[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFSM7Q8XCCMJW9XEC5KRNX":3,"skill-same:01M6XFSM7Q8XCCMJW9XEC5KRNX":57},{"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":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":14,"repositoryHtmlUrl":40,"repositoryHomepageUrl":6,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":42,"install":43,"resources":56},"01M6XFSM7Q8XCCMJW9XEC5KRNX","context-management-context-restore",null,"Use when working with context management context restore","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fcontext-management-context-restore\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30],"context restore","context","management","restore","context management context restore","context management","use","community","with","unknown","when","2026","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\ndate_added: \"2026-02-27\"\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":28,"source":26,"date_added":36,"description":7},"2026-02-27","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fcopilot\u002Fcontext-management-context-restore","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill context-management-context-restore",{"tool":49,"command":50},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill context-management-context-restore",{"tool":52,"command":53},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill context-management-context-restore","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fcopilot\u002Fcontext-management-context-restore\u002FSKILL.md",true,[],[58,81,106,116,129,148],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":69,"publishedAt":31},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[64],{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[70,71,72,73,74,75,76,77,78,79,80,27],"wechat search","wechat","search","capabilities","openclaw","design","web search","compliance","web","official","fetch",{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":31},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend tap', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-tap\u002FSKILL.md",[],[88,89,90,91,92,93,72,94,95,96,97,29,98,99,100,101,102,103,104,105],"trend","trend tap","tap","api keys","Bash","time","trending","hacker","aggregator","do","brew","live","data","google","install","read","across","api",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":31},"01M71ATJ9AM5KM15AA8PS302EB","trend-radar","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend pulse', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-radar\u002FSKILL.md",[],[113,114,88,98,99,100,101,92,72,103,115,104,105,29,96,95,94,102,93,91],"trend radar","radar","pulse",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":122,"publishedAt":31},"01M71ATGF095N3EJM9G8SD0BB5","tech-news-daily","每日科技新闻汇——从全球多个 RSS 源抓取最新科技新闻，翻译（英→中）、去重、排版成每日简报。当用户询问科技新闻、科技新鲜事、每日科技、技术动态、Daily Tech News 或类似表达时触发此技能。","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftech-news-daily\u002FSKILL.md",[],[123,124,125,126,127,128],"news","tech","tech news daily","daily","rss","翻译",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":140,"publishedAt":31},"01M71ATFAEAMQ17M70NQNY7SR8","summarize","Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsummarize\u002FSKILL.md",[135],{"categoryNo":136,"categoryName":137,"taxonomyType":138,"taxonomyTypeDesc":139},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[131,78,141,142,143,27,98,144,145,146,90,147,102],"Audio","cli","youtube","images","https","files","pdfs",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":31},"01M71ATDNS3R0M4H936PACQAWZ","sonoscli","Control Sonos speakers (discover\u002Fstatus\u002Fplay\u002Fvolume\u002Fgroup).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsonoscli\u002FSKILL.md",[],[150,155,156,157,102,158,145,159,160,161,162,163],"status","play","go","latest","github","discover","control","cmd","volume"]