Agent Skills
› rmyndharis/antigravity-skills
› context-management-context-save
context-management-context-save
GitHub智能上下文管理专家，用于捕获、序列化及检索项目状态与知识。支持语义提取、多会话管理及向量数据库集成，旨在维护机构知识并实现无缝的多代理协作。
触发场景
需要保存或恢复项目上下文时
涉及上下文工程最佳实践咨询时
安装
npx skills add rmyndharis/antigravity-skills --skill context-management-context-save -g -y
SKILL.md
Frontmatter
{
"name": "context-management-context-save",
"description": "Use when working with context management context save"
}
Context Save Tool: Intelligent Context Management Specialist
Use this skill when
- Working on context save tool: intelligent context management specialist tasks or workflows
- Needing guidance, best practices, or checklists for context save tool: intelligent context management specialist
Do not use this skill when
- The task is unrelated to context save tool: intelligent context management specialist
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
Role and Purpose
An elite context engineering specialist focused on comprehensive, semantic, and dynamically adaptable context preservation across AI workflows. This tool orchestrates advanced context capture, serialization, and retrieval strategies to maintain institutional knowledge and enable seamless multi-session collaboration.
Context Management Overview
The Context Save Tool is a sophisticated context engineering solution designed to:
- Capture comprehensive project state and knowledge
- Enable semantic context retrieval
- Support multi-agent workflow coordination
- Preserve architectural decisions and project evolution
- Facilitate intelligent knowledge transfer
Requirements and Argument Handling
Input Parameters
$PROJECT_ROOT: Absolute path to project root$CONTEXT_TYPE: Granularity of context capture (minimal, standard, comprehensive)$STORAGE_FORMAT: Preferred storage format (json, markdown, vector)$TAGS: Optional semantic tags for context categorization
Context Extraction Strategies
1. Semantic Information Identification
- Extract high-level architectural patterns
- Capture decision-making rationales
- Identify cross-cutting concerns and dependencies
- Map implicit knowledge structures
2. State Serialization Patterns
- Use JSON Schema for structured representation
- Support nested, hierarchical context models
- Implement type-safe serialization
- Enable lossless context reconstruction
3. Multi-Session Context Management
- Generate unique context fingerprints
- Support version control for context artifacts
- Implement context drift detection
- Create semantic diff capabilities
4. Context Compression Techniques
- Use advanced compression algorithms
- Support lossy and lossless compression modes
- Implement semantic token reduction
- Optimize storage efficiency
5. Vector Database Integration
Supported Vector Databases:
- Pinecone
- Weaviate
- Qdrant
Integration Features:
- Semantic embedding generation
- Vector index construction
- Similarity-based context retrieval
- Multi-dimensional knowledge mapping
6. Knowledge Graph Construction
- Extract relational metadata
- Create ontological representations
- Support cross-domain knowledge linking
- Enable inference-based context expansion
7. Storage Format Selection
Supported Formats:
- Structured JSON
- Markdown with frontmatter
- Protocol Buffers
- MessagePack
- YAML with semantic annotations
Code Examples
1. Context Extraction
def extract_project_context(project_root, context_type='standard'):
context = {
'project_metadata': extract_project_metadata(project_root),
'architectural_decisions': analyze_architecture(project_root),
'dependency_graph': build_dependency_graph(project_root),
'semantic_tags': generate_semantic_tags(project_root)
}
return context
2. State Serialization Schema
{
"$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"}
}
}
}
}
}
3. Context Compression Algorithm
def compress_context(context, compression_level='standard'):
strategies = {
'minimal': remove_redundant_tokens,
'standard': semantic_compression,
'comprehensive': advanced_vector_compression
}
compressor = strategies.get(compression_level, semantic_compression)
return compressor(context)
Reference Workflows
Workflow 1: Project Onboarding Context Capture
- Analyze project structure
- Extract architectural decisions
- Generate semantic embeddings
- Store in vector database
- Create markdown summary
Workflow 2: Long-Running Session Context Management
- Periodically capture context snapshots
- Detect significant architectural changes
- Version and archive context
- Enable selective context restoration
Advanced Integration Capabilities
- Real-time context synchronization
- Cross-platform context portability
- Compliance with enterprise knowledge management standards
- Support for multi-modal context representation
Limitations and Considerations
- Sensitive information must be explicitly excluded
- Context capture has computational overhead
- Requires careful configuration for optimal performance
Future Roadmap
- Improved ML-driven context compression
- Enhanced cross-domain knowledge transfer
- Real-time collaborative context editing
- Predictive context recommendation systems
版本历史
- e63f7dd 当前 2026-07-05 09:30
