Results for “multi-chain”

Buy and sell NFTs on OpenSea's Seaport marketplace. Fulfill listings, accept offers, create new orders, cross-chain purchases, and sweep multiple listings.

Execute NFT trades on OpenSea's Seaport marketplace: fulfill listings, accept offers, create orders, and sweep multiple listings with cross-chain support.

Routes research requests to appropriate modes and runs multi-agent deep research with evidence chains, plus optional monitoring.

Benchmarks zero-shot in-context learning on BIG-Bench Hard multiple-choice tasks, comparing self-generated demonstrations against direct prompting and chain-of-thought baselines, and reports accuracy.

Configure and manage GitHub Dependabot for automated dependency updates, security alerts, and vulnerability scanning across multiple ecosystems.

Score and rank options contracts for any ticker using a multi-factor model covering liquidity, IV attractiveness, Greeks balance, and risk/reward. Returns scored option chains with the best contracts highlighted.

Store and retrieve API keys, passwords, and credentials in an HSM-backed encrypted vault via MCP or REST API, with policy-based access control, secret rotation, multi-chain signing, and prompt injection scanning.

Build LLM-powered applications with agents, chains, and RAG using a framework that supports multiple providers and 500+ integrations.

Build, run, and visualize multi-step AI generation workflows by chaining image, video, and audio nodes into automated pipelines.

Orchestrating specialist AI agent teams as a meta-coordinator: decomposes requests into minimum viable chains, spawns each as an independent session, drives to final output. For multi-domain tasks.

Local CLI issue tracker for todos, follow-ups, structured records, and the canonical pattern for decomposing multi-step work into a parent + subissues with acceptance criteria, dependency edges, and priority. Use when the user mentions a task to remember, a bug to track, an open question, anything that needs to outlive the current turn, or when planning multi-heartbeat work that needs `chainlink issue ready`-driven pickup across sessions. Includes guidance on writing descriptions future-you can act on (handles, success path, failure path) and idempotency tactics for actions that might fire twice (boundary firing, overlapping heartbeats, retried subagent completion). Pairs with five-whys (chainlink is the storage backend for RCA trees). Storage is local — issues live under a `.chainlink/` directory in the operator's repo.

Audits a codebase against a multi-layer design system, checking token, primitive, component, and layout drift with automated grep checks and severity ratings.

Routes AI model requests to the optimal provider based on task, cost, latency, and capability requirements, managing multi-provider LLM deployments.

Deploy ERC20 tokens on Base, Ethereum, Arbitrum, and other EVM chains using the Clanker SDK. Use when the user wants to deploy a new token, create a memecoin, set up token vesting, configure airdrops, manage token rewards, claim LP fees, or update token metadata. Supports V4 deployment with vaults, airdrops, dev buys, custom market caps, vanity addresses, and multi-chain deployment.

Convert single-node PyTorch distributed scripts into multi-node Slurm sbatch jobs and debug common multi-node failures, covering srun-native and torch.distributed approaches, container setup, NCCL timeouts, and interactive allocation.

Build collaborative forensic incident timelines using Timesketch to ingest, normalize, and analyze multi-source event data for attack chain reconstruction and investigation documentation.

Orchestrates multi-stage attack-path planning and execution across reconnaissance, initial access, privilege escalation, lateral movement, and impact assessment for authorized penetration testing.

Build LLM-powered applications with modular components for chains, agents, memory, and retrieval, supporting Python and JavaScript frameworks.

Multi-project signal isolation with cascading recipe resolution

Swap or bridge tokens across 54+ blockchains via the Symbiosis protocol, with automatic routing and support for any-to-any token swaps.

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.

Cross-chain token swaps across 54+ blockchains via Symbiosis protocol. Use when the user wants to swap or bridge tokens between any chains — Base, Ethereum, Polygon, Arbitrum, Optimism, BNB Chain, Avalanche, Solana, Bitcoin, TON, Tron, and 40+ more. Supports any-to-any token swaps with automatic routing. Uses Bankr Submit API to execute transactions.

Composite skill — safely refactor a module end-to-end with sequencing, parallel implementation, post-refactor cleanup, and rationale capture. Chains refactor-plan (phased plan + rollback) → three-man-team (architect/builder/reviewer in parallel) → fix-the-suite post-refactor → adr-write → docs-sync. Use for non-trivial refactors that need both careful sequencing and durable record.

Byzantine fault-tolerant consensus and distributed coordination. Queen-led hierarchical swarm management with multiple consensus strategies. Use when: distributed coordination, fault-tolerant operations, multi-agent consensus, collective decision making. Skip when: single-agent tasks, simple operations, local-only work.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. Covers supervisor, swarm, and hierarchical multi-agent patterns; subgraph composition; state management (checkpointers/stores); persistence; evals; and production debugging. Reach for this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns.

Implements Manifold-Constrained Hyper-Connections (mHC) using Doubly Stochastic Matrices to improve deep learning stability.

Three-phase vulnerability chain analysis — parallel agents find individual weaknesses, then a synthesis step identifies which combinations escalate to critical impact. Inspired by Strix (usestrix/strix) "Graph of Agents" pentesting model.

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".