Skill UI

This skill provides a structured methodology for quantifying and scoring competitors based on nine weighted dimensions. By using explicit 1–5 rubrics and anchoring scores to observable evidence, it transforms raw competitive data into defensible, comparable profiles. It is essential for producing actionable insights in a comprehensive competitive report, especially when establishing a target white-space based on client strategic tension.

This skill provides a rigorous, structured method for inspecting animated 3D characters, rigs, and motion retargeting in Blender. It moves beyond visual evidence (screenshots) by extracting and comparing structured data points such as bone transformations, bounding box changes, ground contacts, and axis conventions. Ideal for diagnosing subtle animation flaws like foot sliding, ground penetration, or incorrect orientation that are invisible in still images.

This skill is designed for complex, iterative decision-making processes requiring deep computation, such as stochastic optimization, high-dimensional search, or repeated model rollouts. It mandates the creation of a structured 'Ledger' to rigorously record every trial, prior winner, fresh information, and decision mark. It ensures that conclusions are based on durable evidence, managing risk through explicit promotion gates and preventing premature 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 multiple, scattered research findings retrieved from memory into a single, structured, and actionable report. It automatically grades evidence quality (High/Medium/Low), resolves conflicting claims, and provides concrete, recommendation-based insights, making complex data digestible for decision-making.

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).

This skill executes comprehensive historical backtesting using the high-performance neural-trader engine. It supports features like walk-forward validation and captures key financial metrics (Sharpe, Drawdown). Critically, it signs the resulting artifact using Ed25519 cryptography, providing non-repudiable tamper evidence required for promoting research models to live trading systems.

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.

Guides builders to verify task delivery by collecting required evidence such as CLI output, screenshots, or DOM snapshots, then submitting artifacts for review. Useful when a task must self-certify against a verify plan.

A comprehensive tool for finding solutions to software errors, API usage issues, and compatibility problems by searching major developer communities, official documentation, GitHub, Stack Exchange, and various Chinese technical forums. Ideal for debugging and discovery workflows, it aggregates real-world workarounds and technical Q&A, but results should not be cited as academic evidence.

This advanced tool cross-checks all factual marketing claims—including statistics, awards, rankings, and performance metrics—against provided source evidence. It classifies claims as Verified, Unverified, or Contradicted, ensuring brand content is accurate, defensible, and minimizes reputational risk. It generates detailed discrepancy reports and suggests compliance-ready corrections.