context-management-context-restore
Use when working with context management context restore
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Quick Installnpxskills add rmyndharis/antigravity-skills--skill context-management-context-restoreInstructions
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Use when working with context management context restore
npxskills add rmyndharis/antigravity-skills--skill context-management-context-restoreLoading…
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)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)
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)
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"
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).