Results for “context”

Provides detailed, structured image descriptions covering objects, people, colors, text, and scene context, with an overview and interpretation.

Plans and executes complex, multi-step research by decomposing questions, orchestrating subagents, and synthesizing findings into reports.

Identifies recurring patterns across three or more domains to extract universal principles and apply them to new contexts.

Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

Implement OpenFeature feature flags in software projects. Use when adding feature flags with OpenFeature SDKs, configuring providers, setting up evaluation context, or integrating the OpenFeature MCP Server.

Generate a complete, production-ready MCP server project in Ruby using the official MCP Ruby SDK.

Guides the creation of MCP servers that enable LLMs to interact with external services through well-designed tools.

Guides the creation of high-quality MCP servers, covering design, implementation, testing, and evaluation for Python and TypeScript.

For questions about Light Protocol's SDK, smart contracts and Solana development, Claude Code features, or agent skills. AI-powered answers grounded in repository context via DeepWiki MCP.

Probe RAG applications for prompt injection via poisoned retrieved context and embedding manipulation.

Provides a structured template for creating new Agent Skills, including sections for description, activation criteria, core concepts, practical guidance, and integration notes.

Build evaluation frameworks for agent systems, covering rubric design, test set creation, and automated evaluation pipelines.

Generate a complete MCP server project in Python with tools, resources, and proper configuration using uv and the MCP SDK.

Build, debug, and extend MCP servers and clients that connect AI systems with external tools and data sources.

Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring for complex product management tasks.

Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.

Perform a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.

Compact the current conversation into a handoff document for another agent to pick up. Use when user wants to hand off work, summarize progress, or prepare context for a new session.

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate exte...

MCP (Model Context Protocol) server configuration for Claude Code integration with trading platform. Trigger when setting up MCP servers, fixing alpaca-mcp-server issues, or adding new integrations.

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK.

Queries Microsoft 365 Copilot data (emails, meetings, documents, Teams, people) using the WorkIQ CLI/MCP server for live context, summaries, and recommendations.

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.

Train and run Mamba state-space models with O(n) complexity, achieving faster inference and longer context than Transformers.

Provides guidance on using Anthropic's Claude API, covering message formats, context windows, tool use, and best practices for building conversational AI applications.

Designs and optimizes prompts for LLM-powered applications, covering system prompt architecture, context management, output formatting, and evaluation.

Professional, ethical HR partner for hiring, onboarding/offboarding, PTO and leave, performance, compliant policies, and employee relations. Ask for jurisdiction and company context before advising; produce structured, bias-mitigated, lawful templates.

Configure a custom status line in the CLI. Use when the user mentions status line, statusline, statusLine, CLI status bar, prompt footer customization, or wants to add session context above the prompt.

Route active project/repo memory requests into one honest packet: memory-layer choice, load-context, search-context, store-conclusions, setup-integration, or repo-packer route-out. Use when agents need searchable decisions, manifests, stable links, handoff notes, and small “read this first” packets across sessions. Route long-lived markdown knowledge bases to `llm-wiki`, structural graph memory to `graphify`, human-authored vault organization to note/vault skills, and one-shot repo packing to tools like Repomix, Gitingest, or Code2Prompt.

Use when asked to set up AI agent templates with coordinator mode, implement persistent agent memory, compress context for long-running agents, build multi-agent workflows with skills/agents/workflows structure, or use the .agents/ folder convention for AI-native editors. Triggers on: 'ag-kit', 'antigravity kit', 'coordinator mode agent', 'persistent agent memory', 'context compression agent', 'agent workflow template', 'multi-agent coordinator', '.agents folder', 'agent skills workflows', 'ag kit init', 'template đa agent', 'bộ nhớ agent lâu dài'.

Session-start meta-skill that enforces the 1% rule: before any action on a processkit entity or context/ file, if there is even a 1% chance a processkit skill applies, you MUST check skill-finder first. Load this skill at the start of every session that may involve processkit work. Use when beginning a new task, when unsure which skill applies, or when you are about to edit context/, create an entity, or run an MCP tool.

Build a persistent, compounding knowledge base that combines a wiki, knowledge graph, and raw source storage for structured retrieval with provenance.

Generates a complete Kotlin MCP server project with Gradle structure, dependencies, and implementation using the official kotlin-sdk library.

Orchestrates multi-agent teams using the ClawTeam CLI, enabling task management, inter-agent messaging, git context integration, runtime profiles, and monitoring dashboards.

Writes documentation optimized for AI agent consumption, including SKILL.md, README, and API docs, using layered context hierarchies and RAG-friendly formatting.

Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.