legal-risk-assessment
Assess and classify legal risks using a severity-by-likelihood framework. Evaluate contract risk, deal exposure, and issue severity to determine if senior counsel or external review is required.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
227 skills found
Assess and classify legal risks using a severity-by-likelihood framework. Evaluate contract risk, deal exposure, and issue severity to determine if senior counsel or external review is required.
Retrieve current, source-backed technical information using MCP tools to resolve queries about libraries, APIs, SDKs, and evolving tech ecosystems.
Bayesian modeling and probabilistic programming with PyMC. Build hierarchical models, perform MCMC sampling (NUTS), variational inference, and conduct rigorous model comparison using LOO and WAIC.
Monitor US-Iran strike probability via real-time open-source signals including market odds, flight traffic, energy prices, and geopolitical alerts.
Holistic, multi-dimensional code review skill providing prioritized, actionable feedback on correctness, security, performance, design, and accessibility.
Diagnose, isolate, and mitigate LLM context failures like lost-in-middle, poisoning, distraction, and context clash to improve agent reliability.
Enforce epistemic quality in RAG systems with pre-ingestion verification. Ensures documents are properly qualified and structured before knowledge base entry.
Performs a structured five-stage code review covering requirements, correctness, code quality, testing, and security. Provides actionable, categorized feedback (Blocker/Major/Minor/Nit) to improve PR quality.
Verifies blockchain smart contract code against technical specifications, whitepapers, and design documents to ensure exact implementation compliance.
A framework for building modular, reusable agent skills. Provides guidelines for structuring SKILL.md, bundled scripts, references, and assets to extend Claude's capabilities.
Perform automated security audits, bug detection, and code quality assessments on local branch diffs using a structured, checklist-driven verification process.
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.