving using corporate PUA rhetoric and structured debugging methodology. MUST trigger when: (1) any task has failed 2+ times or you're stuck in a loop tweaking the same approach; (2) you're about to say 'I cannot', suggest the user do something manually, or blame the environment without verifying; (3) you catch yourself being passive — not searching, not reading source, not verifying, just waiting for instructions; (4) user expresses frustration in ANY form: 'try harder', 'stop giving up', 'figure it out', 'why isn't this working', 'again???', or any similar sentiment even if phrased differently. Also trigger when facing complex multi-step debugging, environment issues, config problems, or deployment failures where giving up early is tempting. Applies to ALL task types: code, config, research, writing, deployment, infrastructure, API integration. Do NOT trigger on first-attempt failures or when a known fix is already executing successfully.