Skill UI

A comprehensive presentation template designed for educational workshops and training sessions. It features a warm paper aesthetic, persistent learning objectives on the left sidebar, core content slides, interactive Multiple Choice Question (MCQ) self-assessment pages, and module wrap-ups, ensuring a structured and engaging learning experience.

This comprehensive single-page template guides new hires through their first week. It integrates the welcome message, detailed 5-day schedule, key contacts (Manager/Buddy), necessary equipment checklist, and structured learning tracks. It ensures new employees understand their immediate journey and completion standards upon joining.

This framework guides AI agents to critically assess their own output across five key axes: Accuracy, Completeness, Clarity, Actionability, and Conciseness. It provides a structured, evidence-based scorecard and concrete suggestions for improvement, turning every task completion into a deep learning opportunity.

This powerful skill leverages learned patterns and the current operational state to predict the most efficient next action. It guides complex workflows by offering immediate, high-confidence predictions. If confidence is low, it intelligently falls back to task priority lists. After every completed task, it utilizes a robust learning loop to update success patterns, ensuring continuous process improvement and cross-session memory recall.

Generates structured CodeTour `.tour` files in JSON format for detailed, step-by-step code walkthroughs. This tool is essential for onboarding new engineers, conducting architectural reviews, or documenting complex PR changes. It provides a guided narrative that directs users to specific files and line anchors, ensuring a structured and focused learning journey.

This tool enables the definition and management of structured cognitive patterns for advanced AI agents. It guides decision-making by codifying complex reasoning strategies, including decision trees, chain of thought (CoT), and analogy mapping. Use it to build robust, self-improving agents that can solve complex problems step-by-step and track their learning status.

This skill analyzes completed code sessions to automatically extract reusable patterns, transforming raw interaction data into structured, learned skills. It allows the AI to continuously improve its domain knowledge by capturing techniques like error resolution, debugging methods, and workarounds, making it effective for advanced coding assistance and knowledge curation.

An advanced system that analyzes coding sessions via hooks to extract atomic, confidence-scored behaviors called "instincts." It tracks patterns, allowing users to evolve these instincts into comprehensive skills, commands, or agents. V2.1 introduces project-scoped learning, preventing knowledge contamination between different codebases and improving development workflow efficiency.

A Stop hook that mechanically blocks Claude from finishing until quality checks pass. Detects rationalization patterns, stale learning logs, and low disk space using deterministic checks. Enforces learning capture habits for complex tasks.

This skill helps you write growth log entries that extract reusable patterns from completed work, focusing on root causes and transferable rules rather than diary-style narration. It guides you to capture learnings after complex tasks, debugging, or failures using a specific template, ensuring knowledge compounds across sessions.

Run a GEPA learning cycle via metaharness learn to optimize a harness genome against a SWE-bench-style slice manifest. Supports dry-run for cost estimation and --run for explicit execution. Requires a metaharness repo checkout.

Runs the upstream metaharness-darwin security bench, evolving a champion security-detection harness against a 10-vuln/9-decoy corpus. Grades TPR/FPR/patch-pass/repro/unsafe vs four baselines, supporting ADR-155 nightly self-learning security harness. Degrades gracefully when the upstream package is absent.