Skill UI

Scores a manuscript against a specific venue's documented desk-reject triggers and returns a ranked, fixable risk report. Use before submission after mechanical readiness is confirmed.

This skill covers core principles for 2D game development, including sprite systems, atlases, tilemaps, physics engines, and camera controls. It addresses genre-specific patterns like platformers and top-down games. Suitable for frameworks like Phaser, Kaplay, Pixi, or Canvas. Also applies to guest viewports in hybrid web apps. Focuses on performance optimization, collision handling, and animation techniques to ensure clarity and smooth gameplay.

This skill covers fundamental principles for 3D game development, including rendering pipelines, shader programming, physics systems, camera controls, lighting techniques, and level of detail optimization. It helps developers create efficient and visually appealing 3D environments by understanding optimization strategies like frustum culling, occlusion culling, and batching. Suitable for implementing core game mechanics and profiling rendering performance.

A comprehensive guide for designing and implementing 3D user interfaces across web, mobile, and cross-platform environments. Learn core principles like perspective transformations, Z-axis depth, and interactive rotation to build visually stunning, spatially immersive, and highly engaging product experiences.

This skill routes goals through the acc's scored-memory loop using acc_act with runtime="solve". It enables deliberation on returned brain_frames and submission via continue. Users must reason over frames and end proposals with a prediction score. It ensures honest commitment closure and prevents solo-derivation outside the loop.

Design LLM-as-judge rubrics, direct scores and pairwise comparisons. Use for judge models, evals, position bias and calibration against human labels.

Evaluate AI agent behavior using versioned cases and explicit verifiers. This skill helps compare agent or prompt changes, reproduce failures, and run regression tests. It ensures reliability on declared task distributions without inferring product readiness from generic scores. Includes methods for uncertainty reporting and safety verification.

Audit local AI coding-agent sessions with agenttrace to inspect token and cost spikes, tool failures, retry loops, latency, anomalies, health scores, diffs, and CI gates. Use for debugging slow, expensive, or unreliable runs and comparing attempts.

AKF is an AI-native file format that embeds trust scores, source provenance, and compliance metadata into various file types like DOCX, PDF, and code. It supports auditing for regulations such as the EU AI Act, SOX, and HIPAA, ensuring transparency and accountability in AI-generated content workflows.

This skill is designed to prevent AI coding assistants from exhibiting sycophantic agreement bias. When active, it forces a structured, critical assessment of user claims. The process involves extracting the core claim, assessing it independently with evidence (for/against), and concluding solely based on that assessment, ensuring objective and evidence-based responses.

Run protected AAS maintainer sweeps, PR merge batches, canonical sync, Core preview checks, and scripted releases. Use for repository maintenance, main alignment, CLI/MCP/Workbench changes, or release work; not ordinary contribution tasks.

Spectral vector search using graph Laplacian eigenstructure. Augments nearest-neighbour search with lambda-tau scores, capturing both semantic similarity and structural role in embeddings. Ideal for RAG pipelines and graph-based retrieval where cosine/L2 similarity misses latent structure.