A bilingual Chinese-English health-recording guide for bowel movements, including stool shape, frequency, food clues, and warning signs. Bristol stool types are a commonly used seven-category description of stool shape.
Generate synthetic and simulated datasets for evaluation and fine-tuning using Azure AI Foundry simulators. Create non-adversarial task data, adversarial safety data, and conversation datasets without manual data collection.
Train or fine-tune LLMs on Azure ML managed compute with TRL trainers. Uses direct trainer loops (SFT, DPO, RL) without relying on serverless APIs or Hugging Face infrastructure.
Evaluate generative AI applications and models locally or in the cloud using Azure AI Evaluation SDK. Measure quality, safety, and performance with built-in and custom evaluators.
A lightweight project-maintenance workflow documented in Chinese and English. It reads the project's current files and records what is confirmed, inferred, unknown, changed, and verified.
Fable-class agentic operating discipline, gated to task risk and domain so overhead scales with stakes. Trigger on: explicit /fable-skill or $fable-skill; debugging or diagnosing a failure; changes spanning multiple files; refactors and migrations; irreversible or destructive actions; ambiguous goals needing…
A routing guide for organising work between multiple AI agents, which are separate helpers with assigned responsibilities. It is used only when the user explicitly calls it.
Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a…
A workspace analysis tool that creates or updates a Project Structure Guide, a map of where files and assets are stored and how they depend on one another. The guide describes structure, not build, deployment, or run instructions.
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Allows agents (specifically orchestrator and devops-engineer) to interact with the Agentica Exchange to keep the workspace updated with the latest specialist agents, code patterns, and security definitions.
Automatic agent selection and intelligent task routing. Analyzes user requests and automatically selects the best specialist agent(s) without requiring explicit user mentions.
Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.
Review an ADR the user has written in docs/adr/. Use when the user says an ADR is ready for review or asks for feedback on a decision record. Never use this to write or rewrite an ADR for them.
Produce or continue a deep-dive learning-track lesson (a focused mini-course on a hard subject the project needs), or review the user's track exercise work. Use when the user asks for the next lesson, help with a track exercise, or to start a new track.
Produce the next guided build doc (six-part - Concept, ADR, Guided build, Challenges, Verify, Stretch). Use when the user asks to start a phase, begin a milestone, or wants the next build step.
Perform a deterministic, graph-driven Change Impact Analysis for a repository. Given a set of changed files, builds a dependency graph, finds directly and transitively affected modules, validates API contracts for breaking changes, parses CODEOWNERS, and computes a 0-100 deployment risk score. Trigger on: change…
Reverse-engineer / discover a codebase. Works in two modes — provide EXACTLY one input: repourl (a github.com URL) clones the repo server-side and runs full static analysis, producing a System Design Document, architecture + dependency overview, and a 100-point quality score; repopath (a local directory the server can…
Prepare and normalize Xquik X data for MCP clients. Build request plans for tweet search, tweet lookup, and trends, or normalize supplied Xquik JSON into compact records. Trigger when the user asks for Xquik, X data, Twitter data, tweet search, trends, or social data ingest.
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originalMIT
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: