Qredence

Execute batched or parallel RLM work using Daytona sandboxes. Use when running multiple sub-tasks, parameter sweeps, or batch recursive RLM calls in the daytona_pilot runtime.

Debug fleet-rlm runtime issues from Claude Code. Use when diagnosing daytona_pilot failures, API and websocket contract problems, sandbox persistence bugs, or runtime readiness drift.

Execute Python code in Daytona sandboxes with durable volume persistence. Use when running code in a Daytona sandbox, processing data with stateful execution, or persisting results across sessions.

(EXPERIMENTAL) Research implementation for RLM long-context processing using standalone Python scripts. For production use, prefer the rlm skill which uses the fleet-rlm package with Daytona sandboxes. This skill is for experimentation, evaluation, and alternative implementation patterns.

Long-term memory persistence for RLM using Daytona durable volume storage. Use when storing, recalling, listing, or searching data that persists across sandbox sessions in the daytona_pilot runtime.

Run fleet-rlm through its current public entrypoints. Use when you need the right command for the Web UI, API server, MCP server, terminal chat, or Daytona smoke validation from a Claude Code workflow.

Translate fleet-rlm's shared ReAct plus dspy.RLM runtime into Claude Code workflows. Use when you need a Claude-facing mental model for fleet-rlm, especially for daytona_pilot execution, running the local server surfaces, or planning long-context/runtime work.

Validate fleet-rlm with the current repo test lanes. Use when you need the right confidence level for runtime, websocket, frontend-contract, or Daytona-backed changes.

Delegate recursive work to child RLM sandboxes with budget management. Use when decomposing tasks into sub-queries, fanning out batched work, managing LLM call budgets, or building parent-child RLM hierarchies.

Diagnose fleet-rlm runtime failures, API contract drift, sandbox errors, and observability issues. Use when something is broken — Daytona connection failures, websocket mismatches, escalation not triggering, budget exhaustion, or missing traces.

Optimize fleet-rlm DSPy programs and RLM skill bundles using GEPA with MLflow tracking. Use when running optimization loops, designing feedback metrics, building training datasets, or comparing runs.

Hub skill for fleet-rlm: when to use dspy.RLM vs ReAct/CodeAct and which workflow skill to load next.

Design DSPy signatures and compose runtime modules for fleet-rlm tasks. Use when creating input/output field definitions, choosing between built-in signatures, selecting execution modes, or wiring custom modules.

Fetch and inspect JavaScript-heavy pages with Playwright in a Daytona browser-capable snapshot.

Process documents and codebases exceeding a single context window using canonical dspy.RLM variable mode in the Daytona REPL.

Execute Python in Daytona sandboxes with durable volume persistence. Use when running code, persisting results, inspecting volumes, managing workspace lifecycle, or validating Daytona connectivity.

Complete guide to the AgenticFleet memory system. Read this first.

Comprehensive guide to integrating DSPy with Microsoft Agent Framework in AgenticFleet, covering typed signatures, assertions, routing cache, GEPA optimization, and agent handoffs.

Quick reference card for DSPy + Agent Framework integration patterns: typed signatures, assertions, routing cache, and agent handoffs.

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

Comprehensive guide for initializing or reorganizing agent memory and project context. Use when setting up a new project, when the user asks you to learn about the codebase, or when you need to create effective memory blocks for project conventions, preferences, and workflows.

Expert Python backend code reviewer that identifies over-complexity, duplicates, bad optimizations, and violations of best practices. Use when asked to review Python code quality, check for duplicate code, analyze module complexity, optimize backend code, identify anti-patterns, or ensure adherence to best practices. Ideal for preventing AI-generated code from creating unnecessary files instead of imports, finding repeated validation logic, and catching over-engineered solutions.

Context-aware development assistant for AgenticFleet with auto-learning and dual memory (NeonDB + ChromaDB). Handles development workflows with intelligent context management.

Initialize or hydrate the agent's memory system and verify configuration.

Build generative UI apps with OpenUI and OpenUI Lang — the token-efficient open standard for LLM-generated interfaces. Use when mentioning OpenUI, @openuidev, generative UI, streaming UI from LLMs, component libraries for AI, or replacing json-render/A2UI. Covers scaffolding, defineComponent, system prompts, the Renderer, and debugging OpenUI Lang output.

Map the Fleet Pi codebase before planning or editing. Use when asked what the app does, where chat/session/resource code lives, or how the monorepo fits together.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

Diagnose Fleet Pi chat runtime failures, including NDJSON streams, Pi sessions, Bedrock model errors, resource loader diagnostics, queueing, aborts, and Plan mode question flow.

Build or modify Fleet Pi's React Agent Elements UI, including InputBar controls, tool cards, question prompts, resource drawer, model picker, mode selector, and streaming state.