Llm Context

Wire Alchemy into application code without an API key, using the x402 or MPP gateway with wallet-based auth (SIWE/SIWS) and per-request payments (USDC via x402, or USDC/credit-card via MPP). Specialized app-integration path. Use when the user is building application code AND no API key is available, or they're an autonomous agent that needs to pay for itself, or they explicitly want x402/MPP. For normal app code with an API key, use `alchemy-api`. For live agent work in this session (querying, admin, automation), use `alchemy-cli` (preferred) or `alchemy-mcp`.

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.

Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.

Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).

Comprehensive guide for maintaining, debugging, and upgrading the memory-lancedb-pro OpenClaw plugin — an enhanced LanceDB-backed long-term memory system with hybrid retrieval (Vector + BM25), cross-encoder reranking, multi-scope isolation, noise filtering, adaptive retrieval, and a management CLI. Use this skill when: (1) developing new features or fixing bugs in memory-lancedb-pro, (2) modifying the retrieval pipeline (vector search, BM25, RRF fusion, reranking, scoring stages), (3) adding or changing embedding providers, (4) updating scope/access control logic, (5) modifying agent tools or CLI commands, (6) troubleshooting memory quality issues (noise, duplicates, low recall), (7) working on the JSONL session distillation pipeline, (8) migrating data between memory backends, or (9) understanding the plugin's architecture to plan enhancements.

Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.