[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CSHTK2ASM708E8AXT3KD3J":3,"skill-same:01M6CSHTK2ASM708E8AXT3KD3J":62},{"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":23,"publishedAt":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":44,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":46,"updatedAt":47,"install":48,"resources":61},"01M6CSHTK2ASM708E8AXT3KD3J","code-refactoring-context-restore",null,"Use when working with code refactoring context restore","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fcode-refactoring-context-restore\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32],"code","refactoring","context restore","context","code refactoring","restore","code refactoring context restore","use","community","2026-11-10 10:12:30",false,"---\nname: code-refactoring-context-restore\ndescription: \"Use when working with code refactoring 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":38,"source":32,"date_added":39,"description":7},"unknown","2026-02-27","main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fcode-refactoring-context-restore",0,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant","https:\u002F\u002Fgithub.com\u002FSam-06060","Mobile Application Developer | Kotlin | Flutter, KMP, Swfit, Android,Swift UI | Building at Saar It Resources | Creator of Jarvis (AI Assistant)","2026-06-23 05:13:38",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill code-refactoring-context-restore",{"tool":54,"command":55},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill code-refactoring-context-restore",{"tool":57,"command":58},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill code-refactoring-context-restore","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fcode-refactoring-context-restore\u002FSKILL.md",true,[],[63,76,92,119,138,165],{"skillNo":64,"name":65,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":73,"publishedAt":33},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[68],{"categoryNo":69,"categoryName":70,"taxonomyType":71,"taxonomyTypeDesc":72},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[74,75],"Test Skill","test",{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":33},"01M6GDRQ40HX22MA6XWC31RHHK","zustand-store-ts","Create Zustand stores following established patterns with proper TypeScript types and middleware.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzustand-store-ts\u002FSKILL.md",[],[83,84,85,86,87,88,89,32,90,91],"zustand store ts","zustand","store","with","types","middleware","patterns","create","typescript",{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":101,"publishedAt":33},"01M6GDRKEA0WE670SPFWX5GKCN","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoom-automation\u002FSKILL.md",[98],{"categoryNo":99,"categoryName":100,"taxonomyType":71,"taxonomyTypeDesc":72},"01JBSEEDCAT000000000000023","Skill与智能体开发",[102,103,104,105,106,107,108,109,110,111,112,113,114,115,32,116,117,118],"automation","zoom automation","zoom","first","Tools","search","MCP","via","meeting","automate","critical","creation","webinars","composio","recordings","management","tracking",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":126,"publishedAt":33},"01M6GDRFS1452PC71JQ38S24HR","zoho-crm-automation","Automate Zoho CRM tasks via Rube MCP (Composio): create\u002Fupdate records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoho-crm-automation\u002FSKILL.md",[125],{"categoryNo":99,"categoryName":100,"taxonomyType":71,"taxonomyTypeDesc":72},[127,102,128,129,130,131,132,32,107,133,115,134,112,111,106,135,90,136,108,137],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":147,"publishedAt":33},"01M6GDRCKQBAVTGHX5XBH26KPZ","zod-validation-expert","Expert in Zod — TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, and tRPC.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzod-validation-expert\u002FSKILL.md",[144],{"categoryNo":145,"categoryName":146,"taxonomyType":71,"taxonomyTypeDesc":72},"01JBSEEDCAT000000000000009","前端开发",[148,149,150,151,152,91,153,154,155,156,157,158,86,159,160,161,162,32,163,164],"expert","zod","zod validation expert","validation","react","safe","parsing","errors","schema validation","react hook form","hook","trpc","form","type","next","schema","Next.js",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":172,"publishedAt":33},"01M6GDR658B08K4MY0RWD4A6EH","zeroize-audit","Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C\u002FC++\u002FRust code handling secrets, keys, passwords, or other sensitive data.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzeroize-audit\u002FSKILL.md",[171],{"categoryNo":99,"categoryName":100,"taxonomyType":71,"taxonomyTypeDesc":72},[173,174,175,176,177,178,179,180,32,181,182,183,184,185,186,187,188,24,189,108],"zeroize audit","audit","write","passwords","get","project","c++","activate","assembly","read","Bash","secrets","analysis","data","flow","verification","rust"]