[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71FMTW0PPAQTA72FPBH6M7P":3,"skill-same:01M71FMTW0PPAQTA72FPBH6M7P":83},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":13,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":62,"install":63,"resources":76},"01M71FMTW0PPAQTA72FPBH6M7P","faiss",null,"Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.","gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6","gjbvsjjvgm-sketch","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F278511911?v=4","01M71F3JXKZQ70H2KCHHTXKZDA","01M71F3JY8A79Z3NZBW08TF4JD",0,"MIT","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Ffaiss\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"Facebook AI","when","for","orchestra","you","types","Large Scale","K-NN","Billion-Scale","GPU Acceleration","performance","CPU","high-performance","clustering","library","high performance","vector","search","rag","2026-11-10 10:12:30",false,"---\nname: faiss\ndescription: Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [faiss-cpu, faiss-gpu, numpy]\nmetadata:\n  hermes:\n    tags: [RAG, FAISS, Similarity Search, Vector Search, Facebook AI, GPU Acceleration, Billion-Scale, K-NN, HNSW, High Performance, Large Scale]\n\n---\n\n# FAISS - Efficient Similarity Search\n\nFacebook AI's library for billion-scale vector similarity search.\n\n## When to use FAISS\n\n**Use FAISS when:**\n- Need fast similarity search on large vector datasets (millions\u002Fbillions)\n- GPU acceleration required\n- Pure vector similarity (no metadata filtering needed)\n- High throughput, low latency critical\n- Offline\u002Fbatch processing of embeddings\n\n**Metrics**:\n- **31,700+ GitHub stars**\n- Meta\u002FFacebook AI Research\n- **Handles billions of vectors**\n- **C++** with Python bindings\n\n**Use alternatives instead**:\n- **Chroma\u002FPinecone**: Need metadata filtering\n- **Weaviate**: Need full database features\n- **Annoy**: Simpler, fewer features\n\n## Quick start\n\n### Installation\n\n```bash\n# CPU only\npip install faiss-cpu\n\n# GPU support\npip install faiss-gpu\n```\n\n### Basic usage\n\n```python\nimport faiss\nimport numpy as np\n\n# Create sample data (1000 vectors, 128 dimensions)\nd = 128\nnb = 1000\nvectors = np.random.random((nb, d)).astype('float32')\n\n# Create index\nindex = faiss.IndexFlatL2(d)  # L2 distance\nindex.add(vectors)             # Add vectors\n\n# Search\nk = 5  # Find 5 nearest neighbors\nquery = np.random.random((1, d)).astype('float32')\ndistances, indices = index.search(query, k)\n\nprint(f\"Nearest neighbors: {indices}\")\nprint(f\"Distances: {distances}\")\n```\n\n## Index types\n\n### 1. Flat (exact search)\n\n```python\n# L2 (Euclidean) distance\nindex = faiss.IndexFlatL2(d)\n\n# Inner product (cosine similarity if normalized)\nindex = faiss.IndexFlatIP(d)\n\n# Slowest, most accurate\n```\n\n### 2. IVF (inverted file) - Fast approximate\n\n```python\n# Create quantizer\nquantizer = faiss.IndexFlatL2(d)\n\n# IVF index with 100 clusters\nnlist = 100\nindex = faiss.IndexIVFFlat(quantizer, d, nlist)\n\n# Train on data\nindex.train(vectors)\n\n# Add vectors\nindex.add(vectors)\n\n# Search (nprobe = clusters to search)\nindex.nprobe = 10\ndistances, indices = index.search(query, k)\n```\n\n### 3. HNSW (Hierarchical NSW) - Best quality\u002Fspeed\n\n```python\n# HNSW index\nM = 32  # Number of connections per layer\nindex = faiss.IndexHNSWFlat(d, M)\n\n# No training needed\nindex.add(vectors)\n\n# Search\ndistances, indices = index.search(query, k)\n```\n\n### 4. Product Quantization - Memory efficient\n\n```python\n# PQ reduces memory by 16-32×\nm = 8   # Number of subquantizers\nnbits = 8\nindex = faiss.IndexPQ(d, m, nbits)\n\n# Train and add\nindex.train(vectors)\nindex.add(vectors)\n```\n\n## Save and load\n\n```python\n# Save index\nfaiss.write_index(index, \"large.index\")\n\n# Load index\nindex = faiss.read_index(\"large.index\")\n\n# Continue using\ndistances, indices = index.search(query, k)\n```\n\n## GPU acceleration\n\n```python\n# Single GPU\nres = faiss.StandardGpuResources()\nindex_cpu = faiss.IndexFlatL2(d)\nindex_gpu = faiss.index_cpu_to_gpu(res, 0, index_cpu)  # GPU 0\n\n# Multi-GPU\nindex_gpu = faiss.index_cpu_to_all_gpus(index_cpu)\n\n# 10-100× faster than CPU\n```\n\n## LangChain integration\n\n```python\nfrom langchain_community.vectorstores import FAISS\nfrom langchain_openai import OpenAIEmbeddings\n\n# Create FAISS vector store\nvectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())\n\n# Save\nvectorstore.save_local(\"faiss_index\")\n\n# Load\nvectorstore = FAISS.load_local(\n    \"faiss_index\",\n    OpenAIEmbeddings(),\n    allow_dangerous_deserialization=True\n)\n\n# Search\nresults = vectorstore.similarity_search(\"query\", k=5)\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.faiss import FaissVectorStore\nimport faiss\n\n# Create FAISS index\nd = 1536\nfaiss_index = faiss.IndexFlatL2(d)\n\nvector_store = FaissVectorStore(faiss_index=faiss_index)\n```\n\n## Best practices\n\n1. **Choose right index type** - Flat for \u003C10K, IVF for 10K-1M, HNSW for quality\n2. **Normalize for cosine** - Use IndexFlatIP with normalized vectors\n3. **Use GPU for large datasets** - 10-100× faster\n4. **Save trained indices** - Training is expensive\n5. **Tune nprobe\u002Fef_search** - Balance speed\u002Faccuracy\n6. **Monitor memory** - PQ for large datasets\n7. **Batch queries** - Better GPU utilization\n\n## Performance\n\n| Index Type | Build Time | Search Time | Memory | Accuracy |\n|------------|------------|-------------|--------|----------|\n| Flat | Fast | Slow | High | 100% |\n| IVF | Medium | Fast | Medium | 95-99% |\n| HNSW | Slow | Fastest | High | 99% |\n| PQ | Medium | Fast | Low | 90-95% |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffaiss ⭐ 31,700+\n- **Wiki**: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffaiss\u002Fwiki\n- **License**: MIT\n\n\n","# FAISS - Efficient Similarity Search\n\nFacebook AI's library for billion-scale vector similarity search.\n\n## When to use FAISS\n\n**Use FAISS when:**\n- Need fast similarity search on large vector datasets (millions\u002Fbillions)\n- GPU acceleration required\n- Pure vector similarity (no metadata filtering needed)\n- High throughput, low latency critical\n- Offline\u002Fbatch processing of embeddings\n\n**Metrics**:\n- **31,700+ GitHub stars**\n- Meta\u002FFacebook AI Research\n- **Handles billions of vectors**\n- **C++** with Python bindings\n\n**Use alternatives instead**:\n- **Chroma\u002FPinecone**: Need metadata filtering\n- **Weaviate**: Need full database features\n- **Annoy**: Simpler, fewer features\n\n## Quick start\n\n### Installation\n\n```bash\n# CPU only\npip install faiss-cpu\n\n# GPU support\npip install faiss-gpu\n```\n\n### Basic usage\n\n```python\nimport faiss\nimport numpy as np\n\n# Create sample data (1000 vectors, 128 dimensions)\nd = 128\nnb = 1000\nvectors = np.random.random((nb, d)).astype('float32')\n\n# Create index\nindex = faiss.IndexFlatL2(d)  # L2 distance\nindex.add(vectors)             # Add vectors\n\n# Search\nk = 5  # Find 5 nearest neighbors\nquery = np.random.random((1, d)).astype('float32')\ndistances, indices = index.search(query, k)\n\nprint(f\"Nearest neighbors: {indices}\")\nprint(f\"Distances: {distances}\")\n```\n\n## Index types\n\n### 1. Flat (exact search)\n\n```python\n# L2 (Euclidean) distance\nindex = faiss.IndexFlatL2(d)\n\n# Inner product (cosine similarity if normalized)\nindex = faiss.IndexFlatIP(d)\n\n# Slowest, most accurate\n```\n\n### 2. IVF (inverted file) - Fast approximate\n\n```python\n# Create quantizer\nquantizer = faiss.IndexFlatL2(d)\n\n# IVF index with 100 clusters\nnlist = 100\nindex = faiss.IndexIVFFlat(quantizer, d, nlist)\n\n# Train on data\nindex.train(vectors)\n\n# Add vectors\nindex.add(vectors)\n\n# Search (nprobe = clusters to search)\nindex.nprobe = 10\ndistances, indices = index.search(query, k)\n```\n\n### 3. HNSW (Hierarchical NSW) - Best quality\u002Fspeed\n\n```python\n# HNSW index\nM = 32  # Number of connections per layer\nindex = faiss.IndexHNSWFlat(d, M)\n\n# No training needed\nindex.add(vectors)\n\n# Search\ndistances, indices = index.search(query, k)\n```\n\n### 4. Product Quantization - Memory efficient\n\n```python\n# PQ reduces memory by 16-32×\nm = 8   # Number of subquantizers\nnbits = 8\nindex = faiss.IndexPQ(d, m, nbits)\n\n# Train and add\nindex.train(vectors)\nindex.add(vectors)\n```\n\n## Save and load\n\n```python\n# Save index\nfaiss.write_index(index, \"large.index\")\n\n# Load index\nindex = faiss.read_index(\"large.index\")\n\n# Continue using\ndistances, indices = index.search(query, k)\n```\n\n## GPU acceleration\n\n```python\n# Single GPU\nres = faiss.StandardGpuResources()\nindex_cpu = faiss.IndexFlatL2(d)\nindex_gpu = faiss.index_cpu_to_gpu(res, 0, index_cpu)  # GPU 0\n\n# Multi-GPU\nindex_gpu = faiss.index_cpu_to_all_gpus(index_cpu)\n\n# 10-100× faster than CPU\n```\n\n## LangChain integration\n\n```python\nfrom langchain_community.vectorstores import FAISS\nfrom langchain_openai import OpenAIEmbeddings\n\n# Create FAISS vector store\nvectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())\n\n# Save\nvectorstore.save_local(\"faiss_index\")\n\n# Load\nvectorstore = FAISS.load_local(\n    \"faiss_index\",\n    OpenAIEmbeddings(),\n    allow_dangerous_deserialization=True\n)\n\n# Search\nresults = vectorstore.similarity_search(\"query\", k=5)\n```\n\n## LlamaIndex integration\n\n```python\nfrom llama_index.vector_stores.faiss import FaissVectorStore\nimport faiss\n\n# Create FAISS index\nd = 1536\nfaiss_index = faiss.IndexFlatL2(d)\n\nvector_store = FaissVectorStore(faiss_index=faiss_index)\n```\n\n## Best practices\n\n1. **Choose right index type** - Flat for \u003C10K, IVF for 10K-1M, HNSW for quality\n2. **Normalize for cosine** - Use IndexFlatIP with normalized vectors\n3. **Use GPU for large datasets** - 10-100× faster\n4. **Save trained indices** - Training is expensive\n5. **Tune nprobe\u002Fef_search** - Balance speed\u002Faccuracy\n6. **Monitor memory** - PQ for large datasets\n7. **Batch queries** - Better GPU utilization\n\n## Performance\n\n| Index Type | Build Time | Search Time | Memory | Accuracy |\n|------------|------------|-------------|--------|----------|\n| Flat | Fast | Slow | High | 100% |\n| IVF | Medium | Fast | Medium | 95-99% |\n| HNSW | Slow | Fastest | High | 99% |\n| PQ | Medium | Fast | Low | 90-95% |\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffaiss ⭐ 31,700+\n- **Wiki**: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffaiss\u002Fwiki\n- **License**: MIT\n\n\n",{"name":5,"author":42,"license":14,"version":43,"metadata":44,"description":7,"dependencies":53},"Orchestra Research","1.0.0",{"hermes":45},{"tags":46},[47,48,49,50,18,27,26,25,51,52,24],"RAG","FAISS","Similarity Search","Vector Search","HNSW","High Performance",[54,55,56],"faiss-cpu","faiss-gpu","numpy","main","93bbc269671fbb340afa15b36a8766f15dc30df2","hermes-agent\u002Foptional-skills\u002Fmlops\u002Ffaiss","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch","2026-04-27 02:51:28",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill faiss",{"tool":69,"command":70},"bunx","bunx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill faiss",{"tool":72,"command":73},"pnpm","pnpm dlx skills add gjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6 --skill faiss","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Ffaiss\u002FSKILL.md",true,[77],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Findex_types.md","REFERENCE","参考文档",6181,"https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes-agent\u002Foptional-skills\u002Fmlops\u002Ffaiss\u002Freferences\u002Findex_types.md",[84,124,152,168,187,208],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":106,"publishedAt":37},"01M71G6TJ2RAQ1J0PV3K6D9GQA","writing-plans","Use when you have a spec or requirements for a multi-step task. Creates comprehensive implementation plans with bite-sized tasks, exact file paths, and complete code examples.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fwriting-plans\u002FSKILL.md",[90,95,98,103],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":96,"categoryName":97,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",{"categoryNo":104,"categoryName":105,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000036","内容创作者",[107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,22,20,123,19],"writing","writing plans","plans","design","review","workflow","documentation","tasks","development","code","planning","code-review","subagent","superpowers","paths","test-driven-development","test-driven",{"skillNo":125,"name":122,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":137,"publishedAt":37},"01M71G6R81013R3T7W5VP56GCB","Use when implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle with test-first approach.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Ftest-driven-development\u002FSKILL.md",[129,132,135,136],{"categoryNo":130,"categoryName":131,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":133,"categoryName":134,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":96,"categoryName":97,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":104,"categoryName":105,"taxonomyType":101,"taxonomyTypeDesc":102},[138,139,115,140,141,107,142,116,143,119,120,144,145,146,147,148,149,150,151,19],"test","test driven development","driven","testing","debugging","refactor","red-green-refactor","cycle","green","systematic","bugfix","implementing","systematic-debugging","test-first",{"skillNo":153,"name":150,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":158,"publishedAt":37},"01M71G6N0Y546JZEDF25E2PMCE","Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fsystematic-debugging\u002FSKILL.md",[157],{"categoryNo":133,"categoryName":134,"taxonomyType":93,"taxonomyTypeDesc":94},[159,142,147,107,138,115,119,160,120,161,162,163,109,164,122,165,123,166,167,19],"systematic debugging","use","bug","root-cause","problem-solving","problem","fixes","phase","root cause",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":180,"publishedAt":37},"01M71G6J0DXEGY48GH4KVW2SJH","subagent-driven-development","Use when executing implementation plans with independent tasks. Dispatches fresh delegate_task per task with two-stage review (spec compliance then code quality).","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fsubagent-driven-development\u002FSKILL.md",[174,175,176,177],{"categoryNo":96,"categoryName":97,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":104,"categoryName":105,"taxonomyType":101,"taxonomyTypeDesc":102},{"categoryNo":178,"categoryName":179,"taxonomyType":101,"taxonomyTypeDesc":102},"01JBSEEDCAT000000000000037","律师与法务",[115,181,119,140,107,111,112,182,114,116,183,118,184,185,120,109,186,122,123,19],"subagent driven development","code quality","delegation","parallel","delegate","fresh",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":195,"publishedAt":37},"01M71G6EFFBQX6634E6XHB09Y1","requesting-code-review","Pre-commit verification pipeline — static security scan, baseline-aware quality gates, independent reviewer subagent, and auto-fix loop. Use after code changes and before committing, pushing, or opening a PR.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Frequesting-code-review\u002FSKILL.md",[193,194],{"categoryNo":130,"categoryName":131,"taxonomyType":93,"taxonomyTypeDesc":94},{"categoryNo":99,"categoryName":100,"taxonomyType":101,"taxonomyTypeDesc":102},[196,111,116,197,107,198,118,199,200,201,202,119,203,204,205,120,122,206,207,123],"code review","requesting code review","commit","verification","security scan","pipeline","loop","reviewer","fix","auto","static","auto-fix",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":212,"categories":213,"keywords":214,"publishedAt":37},"01M71G6CM8ANSWVFXCN9AAD58Y","plan","Plan mode for Hermes — inspect context, write a markdown plan into the active workspace's `.hermes\u002Fplans\u002F` directory, and do not execute the work.","https:\u002F\u002Fgithub.com\u002Fgjbvsjjvgm-sketch\u002Fhermes-sovereign-unified-v13.6\u002Fblob\u002Fmain\u002Fhermes_sovereign_data\u002Fskills\u002Fsoftware-development\u002Fplan\u002FSKILL.md",[],[210,107,215,112,115,117,216,119,217,218,219,220,221,222,109,223,224,225,20,226],"directory","markdown","workspace","work","write","implementation","do","active","plan mode","inspect","mode","plan-mode"]