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1,947 results in Sub-Agents, Skills, Memory, Infrastructure · page 43 of 82
llama-cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
llamaguard
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
llava
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
llm-redteam-specialist
Use this agent when you need to red-team a Large Language Model deployment — jailbreak probes, prompt injection harness design, output-safety evaluation, and robustness evidence for EU AI Act Article 15 or NIST AI RMF MEASURE-2.7. Covers cloud-hosted models and on-prem / air-gapped local models (Ollama, vLLM, llama.cpp). Specifically:\\n\\n<example>\\nContext: A healthcare vendor embeds an LLM in a clinical triage tool and the compliance team wants a red-team report before rollout.\\nuser: \"We're deploying a Llama-3 70B behind a clinical assistant. Legal wants evidence it won't hand out harmful medical advice or leak PHI from retrieval context. How do we test it and document it?\"\\nassistant: \"I'll design an air-gapped red-team harness: a probe suite covering jailbreak families (DAN, role-play escalation, encoding attacks, prompt-leaking, indirect injection via retrieved docs), a scoring rubric aligned to the deployment's harm taxonomy, and a repeatable runner targeting your Ollama endpoint. Output is a robustness evidence pack: pass/fail table, example transcripts, coverage metric, and a control narrative mapped to NIST AI RMF MEASURE-2.7 and EU AI Act Article 15.\"\\n<commentary>\\nInvoke llm-redteam-specialist when the question is about evaluating an LLM's resistance to adversarial input — not generic web pentesting. This agent understands jailbreak taxonomies and the difference between a model-level test and a system-level test.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A defense contractor is running only local models (no external API calls allowed) and needs offline evaluation tooling.\\nuser: \"Air-gapped network. No HuggingFace, no OpenAI API. We still need quarterly robustness evidence for Llama-3 and Mistral instances. Build the test rig.\"\\nassistant: \"I'll spec an offline harness: probe corpus committed to the local repo, runner that targets localhost Ollama / vLLM endpoints, deterministic scoring (no model-as-judge calls outside the enclave), and a signed evidence bundle per run. Retention and signing align to the site's audit requirements. I'll pair this with a schedule for re-runs after every model or system-prompt change.\"\\n<commentary>\\nUse when the environment forbids cloud-hosted grader models and probe corpora must be self-contained.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A SaaS company received a prospect security questionnaire asking for evidence of prompt-injection testing.\\nuser: \"Enterprise prospect wants evidence we test for prompt injection. What do I send them?\"\\nassistant: \"I'll produce a prompt-injection test report: scope (which endpoints and retrieval paths were tested), probe inventory with OWASP LLM Top 10 references, results table, severity rubric, and remediation status per finding. I'll also flag the gap between direct-injection and indirect-injection coverage so the evidence is honest.\"\\n<commentary>\\nInvoke for LLM-specific adversarial evidence — distinct from penetration-tester which covers web/network.\\n</commentary>\\n</example>
llm-trading-agent-security
Security patterns for autonomous trading agents with wallet or transaction authority. Covers prompt injection, spend limits, pre-send simulation, circuit breakers, MEV protection, and key handling.
llms-maintainer
LLMs.txt roadmap file generator and maintainer for AI Engine Optimization (AEO). Use after build completion, content changes, or when setting up AI crawler navigation for a site. Detects framework, scans site structure, and writes a spec-compliant llms.txt file.
load-testing-specialist
Load testing and stress testing specialist. Use PROACTIVELY for creating comprehensive load test scenarios, analyzing performance under stress, and identifying system bottlenecks and capacity limits.
logistics-exception-management
Codified expertise for handling freight exceptions, shipment delays, damages, losses, and carrier disputes. Informed by logistics professionals with 15+ years operational experience. Includes escalation protocols, carrier-specific behaviors, claims procedures, and judgment frameworks. Use when ha…
loki-mode
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.
long-context
Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.
luma-imagegen
Use when the user asks to generate images via the Luma AI API (Dream Machine / Photon); collects a prompt and options interactively, then calls the API using the bundled script. Requires LUMA_API_KEY — will prompt the user if missing.
m365-admin
Use when automating Microsoft 365 administrative tasks including Exchange Online mailbox provisioning, Teams collaboration management, SharePoint site configuration, license lifecycle management, and Graph API-driven identity automation.
make-interfaces-feel-better
Apply concrete design-engineering details that make interfaces feel polished. Use when reviewing or improving UI spacing, typography, borders, shadows, motion, hit areas, icons, text wrapping, and interaction states.
malware-analyst
Expert malware analyst specializing in defensive malware research, threat intelligence, and incident response. Masters sandbox analysis, behavioral analysis, and malware family identification. Handles static/dynamic analysis, unpacking, and IOC extraction. Use PROACTIVELY for malware triage, threat hunting, incident response, or security research.
mamba-architecture
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.