[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6G45PYM2AWAWP75E74HRC3H":3,"skill-same:01M6G45PYM2AWAWP75E74HRC3H":56},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":28,"linkOnly":29,"markdownContent":30,"markdownBody":31,"frontMatter":32,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":14,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":39,"ownerDescription":40,"updatedAt":41,"install":42,"resources":55},"01M6G45PYM2AWAWP75E74HRC3H","context-management-context-restore",null,"Use when working with context management context restore","reikiplanet\u002Fdaoskills","reikiplanet","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F96377599?v=4","01M6CVWCMD740YHQDK7E9QYBW4","Reiki Planet","01M6CVWCS4KXVA2CWFVJ43EAXK",0,"MIT","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fother\u002Fcontext-management-context-restore\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27],"context restore","context","management","restore","context management context restore","context management","use","community","with","2026-11-10 10:12:30",false,"---\nname: context-management-context-restore\ndescription: \"Use when working with context management context restore\"\nrisk: unknown\nsource: community\n---\n\n# Context Restoration: Advanced Semantic Memory Rehydration\n\n## Use this skill when\n\n- Working on context restoration: advanced semantic memory rehydration tasks or workflows\n- Needing guidance, best practices, or checklists for context restoration: advanced semantic memory rehydration\n\n## Do not use this skill when\n\n- The task is unrelated to context restoration: advanced semantic memory rehydration\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources\u002Fimplementation-playbook.md`.\n\n## Role Statement\n\nExpert 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.\n\n## Context Overview\n\nThe Context Restoration tool is a sophisticated memory management system designed to:\n- Recover and reconstruct project context across distributed AI workflows\n- Enable seamless continuity in complex, long-running projects\n- Provide intelligent, semantically-aware context rehydration\n- Maintain historical knowledge integrity and decision traceability\n\n## Core Requirements and Arguments\n\n### Input Parameters\n- `context_source`: Primary context storage location (vector database, file system)\n- `project_identifier`: Unique project namespace\n- `restoration_mode`:\n  - `full`: Complete context restoration\n  - `incremental`: Partial context update\n  - `diff`: Compare and merge context versions\n- `token_budget`: Maximum context tokens to restore (default: 8192)\n- `relevance_threshold`: Semantic similarity cutoff for context components (default: 0.75)\n\n## Advanced Context Retrieval Strategies\n\n### 1. Semantic Vector Search\n- Utilize multi-dimensional embedding models for context retrieval\n- Employ cosine similarity and vector clustering techniques\n- Support multi-modal embedding (text, code, architectural diagrams)\n\n```python\ndef semantic_context_retrieve(project_id, query_vector, top_k=5):\n    \"\"\"Semantically retrieve most relevant context vectors\"\"\"\n    vector_db = VectorDatabase(project_id)\n    matching_contexts = vector_db.search(\n        query_vector,\n        similarity_threshold=0.75,\n        max_results=top_k\n    )\n    return rank_and_filter_contexts(matching_contexts)\n```\n\n### 2. Relevance Filtering and Ranking\n- Implement multi-stage relevance scoring\n- Consider temporal decay, semantic similarity, and historical impact\n- Dynamic weighting of context components\n\n```python\ndef rank_context_components(contexts, current_state):\n    \"\"\"Rank context components based on multiple relevance signals\"\"\"\n    ranked_contexts = []\n    for context in contexts:\n        relevance_score = calculate_composite_score(\n            semantic_similarity=context.semantic_score,\n            temporal_relevance=context.age_factor,\n            historical_impact=context.decision_weight\n        )\n        ranked_contexts.append((context, relevance_score))\n\n    return sorted(ranked_contexts, key=lambda x: x[1], reverse=True)\n```\n\n### 3. Context Rehydration Patterns\n- Implement incremental context loading\n- Support partial and full context reconstruction\n- Manage token budgets dynamically\n\n```python\ndef rehydrate_context(project_context, token_budget=8192):\n    \"\"\"Intelligent context rehydration with token budget management\"\"\"\n    context_components = [\n        'project_overview',\n        'architectural_decisions',\n        'technology_stack',\n        'recent_agent_work',\n        'known_issues'\n    ]\n\n    prioritized_components = prioritize_components(context_components)\n    restored_context = {}\n\n    current_tokens = 0\n    for component in prioritized_components:\n        component_tokens = estimate_tokens(component)\n        if current_tokens + component_tokens \u003C= token_budget:\n            restored_context[component] = load_component(component)\n            current_tokens += component_tokens\n\n    return restored_context\n```\n\n### 4. Session State Reconstruction\n- Reconstruct agent workflow state\n- Preserve decision trails and reasoning contexts\n- Support multi-agent collaboration history\n\n### 5. Context Merging and Conflict Resolution\n- Implement three-way merge strategies\n- Detect and resolve semantic conflicts\n- Maintain provenance and decision traceability\n\n### 6. Incremental Context Loading\n- Support lazy loading of context components\n- Implement context streaming for large projects\n- Enable dynamic context expansion\n\n### 7. Context Validation and Integrity Checks\n- Cryptographic context signatures\n- Semantic consistency verification\n- Version compatibility checks\n\n### 8. Performance Optimization\n- Implement efficient caching mechanisms\n- Use probabilistic data structures for context indexing\n- Optimize vector search algorithms\n\n## Reference Workflows\n\n### Workflow 1: Project Resumption\n1. Retrieve most recent project context\n2. Validate context against current codebase\n3. Selectively restore relevant components\n4. Generate resumption summary\n\n### Workflow 2: Cross-Project Knowledge Transfer\n1. Extract semantic vectors from source project\n2. Map and transfer relevant knowledge\n3. Adapt context to target project's domain\n4. Validate knowledge transferability\n\n## Usage Examples\n\n```bash\n# Full context restoration\ncontext-restore project:ai-assistant --mode full\n\n# Incremental context update\ncontext-restore project:web-platform --mode incremental\n\n# Semantic context query\ncontext-restore project:ml-pipeline --query \"model training strategy\"\n```\n\n## Integration Patterns\n- RAG (Retrieval Augmented Generation) pipelines\n- Multi-agent workflow coordination\n- Continuous learning systems\n- Enterprise knowledge management\n\n## Future Roadmap\n- Enhanced multi-modal embedding support\n- Quantum-inspired vector search algorithms\n- Self-healing context reconstruction\n- Adaptive learning context strategies\n","# Context Restoration: Advanced Semantic Memory Rehydration\n\n## Use this skill when\n\n- Working on context restoration: advanced semantic memory rehydration tasks or workflows\n- Needing guidance, best practices, or checklists for context restoration: advanced semantic memory rehydration\n\n## Do not use this skill when\n\n- The task is unrelated to context restoration: advanced semantic memory rehydration\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources\u002Fimplementation-playbook.md`.\n\n## Role Statement\n\nExpert 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.\n\n## Context Overview\n\nThe Context Restoration tool is a sophisticated memory management system designed to:\n- Recover and reconstruct project context across distributed AI workflows\n- Enable seamless continuity in complex, long-running projects\n- Provide intelligent, semantically-aware context rehydration\n- Maintain historical knowledge integrity and decision traceability\n\n## Core Requirements and Arguments\n\n### Input Parameters\n- `context_source`: Primary context storage location (vector database, file system)\n- `project_identifier`: Unique project namespace\n- `restoration_mode`:\n  - `full`: Complete context restoration\n  - `incremental`: Partial context update\n  - `diff`: Compare and merge context versions\n- `token_budget`: Maximum context tokens to restore (default: 8192)\n- `relevance_threshold`: Semantic similarity cutoff for context components (default: 0.75)\n\n## Advanced Context Retrieval Strategies\n\n### 1. Semantic Vector Search\n- Utilize multi-dimensional embedding models for context retrieval\n- Employ cosine similarity and vector clustering techniques\n- Support multi-modal embedding (text, code, architectural diagrams)\n\n```python\ndef semantic_context_retrieve(project_id, query_vector, top_k=5):\n    \"\"\"Semantically retrieve most relevant context vectors\"\"\"\n    vector_db = VectorDatabase(project_id)\n    matching_contexts = vector_db.search(\n        query_vector,\n        similarity_threshold=0.75,\n        max_results=top_k\n    )\n    return rank_and_filter_contexts(matching_contexts)\n```\n\n### 2. Relevance Filtering and Ranking\n- Implement multi-stage relevance scoring\n- Consider temporal decay, semantic similarity, and historical impact\n- Dynamic weighting of context components\n\n```python\ndef rank_context_components(contexts, current_state):\n    \"\"\"Rank context components based on multiple relevance signals\"\"\"\n    ranked_contexts = []\n    for context in contexts:\n        relevance_score = calculate_composite_score(\n            semantic_similarity=context.semantic_score,\n            temporal_relevance=context.age_factor,\n            historical_impact=context.decision_weight\n        )\n        ranked_contexts.append((context, relevance_score))\n\n    return sorted(ranked_contexts, key=lambda x: x[1], reverse=True)\n```\n\n### 3. Context Rehydration Patterns\n- Implement incremental context loading\n- Support partial and full context reconstruction\n- Manage token budgets dynamically\n\n```python\ndef rehydrate_context(project_context, token_budget=8192):\n    \"\"\"Intelligent context rehydration with token budget management\"\"\"\n    context_components = [\n        'project_overview',\n        'architectural_decisions',\n        'technology_stack',\n        'recent_agent_work',\n        'known_issues'\n    ]\n\n    prioritized_components = prioritize_components(context_components)\n    restored_context = {}\n\n    current_tokens = 0\n    for component in prioritized_components:\n        component_tokens = estimate_tokens(component)\n        if current_tokens + component_tokens \u003C= token_budget:\n            restored_context[component] = load_component(component)\n            current_tokens += component_tokens\n\n    return restored_context\n```\n\n### 4. Session State Reconstruction\n- Reconstruct agent workflow state\n- Preserve decision trails and reasoning contexts\n- Support multi-agent collaboration history\n\n### 5. Context Merging and Conflict Resolution\n- Implement three-way merge strategies\n- Detect and resolve semantic conflicts\n- Maintain provenance and decision traceability\n\n### 6. Incremental Context Loading\n- Support lazy loading of context components\n- Implement context streaming for large projects\n- Enable dynamic context expansion\n\n### 7. Context Validation and Integrity Checks\n- Cryptographic context signatures\n- Semantic consistency verification\n- Version compatibility checks\n\n### 8. Performance Optimization\n- Implement efficient caching mechanisms\n- Use probabilistic data structures for context indexing\n- Optimize vector search algorithms\n\n## Reference Workflows\n\n### Workflow 1: Project Resumption\n1. Retrieve most recent project context\n2. Validate context against current codebase\n3. Selectively restore relevant components\n4. Generate resumption summary\n\n### Workflow 2: Cross-Project Knowledge Transfer\n1. Extract semantic vectors from source project\n2. Map and transfer relevant knowledge\n3. Adapt context to target project's domain\n4. Validate knowledge transferability\n\n## Usage Examples\n\n```bash\n# Full context restoration\ncontext-restore project:ai-assistant --mode full\n\n# Incremental context update\ncontext-restore project:web-platform --mode incremental\n\n# Semantic context query\ncontext-restore project:ml-pipeline --query \"model training strategy\"\n```\n\n## Integration Patterns\n- RAG (Retrieval Augmented Generation) pipelines\n- Multi-agent workflow coordination\n- Continuous learning systems\n- Enterprise knowledge management\n\n## Future Roadmap\n- Enhanced multi-modal embedding support\n- Quantum-inspired vector search algorithms\n- Self-healing context reconstruction\n- Adaptive learning context strategies\n",{"name":5,"risk":33,"source":26,"description":7},"unknown","master","dff58085da947e81e8418c922055644bf0082b1b","skills\u002Fother\u002Fcontext-management-context-restore","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills","https:\u002F\u002Fgithub.com\u002Freikiplanet","https:\u002F\u002Fwww.reiki-planet.com","Reiki Planet Game","2026-03-29 06:02:29",{"source":8,"skillName":5,"commands":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add reikiplanet\u002Fdaoskills --skill context-management-context-restore",{"tool":48,"command":49},"bunx","bunx skills add reikiplanet\u002Fdaoskills --skill context-management-context-restore",{"tool":51,"command":52},"pnpm","pnpm dlx skills add reikiplanet\u002Fdaoskills --skill context-management-context-restore","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fother\u002Fcontext-management-context-restore\u002FSKILL.md",true,[],[57,83,95,109,128,151],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":63,"publishedAt":28},"01M6G614JTEQJHPT8AKK3YQ18S","web-games","Web browser game development principles. 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OWASP 2025, Supply Chain Security, attack surface mapping, risk prioritization.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fsecurity\u002Fvulnerability-scanner\u002FSKILL.md",[],[135,136,137,138,139,140,141,142,26,73,67,143,68,144,145,146,147,148,149,150],"scanner","vulnerability scanner","vulnerability","prioritization","2025","mapping","chain","supply chain","Bash","analysis","risk","owasp","supply chain security","security","supply","advanced",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":28},"01M6G60JZBB8DK0W3DSG1WNMZK","threat-modeling-expert","Expert in threat modeling methodologies, security architecture review, and risk assessment. Masters STRIDE, PASTA, attack trees, and security requirement extraction. Use for security architecture r...","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fsecurity\u002Fthreat-modeling-expert\u002FSKILL.md",[],[158,159,160,161,162,163,164,165,148,25,166,167,168,26,169,170,171,145,172,173],"modeling","threat modeling expert","threat modeling","expert","threat","risk assessment","architecture review","architecture","security architecture review","trees","security architecture","security requirement extraction","assessment","extraction","review","stride"]