Results for “moloch”

Build cross-platform AI chat interfaces with Makepad using MolyKit, including async utilities, chat widgets, and OpenAI-compatible client integration.

Adopts the voice and comedic methodology of filmmaker Mel Brooks to craft parody, satire, and humor in responses.

CRITICAL: Use for MolyKit AI chat toolkit. Triggers on: BotClient, OpenAI, SSE streaming, AI chat, molykit, PlatformSend, spawn(), ThreadToken, cross-platform async, Chat widget, Messages, PromptInput, Avatar, LLM

Provides a persona that applies Elon Musk's engineering and management principles, including first principles reasoning, the 5-step algorithm, and physics-based analysis, to problem-solving and decision-making.

Matryoshka Representation Learning

MolyKit Skill

Implement a single-story MoonSpec task breakdown with test-driven development. Use when the user asks to run or reproduce `/moonspec.implement`, execute `tasks.md`, build the story from a MoonSpec plan, write failing unit and integration tests before production code, mark tasks complete, or prepare the implementation for `/moonspec.verify`.

Expert guidance for Mojo, the programming language by Modular that combines Python's usability with C-level performance. Helps developers write high-performance AI/ML code, optimize numerical computations with SIMD and parallelism, and gradually port Python code to Mojo for orders-of-magnitude speedups.

CRITICAL: Use for MolyKit AI chat toolkit. Triggers on: BotClient, OpenAI, SSE streaming, AI chat, molykit, PlatformSend, spawn(), ThreadToken, cross-platform async, Chat widget, Messages, PromptInput, Avatar, LLM

CRITICAL: Use for MolyKit AI chat toolkit. Triggers on: BotClient, OpenAI, SSE streaming, AI chat, molykit, PlatformSend, spawn(), ThreadToken, cross-platform async, Chat widget, Messages, PromptInput, Avatar, LLM

Bidirectional conversion between Haskell and Roc. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Haskell↔Roc specific patterns. Use when migrating Haskell applications to Roc's platform model, translating lazy pure functional code to strict platform-based architecture, or refactoring type class based designs to ability-based patterns. Extends meta-convert-dev with Haskell-to-Roc specific patterns.

You are an expert in MobX, the simple and scalable state management library based on transparent reactive programming. You help developers build React applications with observable state, automatic tracking of dependencies, computed values, actions for state mutations, and reactions for side effects — providing a natural, class-based or functional approach where the UI automatically updates when state changes without manual subscriptions.

Enables AI agents to pay humans with USDC via molty.cash, supporting tips, hiring for tasks, and creating gigs with on-chain settlement on Base.

CRITICAL: Use for MolyKit AI chat toolkit. Triggers on: BotClient, OpenAI, SSE streaming, AI chat, molykit, PlatformSend, spawn(), ThreadToken, cross-platform async, Chat widget, Messages, PromptInput, Avatar, LLM

Drive Moli (`moli`), Lexmount's open-source headless browser for AI agents, built around on-demand rendering: real JavaScript, DOM, and CSS by default, with layout and pixels computed only when explicitly requested via `--layout`. Use when the user wants to fetch/extract a live JavaScript-rendered page as Markdown/HTML/JSON/semantic-tree, capture a screenshot or PDF, run a small bounded crawl, start a CDP/WebDriver automation server for Playwright/Puppeteer, replace a Chromium/ChromeDriver dependency, or diagnose readiness/network/frame issues on a rendered page. Triggers on: "moli fetch", "moli serve", "headless browser for agents", "on-demand rendering browser", "CDP server without Chrome", "structure-first web scraping", "Lexmount browser", "moli-webfetch", "moli-cdp-server".

Automates Slack team communication with bots, notifiers, and slash commands for alerts and workflow automation.

Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, implementing the dialectic chat endpoint for AI agents, or wiring Honcho into bot frameworks (nanobot, openclaw, picoclaw, etc).

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems. Use PROACTIVELY for database optimization or complex analysis.

索隆（少年漫）认知与表达框架（压缩蒸馏）：路痴反差、三刀流执念、副船长担当 触发：海贼王 等。虚构；非暴力教唆

{'CRITICAL': 'Use for MolyKit AI chat toolkit. Triggers on:'}

Use when moc expertise is needed to unblock implementation decisions.

CRITICAL: Use for MolyKit AI chat toolkit. Triggers on: BotClient, OpenAI, SSE streaming, AI chat, molykit, PlatformSend, spawn(), ThreadToken, cross-platform async, Chat widget, Messages, PromptInput, Avatar, LLM

Heroku Slack Agents

CRITICAL: Use for MolyKit AI chat toolkit. Triggers on: BotClient, OpenAI, SSE streaming, AI chat, molykit, PlatformSend, spawn(), ThreadToken, cross-platform async, Chat widget, Messages, PromptInput, Avatar, LLM

Configure Kotlin build and quality tooling, including Gradle Kotlin DSL, Version Catalogs, KMP, ktlint, Detekt, MockK, coverage, and quality plugins. Use for Kotlin-specific build, lint, test-tool, or coverage setup; defer Java-only tooling and Spring test behavior.

Agent skill for github-modes - invoke with $agent-github-modes

Self-hosted open-source collaboration platform — chat, bots, webhooks, plugins, AI integration, Slack/Teams alternative with full data control. Triggers on: 'mattermost', 'self-hosted slack alternative', 'open source teams alternative', 'mattermost bot', 'mattermost webhook', 'mattermost plugin', 'mattermost API', 'mattermost integration', 'AI agent mattermost', 'deploy mattermost', 'mattermost docker', 'mattermost kubernetes', 'enterprise chat self-hosted', 'mattermost AI features', 'mattermost incoming webhook', 'mattermost slash command'.

MLOps lead for deployment, monitoring, and scaling. Use when the user asks to talk to McGonagall, requests deployment help, or needs CI/CD for AI systems.

Guides through mocha test setup with step-by-step instructions, best practices, and production-ready configurations.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.

Bluesky + X social loop. The bundled notifications poller runs `social-cli sync` on cron (default `*/15`), parses the per-platform `inbox-<platform>.yaml` files, and wakes the agent in batches of up to 3 never-seen notifications per turn. The optional feed poller runs `social-cli feed` every 2h for timeline scanning. Agent reads inbox, writes `outbox-<platform>.yaml`, runs `social-cli dispatch`. Also supports one-shot commands (post/reply/thread/like). Opt-in: install the skill, drop `.env` credentials into `<home>/state/pollers/social-cli-notifications/`. Companion to the `pollers` framework skill and the `world-scanning` skill.

Interact with Moltbook social network for AI agents. Post, reply, browse, and analyze engagement. Use when the user wants to engage with Moltbook, check their feed, reply to posts, or track their activity on the agent social network.

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

Design and implement ML operations — model registry, serving patterns, deployment strategies (shadow/canary/blue-green), drift detection, feature stores, retraining triggers, and prediction monitoring. Use when asked to "deploy a model", "model registry", "MLflow", "feature store", "drift detection", "retrain trigger", "shadow mode", "model versioning", "serving infrastructure", or "ML pipeline". Do NOT use for: prompt engineering or RAG pipelines — see prompt-engineering and rag-architect skills. Do NOT use for: general API deployment without an ML component.