AI Dataset2026
Curates ~1.1M instruction–response examples for 'vibe coding' scenarios where developers prompt LLMs to produce implementation plans, architecture choices, and deployment steps. Covers conversation memory, prompt templates, model routing, streaming responses, and scaling considerations; Apache-2.0.
AI Agent2026
Provides a ruleset and skills that make AI coding agents prefer the simplest correct implementation: reuse existing code, prefer stdlib/native features, and only write minimal new code. Cuts generated LOC, tokens, cost and time while preserving validation and safety.
AI Others2026
Collects 31 system prompts, persona definitions, and agent operating specifications targeting 25+ models. It offers material for studying instruction hierarchies and prompt-injection defenses, though behavior varies by model version.
AI Agent2026
Self‑hosted A‑share quantitative workbench for screening, monitoring, backtesting and stock-level analysis using TickFlow data; supports 18 Polars strategies, vectorbt backtesting, realtime rule-based alerts, pluginable data sources and optional LLM-driven strategy generation and stock analysis.
AI Agent Papers2026
Proposes SkillOpt-Lite, a minimal pipeline for optimizing LLM agent skills by treating rollout traces as filesystem files and applying trajectory exploration, consensus mining, and independent validation; integrates as a one-line VSCode Copilot command and reports cross-benchmark improvements that let smaller models sometimes outperform larger ones.
AI Dataset2026
Converts long or messy model reasoning traces into concise, user-facing summaries with optional metadata. 61K cleaned English samples in JSON format, Apache-2.0 licensed, created to train and evaluate reasoning-summarization models and to present safe, readable explanations instead of raw chain-of-thought.
AI Model2026
Decides whether a user prompt should be executed locally on an edge small LLM or routed to a larger cloud model, emitting a deterministic pipe-separated decision string. A 51.7M micro-LLM fine-tuned with multi-task sequence generation to predict domain, complexity and code/math flags, optimized for ultra-low latency edge routing.
AI Dataset2026
Provides labeled prompts with full-reference answers (including chain-of-thought and code blocks) and per-example metadata to train edge routing/orchestrator models that decide whether to handle inputs locally or route them to larger models. Includes complexity scores, coding/math flags, routing justifications, and an automated override rule; suited for fine-tuning small models (50M–1.5B) for edge deployment.
Provides a reusable skill suite for evidence-grounded research ideation: Paper-Search for multi-source literature retrieval, Scoop-Check for prior-art collision checking, and IdeaSpark for pattern-guided idea generation, evidence auditing, and idea-card rendering.
AI Agent2026
Helps produce and revise natural, readable Japanese for work documents by applying a 12-item writing constitution and sudachipy-powered lint to detect and remove “AI-smelling” patterns; includes diagnostic scoring and iterative rewrite loops.
AI Agent2026
Rewrites agent-facing English into ASD-STE100 Simplified Technical English to remove parsing ambiguity for downstream AI agents. Performs sentence-by-sentence rule checks and deterministic rewrites while preserving all facts and scope qualifiers. Best for error messages, tool descriptions, and inter-agent instructions.
AI Dataset2026
Provides 4,868 Turkish instruction examples with explicit chain-of-thought traces (<think>...</think>) in a messages-format JSONL for training and evaluating step-by-step reasoning of Turkish LLMs; culturally localized and Apache-2.0 licensed.