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Found 1,140 Skills
🐟 Rust-native Fish shell-friendly file operations with Steel-backed SCI Clojure evaluation.
Apply principles of good design taste when creating, reviewing, or critiquing any creative or technical work. Use this skill whenever the user asks you to design something, review a design, create UI/UX, architect a system, write something with aesthetic intent, evaluate the quality of code or creative work, or asks for feedback on whether something is "good." Also trigger when users mention taste, aesthetics, beauty in design, elegance, simplicity, or when they want help making something not just functional but genuinely well-crafted. This skill applies across domains: software, writing, visual design, architecture, presentations, APIs, data models, and more. Even if the user doesn't explicitly mention "design," use this skill when the underlying task is about making something better, more elegant, or more refined.
Comprehensively reviews Python libraries for quality across project structure, packaging, code quality, testing, security, documentation, API design, and CI/CD. Provides actionable feedback and improvement recommendations. Use when evaluating library health, preparing for major releases, or auditing dependencies.
Use when seeking analogous solutions from other domains, when stuck on a problem and need fresh perspectives, or when evaluating whether approaches from field X might apply to field Y. Requires structured problem statement.
Language-agnostic guidance for selecting and applying Gang of Four (GoF) design patterns to recurring object-oriented design problems. Use when deciding among design alternatives, evaluating applicability and tradeoffs, or refactoring rigid/conditional-heavy designs toward better extensibility and lower coupling. Do not use for trivial bug fixes, framework/tool setup, or tasks with no architectural decision. Any TypeScript examples are illustrative only and must be translated to the project's language and constraints.
Retrieve consensus price targets for any stock using Octagon MCP. Use when you need the average, median, high, and low analyst price targets to evaluate upside/downside potential and analyst agreement.
Analyze an influencer's audience demographics to determine whether their followers match your target customer, with a clear pass/fail verdict. This skill should be used when evaluating audience fit, checking influencer demographics, analyzing audience data, reviewing an audience breakdown, assessing demographic alignment, vetting an influencer's audience, determining if a creator's followers match your target demo, reviewing a platform export or stats screenshot, pasting influencer stats, grading audience quality, deciding whether an influencer's audience is a good fit, checking if this creator is worth it, running an audience report, comparing creator audiences, or evaluating audience overlap with target demo. For overall creator vetting beyond demographics, see creator-vetting-scorecard. For finding new creators, see creator-discovery.
Respond to PR review comments with critical evaluation. Use when addressing code review feedback, responding to bot review comments (Gemini Code Assist, CodeRabbit, etc.), or handling PR suggestions. Fetches comments, evaluates each against project context, applies valid fixes, declines invalid suggestions with reasoning, and posts responses.
Nonprofit fundraising performance analysis with donor segmentation, campaign ROI, retention metrics, and trend analysis. Use when evaluating fundraising effectiveness, analyzing donor data, or planning campaigns.
Analytical thinking patterns for comprehensive evaluation, code audits, security analysis, and performance reviews. Provides structured templates for thorough investigation with extended thinking support.
Product vision, roadmap development, and go-to-market execution with structured prioritization frameworks. Use when evaluating features, planning product direction, or assessing market fit.
Evaluates and optimizes agent skills using a DSPy-powered GEPA (Generate/Evaluate/Propose/Apply) loop. Loads scenario YAML files as DSPy datasets, scores outputs with pattern-matching metrics, and optimizes prompts via BootstrapFewShot or MIPROv2 teleprompters. Also generates new scenario YAML files from skill descriptions.