Skill UI

Guides product teams through hypothesis writing, metric definition, sample-size planning, ICE prioritization, and statistical interpretation so experiments stay practical, defensible, and aligned with business decision thresholds.

This skill explains complex concepts using the Socratic method. Instead of delivering direct answers or textbook definitions, it engages the user in a reflective, conversational dialogue. By guiding the user through thought experiments, small observations, and simple analogies, the assistant helps the user construct understanding step-by-step, facilitating deeper clarity through active reasoning. Ideal for understanding theories, abstract ideas, or system mechanics.

A structured methodology for de-risking product ideas and validating market opportunities. This framework guides teams through defining measurable outcomes, building Opportunity Solution Trees (OST), mapping critical assumptions (Desirability, Viability, Feasibility), and planning rigorous experiments (prototyping, interviews, usability tests) before committing to full development resources. Ideal for discovery sprints and pre-build validation.

This skill initializes an autonomous, scheduled loop for running complex research or engineering experiments. Users specify the target experiment and select a recurring interval (e.g., 10 minutes, daily, weekly). It utilizes cron scheduling to ensure continuous research, guiding iterative changes, evaluating metrics, and committing results automatically until manually stopped.

Allows users to resume an interrupted or paused autoresearch experiment. It automatically loads the full context, including configuration, strategy files, complete results history, and recent code changes. The system then summarizes the current best metrics and patterns, prompting the user to continue with a single iteration, set up an automated loop, or manually review the results.

This skill initializes a new autoresearch experiment. It guides the user through defining all necessary parameters, including the domain, target files, evaluation commands, metrics, and optimization direction. It is the essential starting point for automated optimization and performance benchmarking cycles.

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

This tool is designed to analyze complex Machine Learning experiment results from various sources (JSON/CSV). It computes detailed statistics, builds comprehensive comparison tables across hyperparameter variations, and generates structured, actionable insights. It helps researchers interpret findings, identify trends, and propose next steps for research.

This skill transforms a refined research proposal or method idea into a detailed, structured, and claim-driven experiment roadmap. It helps users determine exactly which experiments to run, in what order, and what evidence is needed to support the core claims of a research paper, ensuring focus on novelty, simplicity, and genuine contribution.

This utility integrates status updates and notifications with Feishu/Lark. It is designed as an internal, zero-impact tool that other complex skills (e.g., experiment runners, review loops) call to report key events like test completion, scoring results, or reaching a checkpoint. It supports both 'push' (fire-and-forget status updates) and 'interactive' (bidirectional approval/rejection) modes, ensuring seamless workflow visibility and robust operation even if configuration is missing.

This sophisticated workflow guides users from a broad research concept or initial topic to a fully validated, actionable, and pilot-tested research idea. It orchestrates a multi-stage pipeline, including literature surveys, novelty checks, detailed idea generation, critical review, and the creation of a refined experimental plan, ensuring the output is a robust proposal ready for implementation.

This skill guides the implementation of security chaos engineering experiments. It involves deliberately degrading or disabling security controls (like WAF, firewalls, or log pipelines) in a controlled environment to rigorously verify the detection, alerting, and response capabilities of the Security Operations Center (SOC). Users learn to use tools like boto3 and Python to simulate failure scenarios and assess system resilience and compliance posture.