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Provides reusable “skill” instruction bundles that teach AI coding tools how to author, query, and operate Microsoft Fabric workloads via REST APIs, T-SQL, KQL and notebooks. Includes Copilot CLI/Claude/Cursor integrations, workload-focused bundles, and optional MCP configurations for live data access.
Performs deterministic, sub-millisecond policy checks on every agent action (allow/deny + audit) and adds zero-trust identity, execution sandboxing, and SRE features. Covers the OWASP Agentic Top 10 and is designed to sit between agent frameworks and runtime actions for auditable, low-latency governance in production.
A 228,557-example dataset of reasoning traces segmented into blocks with iterative, compressed "memento" summaries so LLMs can learn to manage long context. Includes a training-ready subset and a `full` subset with sentence/block-level annotations for research and SFT.
Provides a pytest-native framework to write safety and security tests for agentic AI applications. Defines adversarial attacks, benign-failure suites, and harm-category assertions with evaluation-driven checks and CI-friendly reporting, so red-teaming becomes testable and automatable.
Parses local AI coding-assistant session logs and presents a privacy-first dashboard that surfaces practice scores, anti-patterns, code-output metrics, skill discovery, and context-health checks. Runs as a VS Code extension or a GitHub Copilot canvas; requires building/installing the VSIX.
Trains reusable natural-language 'skills' for frozen LLM agents by optimizing the skill document in text-space — using trajectory-driven edits, validation-gated updates, and deployable best_skill.md artifacts. Multi-backend, zero inference-time cost at deployment, designed for iterative, validation-led skill improvement.
Lets AI agents produce expressive, polished charts from compact, human-editable semantic specs; the compiler infers layout, scales, and labels and emits Vega-Lite, ECharts, or Chart.js outputs, with an MCP server for agent-driven chart creation and rendering.
Research-focused text-to-image foundation model that prioritizes training efficiency: a 3.8B-parameter architecture trained on an 800M image-text corpus with mixed-resolution learning, FLUX.2 VAE, RL tuning, and a distilled 4-step Lens-Turbo for fast high-resolution generation.
A 4-step distilled variant of Microsoft's Lens foundational text-to-image model for fast, high-resolution image synthesis. Optimized for mixed-resolution inference up to 1440×1440, GPT-OSS text features and FLUX.2 latents, intended for low-latency prototyping and research under an MIT license.
Moves repository search into a dedicated exploration subagent that issues parallel read-only READ/GLOB/GREP calls and returns compact file:line citations. Trained (4B–30B) with SFT+RL, it reduces main-agent token use up to ~60% and raises end-to-end success by up to ~5.5%.
Provides a lightweight repository-exploration subagent for LLM coding agents: invoked on demand to run parallel read-only READ/GLOB/GREP calls and return compact file-path plus line-range citations so the main solver gets focused evidence instead of noisy reads.