Skill UI

A structured framework for managing complex, iterative, and high-stakes decision processes (e.g., stochastic optimization, ensemble comparison, model rollouts). It mandates the creation of a detailed, auditable 'ledger' that records every prior result, new input, search space metric, and decision mark. This tool enforces rigorous evidence trails and defaults to safe, dry-run modes, ensuring that confidence is built through verifiable, step-by-step proof rather than assumed certainty.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

This tool synthesizes deep, accumulated research findings stored in memory into structured, actionable reports. It systematically grades evidence quality (High, Medium, Low), identifies and resolves conflicting claims, and generates comprehensive outputs including an executive summary, ranked key findings, limitations, and concrete recommendations. Ideal for transforming raw research data into strategic insights.

This skill guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

Performs comprehensive historical backtesting for trading strategies using the high-performance neural-trader engine (Rust/NAPI). This skill supports walk-forward validation and captures key performance metrics. Critically, it Ed25519-signs the final artifact to provide cryptographic tamper evidence, ensuring data integrity for promotion from paper to live trading environments.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

This skill is designed to audit and critique existing skill packages for their readiness, safety boundaries, resource efficiency, and evidence quality. It helps users diagnose concrete issues, such as incorrect triggers or insufficient evidence, and suggests improvements without applying any changes. Use it when you need a comprehensive audit or quality grade for a published skill.