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Found 103 Skills
Implement Syncfusion WPF SfLinearProgressBar for horizontal or vertical linear progress indicators. Use this when building determinate progress displays, indeterminate loading bars, buffer/secondary progress, or segmented progress bars in WPF. Covers RangeColors, gradient progress, IndicatorPadding, IndicatorCornerRadius, and IsIndeterminate using Syncfusion.SfProgressBar.WPF.
Implement Syncfusion WPF TabSplitter for VS 2008-style split tab views with top and bottom panel sections. Use this when building split tab layouts, dual-pane views, or side-by-side tabbed views in WPF. Covers SplitterPage, TopPanelItems, BottomPanelItems, TabSplitterItem, and collapsible split panel configuration.
Maps intelligence for local SEO — geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification (Google/Bing/Apple/OSM), competitor radius mapping, and LocalBusiness schema generation from API data. Three-tier capability: free (Overpass + Geoapify), DataForSEO (full intelligence), DataForSEO + Google (maximum coverage). Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", "maps analysis", "local rank tracking", "Share of Local Voice", or "SoLV".
Guide for implementing Syncfusion RadialMenu control in Windows Forms applications. Use when creating circular context menus, hierarchical radial navigation, or touch-friendly circular menu interfaces. Covers RadialColorPalette for color pickers, RadialFontListBox for font selection, RadialMenuSlider for numeric input, and Office 2016 themed menus for modern circular navigation beyond standard context menus.
Autonomously optimize an existing AI skill by running it repeatedly against binary evals, mutating one instruction at a time, and keeping only changes that improve pass rate. Based on Karpathy-style autoresearch, but applied to SKILL.md iteration instead of ML training. Use when optimizing a skill, benchmarking prompt quality, building evals for a skill, or running self-improvement loops on reusable agent instructions. Triggers on: skill-autoresearch, optimize this skill, improve this skill, benchmark this skill, eval my skill, run autoresearch on this skill, self-improve skill.
Use when the user needs ML pipelines, statistical analysis, data preprocessing, feature engineering, model selection, experiment tracking, or data visualization. Triggers: dataset exploration, model training, feature engineering, hyperparameter tuning, experiment tracking setup, statistical hypothesis testing, visualization creation.
Windows lateral movement playbook. Use when pivoting between Windows hosts via PsExec, WMI, WinRM, DCOM, RDP, pass-the-hash, overpass-the-hash, or pass-the-ticket techniques.
Run metric-driven iterative optimization loops. Define a measurable goal, build measurement scaffolding, then run parallel experiments that try many approaches, measure each against hard gates and/or LLM-as-judge quality scores, keep improvements, and converge toward the best solution. Use when optimizing clustering quality, search relevance, build performance, prompt quality, or any measurable outcome that benefits from systematic experimentation. Inspired by Karpathy's autoresearch, generalized for multi-file code changes and non-ML domains.
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.
Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".
Build and maintain a personal knowledge base using Karpathy's llm-wiki methodology across Claude Code, Codex, and OpenClaw agents.
Autonomously audit an LLM wiki (Karpathy pattern) for gaps, contradictions, orphans, and stale data, then research and fill high-priority gaps using quality-gated web research. Supports audit-only dry-run mode. Operates on a dedicated branch and commits changes for human review — never auto-merges. Use when the user asks to "lint my wiki", "self-heal my knowledge base", "find gaps in my wiki", "update my second brain", "auto-research my wiki", "run a health check on my LLM wiki", "audit my wiki without making changes", "dry run the lint", or wants to schedule periodic wiki maintenance.