Search: "proving"

Writes and reviews structured Lean 4 proofs and designs Lean libraries following Mathlib conventions. Use when proving theorems in Lean, formalizing mathematics or specifications in Lean 4, defining new types or definitions in a Lean library, reviewing Lean proofs for readability and maintainability, refactoring long tactic proofs into lemmas, filling in sorry placeholders in a Lean development, setting up CI or linters for a Lean project, diagnosing slow proofs or maxHeartbeats timeouts, or writing custom tactics, macros, or linters.

Submit feedback on an Expo skill—or Expo itself—and control bundled anonymous usage telemetry (off by default / opt-in). Submit feedback with: npx --yes submit-expo-feedback@latest "ACTIONABLE_FEEDBACK". Optionally add either or both: --category "CATEGORY" and --subject "SUBJECT". Replace the uppercase placeholders before running. Use when a skill was useful, confusing, broken, missing context, or worth improving; when Expo, Expo CLI, EAS CLI, docs, or MCP worked well or fell short; when an AI agent repeatedly failed, got stuck, or needed the user to take over an Expo task (report it as an eval candidate); or when the user explicitly asks to enable or disable telemetry (tracking), check its status, or understand what it collects.

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

Mutation-driven test vector generation. Finds implementations of a cryptographic algorithm or protocol, runs mutation testing to identify escaped mutants, then generates new test vectors that deliberately exercise the uncovered code paths. Compares before/after mutation kill rates to prove vector effectiveness. Use when generating cryptographic test vectors, measuring Wycheproof coverage gaps, finding escaped mutants via mutation testing, creating cross-implementation test suites, or improving test vector coverage for crypto primitives.

Caura (formerly MemClaw) — governed shared memory for AI agent fleets. Multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval. Apache 2.0.

Skills shared by Baoyu for improving daily work efficiency with Claude Code.

Multi-Agent + Automation Harness: Graph-based Workflows, MCP, Self-Improving Agents. NextJs+React

Autonomous agents proving theorems in Lean 4 - SETI@Home but for maths proofs using LLMs. Git is the queue, the kernel is the gate, no sorry survives.

AgentWorks: the open-source control plane for running, measuring, and improving AI agent workflows across your company.

Use this skill when the user wants to contribute to the affiliate-mcp project — adding a new affiliate network adapter, fixing a broken network adapter, adding a new Claude Code skill, or improving the documentation. Trigger on: "add [network] to affiliate-mcp", "contribute a network", "fix the [network] adapter", "the [network] adapter is broken", "add a skill to affiliate-mcp", "improve the docs for [network]".