Manage Project Context and Knowledge

Context Save Tool is a skill for comprehensive, semantic, and adaptable context preservation across AI workflows - capturing project state and architectural decisions so they can be restored later or shared across a multi-agent workflow. It takes a project root path, a context-capture granularity (minimal, standard, comprehensive), a storage format (JSON, Markdown, or vector), and optional semantic tags.

Extraction identifies architectural patterns, decision rationales, cross-cutting concerns, and implicit knowledge structures, then serializes them into a typed, hierarchical schema designed for lossless reconstruction - including a context_fingerprint for identity, versioning support, drift detection between captures, and semantic diffing. Compression can be lossy or lossless depending on the chosen level (minimal removes redundant tokens, standard applies semantic compression, comprehensive applies vector-based compression). It integrates with vector databases (Pinecone, Weaviate, Qdrant) for embedding generation, index construction, and similarity-based retrieval, and can construct a knowledge graph from relational metadata for cross-domain linking and inference-based context expansion. Supported storage formats span structured JSON, Markdown with frontmatter, Protocol Buffers, MessagePack, and YAML with semantic annotations.

Two reference workflows: project onboarding (analyze structure, extract architectural decisions, generate embeddings, store in a vector database, produce a Markdown summary) and long-running session management (periodically snapshot context, detect significant architectural changes, version and archive, enable selective restoration later).

Use it when you need to capture and preserve project knowledge - architecture, decisions, dependencies - for later restoration or cross-agent sharing, rather than relying on an AI session's own working memory. Its own stated limitations matter: sensitive information must be explicitly excluded before capture, context capture carries real computational overhead, and it needs careful configuration to perform well - it is not a drop-in, zero-config tool.

Input is a project root, a context-type granularity, a storage format, and optional tags. Output is a serialized context artifact - JSON, Markdown, or a vector-database entry - following a versioned schema. A representative serialization schema looks like this:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "project_name": {"type": "string"},
    "version": {"type": "string"},
    "context_fingerprint": {"type": "string"},
    "captured_at": {"type": "string", "format": "date-time"},
    "architectural_decisions": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "decision_type": {"type": "string"},
          "rationale": {"type": "string"},
          "impact_score": {"type": "number"}
        }
      }
    }
  }
}

It integrates with vector databases (Pinecone, Weaviate, Qdrant) for semantic embedding and retrieval, and supports multiple serialization formats (JSON, Markdown with frontmatter, Protocol Buffers, MessagePack, YAML) for portability across tools.

Teams running multi-session or multi-agent AI workflows who need project context and architectural decisions captured, versioned, and semantically retrievable rather than lost between sessions - paired with a companion restoration skill for reading it back.