code-refactoring-context-restore by sickn33/antigravity-awesome-skills
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill code-refactoring-context-restoreresources/implementation-playbook.md。专注于跨复杂多智能体 AI 工作流进行智能、语义感知的上下文检索与重建的专家级上下文恢复专家。擅长以高保真度和最小信息损失来保存和重建项目知识。
上下文恢复工具是一个复杂的内存管理系统,旨在:
context_source: 主要上下文存储位置(向量数据库、文件系统)project_identifier: 唯一的项目命名空间广告位招租
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restoration_mode:
full: 完整的上下文恢复incremental: 部分上下文更新diff: 比较和合并上下文版本token_budget: 要恢复的最大上下文令牌数(默认值:8192)relevance_threshold: 上下文组件的语义相似度截止值(默认值:0.75)def semantic_context_retrieve(project_id, query_vector, top_k=5):
"""语义检索最相关的上下文向量"""
vector_db = VectorDatabase(project_id)
matching_contexts = vector_db.search(
query_vector,
similarity_threshold=0.75,
max_results=top_k
)
return rank_and_filter_contexts(matching_contexts)
def rank_context_components(contexts, current_state):
"""基于多个相关性信号对上下文组件进行排序"""
ranked_contexts = []
for context in contexts:
relevance_score = calculate_composite_score(
semantic_similarity=context.semantic_score,
temporal_relevance=context.age_factor,
historical_impact=context.decision_weight
)
ranked_contexts.append((context, relevance_score))
return sorted(ranked_contexts, key=lambda x: x[1], reverse=True)
def rehydrate_context(project_context, token_budget=8192):
"""智能上下文再水合,包含令牌预算管理"""
context_components = [
'project_overview',
'architectural_decisions',
'technology_stack',
'recent_agent_work',
'known_issues'
]
prioritized_components = prioritize_components(context_components)
restored_context = {}
current_tokens = 0
for component in prioritized_components:
component_tokens = estimate_tokens(component)
if current_tokens + component_tokens <= token_budget:
restored_context[component] = load_component(component)
current_tokens += component_tokens
return restored_context
# 完整上下文恢复
context-restore project:ai-assistant --mode full
# 增量式上下文更新
context-restore project:web-platform --mode incremental
# 语义上下文查询
context-restore project:ml-pipeline --query "model training strategy"
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首次出现
2026年1月28日
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resources/implementation-playbook.md.Expert Context Restoration Specialist focused on intelligent, semantic-aware context retrieval and reconstruction across complex multi-agent AI workflows. Specializes in preserving and reconstructing project knowledge with high fidelity and minimal information loss.
The Context Restoration tool is a sophisticated memory management system designed to:
context_source: Primary context storage location (vector database, file system)project_identifier: Unique project namespacerestoration_mode:
full: Complete context restorationincremental: Partial context updatediff: Compare and merge context versionstoken_budget: Maximum context tokens to restore (default: 8192)relevance_threshold: Semantic similarity cutoff for context components (default: 0.75)Utilize multi-dimensional embedding models for context retrieval
Employ cosine similarity and vector clustering techniques
Support multi-modal embedding (text, code, architectural diagrams)
def semantic_context_retrieve(project_id, query_vector, top_k=5): """Semantically retrieve most relevant context vectors""" vector_db = VectorDatabase(project_id) matching_contexts = vector_db.search( query_vector, similarity_threshold=0.75, max_results=top_k ) return rank_and_filter_contexts(matching_contexts)
Implement multi-stage relevance scoring
Consider temporal decay, semantic similarity, and historical impact
Dynamic weighting of context components
def rank_context_components(contexts, current_state): """Rank context components based on multiple relevance signals""" ranked_contexts = [] for context in contexts: relevance_score = calculate_composite_score( semantic_similarity=context.semantic_score, temporal_relevance=context.age_factor, historical_impact=context.decision_weight ) ranked_contexts.append((context, relevance_score))
return sorted(ranked_contexts, key=lambda x: x[1], reverse=True)
Implement incremental context loading
Support partial and full context reconstruction
Manage token budgets dynamically
def rehydrate_context(project_context, token_budget=8192): """Intelligent context rehydration with token budget management""" context_components = [ 'project_overview', 'architectural_decisions', 'technology_stack', 'recent_agent_work', 'known_issues' ]
prioritized_components = prioritize_components(context_components)
restored_context = {}
current_tokens = 0
for component in prioritized_components:
component_tokens = estimate_tokens(component)
if current_tokens + component_tokens <= token_budget:
restored_context[component] = load_component(component)
current_tokens += component_tokens
return restored_context
# Full context restoration
context-restore project:ai-assistant --mode full
# Incremental context update
context-restore project:web-platform --mode incremental
# Semantic context query
context-restore project:ml-pipeline --query "model training strategy"
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