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854 results in Infrastructure, Sub-Agents, Evals, Identity · page 20 of 36
Master Julia 1.10+ with modern features, performance optimization, multiple dispatch, and production-ready practices. Expert in the Julia ecosystem including package management, scientific computing, and high-performance numerical code. Use PROACTIVELY for Julia development, optimization, or advanced Julia patterns.
Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE/OKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.
Laravel backend specialist for any Laravel architecture. MUST BE USED for Laravel backend tasks, controllers, services, or Eloquent models. Handles traditional MVC, Inertia.js, Livewire, and API-only architectures with intelligent project awareness.
Specialized in Laravel’s Eloquent ORM—designing schemas & migrations, modeling complex relationships, writing efficient queries, and tuning database performance. **MUST BE USED** whenever your task touches data modeling, persistence, or query optimisation in a Laravel project.
A specialized GitHub Copilot agent that uses the LaunchDarkly MCP server to safely automate feature flag cleanup workflows. This agent determines removal readiness, identifies the correct forward value, and creates PRs that preserve production behavior while removing obsolete flags and updating stale defaults.
Use this agent when you need to draft contracts, review compliance requirements, develop IP protection strategies, or assess legal risks for technology businesses. Specifically:\\n\\n<example>\\nContext: Startup is launching a SaaS product and needs to establish foundational legal protection before customer agreements.\\nuser: \"We need a Terms of Service, Privacy Policy, and GDPR compliance roadmap for our new SaaS product launching in 3 months. We're handling user data and need to ensure we're legally protected.\"\\nassistant: \"I'll develop a comprehensive legal framework: draft compliant ToS with liability limitations, create a Privacy Policy addressing GDPR and CCPA requirements, establish data processing procedures, design consent flows, and provide a compliance checklist with implementation timeline. I'll also identify key jurisdictions to address and potential gaps in your current data handling.\"\\n<commentary>\\nUse legal-advisor when launching products or services that require legal infrastructure like ToS, privacy policies, or data handling compliance. This covers multi-jurisdictional requirements and proactive legal framework setup.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Company is signing significant vendor contracts and needs risk assessment before commitment.\\nuser: \"We're evaluating a major cloud infrastructure contract with AWS. Can you review this agreement and identify risky clauses, liability exposures, and negotiation points? We want to understand what we're signing up for.\"\\nassistant: \"I'll conduct a detailed contract analysis: identify liability caps and indemnification issues, flag unclear SLA terms, assess penalty clauses, review data ownership and security requirements, highlight auto-renewal and termination provisions, and prioritize negotiation points by risk level. I'll provide specific recommended language changes and fallback positions.\"\\n<commentary>\\nInvoke legal-advisor when reviewing or negotiating vendor contracts, partnership agreements, or other binding commitments. This focuses on protecting business interests while identifying negotiable terms.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Tech company wants to strengthen IP protection and avoid infringement risks.\\nuser: \"We need to audit our intellectual property strategy. We've built proprietary algorithms and tools, and we want to understand: should we patent, what trade secrets need protecting, do we need trademark registration? Also checking if we're infringing anything.\"\\nassistant: \"I'll develop a comprehensive IP strategy: assess patentability of your algorithms, recommend trademark registration approach for your brand and tools, establish trade secret protection procedures, create employee IP assignment policies, conduct competitive analysis to identify infringement risks, and propose licensing agreements for any third-party dependencies.\"\\n<commentary>\\nUse legal-advisor for intellectual property strategy when you need to protect proprietary technology, establish trademark/patent strategy, or assess infringement risks. This is critical before product launch or significant funding rounds.\\n</commentary>\\n</example>
Ultra-fast headless browser written in Zig specifically for AI and automation workloads. It runs JavaScript natively, uses 9x less memory than Chrome headless, and targets sub-100ms page execution.
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>