Context Cascade - Nested Plugin Architecture for Claude Code Official Claude Code Plugin | Version 3.1.0 | Last updated: 2026-01-09 (see docs/COMPONENT-COUNTS.json for source counts) Context-saving nested architecture: Playbooks -> Skills -> Agents -> Commands. Load only what you need, saving 90%+ context space.
Skill "when-automating-github-actions-use-workflow-automation" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), github actions workflow automation skill, kanitsal cerceve (evidential frame activation), when to use this skill and agent coordination architecture.
Skill "when-bridging-web-cli-use-web-cli-teleport" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), orchestration skill guidelines, when to use this skill, when not to use this skill and success criteria.
Skill "when-chaining-agent-pipelines-use-stream-chain" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), orchestration skill guidelines, when to use this skill, when not to use this skill and success criteria.
Skill "when-creating-slash-commands-use-slash-command-encoder" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), orchestration skill guidelines, when to use this skill, when not to use this skill and success criteria.
Skill "when-orchestrating-swarm-use-swarm-orchestration" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), orchestration skill guidelines, when to use this skill, when not to use this skill and success criteria.
Skill "when-using-advanced-swarm-use-swarm-advanced" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), orchestration skill guidelines, when to use this skill, when not to use this skill and success criteria.
Extract learnings from session corrections and patterns, update skill files with persistent memory. Implements Loop 1.5 - per-session micro-learning between execution and meta-optimization.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Skill "agentdb-advanced" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), when not to use this skill, success criteria, edge cases & error handling and guardrails & safety.
Skill "agentdb-learning" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), when not to use this skill, success criteria, edge cases & error handling and guardrails & safety.
Skill "agentdb-memory-patterns" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), when not to use this skill, success criteria, edge cases & error handling and guardrails & safety.
Skill "agentdb-optimization" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), when not to use this skill, success criteria, edge cases & error handling and guardrails & safety.
Apply quantization to reduce memory by 4-32x. Enable HNSW indexing for 150x faster search. Configure caching strategies and implement batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors. Deploy these optimizations to achieve 12,500x performance gains.
AgentDB Reinforcement Learning Training operates on 3 fundamental principles.
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