[{"data":1,"prerenderedAt":1041},["ShallowReactive",2],{"docs-navigation":3,"\u002Fpacks":80,"\u002Fpacks-surround":1036},[4,14,50,56,62,68,74],{"title":5,"path":6,"stem":7,"children":8},"Getting Started","\u002Fgetting-started","1.getting-started\u002Findex",[9,10],{"title":5,"path":6,"stem":7},{"title":11,"path":12,"stem":13},"Usage","\u002Fgetting-started\u002Fusage","1.getting-started\u002Fusage",{"title":15,"path":16,"stem":17,"children":18},"Packs","\u002Fpacks","2.packs",[19,22,28,34,40,44],{"title":20,"path":16,"stem":21},"Pack System","2.packs\u002Findex",{"title":23,"path":24,"stem":25,"children":26},"Pack Commands","\u002Fpacks\u002Fcommands","2.packs\u002Fcommands\u002Findex",[27],{"title":23,"path":24,"stem":25},{"title":29,"path":30,"stem":31,"children":32},"Curation","\u002Fpacks\u002Fcuration","2.packs\u002Fcuration\u002Findex",[33],{"title":29,"path":30,"stem":31},{"title":35,"path":36,"stem":37,"children":38},"Personas","\u002Fpacks\u002Fpersonas","2.packs\u002Fpersonas\u002Findex",[39],{"title":35,"path":36,"stem":37},{"title":41,"path":42,"stem":43},"Pack Prompt","\u002Fpacks\u002Fprompt","2.packs\u002Fprompt",{"title":45,"path":46,"stem":47,"children":48},"Skills","\u002Fpacks\u002Fskills","2.packs\u002Fskills\u002Findex",[49],{"title":45,"path":46,"stem":47},{"title":51,"path":52,"stem":53,"children":54},"Knowledge Graph & Memory","\u002Fmemory","3.memory\u002Findex",[55],{"title":51,"path":52,"stem":53},{"title":57,"path":58,"stem":59,"children":60},"Configuration","\u002Fconfiguration","4.configuration\u002Findex",[61],{"title":57,"path":58,"stem":59},{"title":63,"path":64,"stem":65,"children":66},"Architecture","\u002Farchitecture","5.architecture\u002Findex",[67],{"title":63,"path":64,"stem":65},{"title":69,"path":70,"stem":71,"children":72},"Commands","\u002Fcommands","6.commands\u002Findex",[73],{"title":69,"path":70,"stem":71},{"title":75,"path":76,"stem":77,"children":78},"Security","\u002Fsecurity","7.security\u002Findex",[79],{"title":75,"path":76,"stem":77},{"id":81,"title":20,"body":82,"description":1029,"extension":1030,"links":1031,"meta":1032,"navigation":592,"path":16,"seo":1034,"stem":21,"__hash__":1035},"docs\u002F2.packs\u002Findex.md",{"type":83,"value":84,"toc":1014},"minimark",[85,90,94,98,101,138,141,145,155,166,170,173,266,269,566,570,651,655,711,715,722,727,744,747,751,757,760,814,821,828,832,837,857,862,876,881,892,896,899,1010],[86,87,89],"h2",{"id":88},"what-are-packs","What Are Packs?",[91,92,93],"p",{},"Packs specialize Working Mind for a domain. A pack is a directory containing a system prompt, tool declarations, skills, personas, and commands. The core agent is pack-agnostic -- it knows nothing about any domain. Packs define the domain.",[86,95,97],{"id":96},"the-starter-pack","The Starter Pack",[91,99,100],{},"The only pack that ships with Working Mind today. It provides:",[102,103,104,112,123,132],"ul",{},[105,106,107,111],"li",{},[108,109,110],"strong",{},"Persistent memory"," -- built-in SQLite knowledge graph with FTS5 search, contradiction detection, and temporal validity",[105,113,114,117,118,122],{},[108,115,116],{},"Web search"," -- via Brave Search MCP server (requires ",[119,120,121],"code",{},"BRAVE_API_KEY",")",[105,124,125,128,129,122],{},[108,126,127],{},"Web scraping"," -- via Firecrawl MCP server (requires ",[119,130,131],{},"FIRECRAWL_API_KEY",[105,133,134,137],{},[108,135,136],{},"Session summary and export"," -- built-in curation prompts",[91,139,140],{},"The starter pack works immediately. Memory works without any API keys or MCP servers. Search and scraping are optional -- add keys when you're ready.",[86,142,144],{"id":143},"pack-anatomy","Pack Anatomy",[146,147,152],"pre",{"className":148,"code":150,"language":151},[149],"language-text","my-pack\u002F\n  pack.json       -- name, version, MCP server declarations, settings\n  prompt.md       -- the domain expert system prompt\n  skills\u002F         -- reusable subroutines (step-by-step workflows)\n  commands\u002F       -- slash command entry points (markdown files)\n  personas\u002F       -- execution modes (role-specific behaviors)\n  curation\u002F       -- output templates (summarize + export formats)\n","text",[119,153,150],{"__ignoreMap":154},"",[91,156,157,158,161,162,165],{},"Every file is human-readable. The directory is forkable (copy it, edit ",[119,159,160],{},"prompt.md","), versionable (",[119,163,164],{},"pack.json"," has a version field), distributable (push to git), and composable (load multiple packs).",[86,167,169],{"id":168},"pack-sections","Pack Sections",[91,171,172],{},"Each part of the pack serves a specific purpose:",[174,175,176,192],"table",{},[177,178,179],"thead",{},[180,181,182,186,189],"tr",{},[183,184,185],"th",{},"Section",[183,187,188],{},"Purpose",[183,190,191],{},"See",[193,194,195,210,224,238,252],"tbody",{},[180,196,197,202,205],{},[198,199,200],"td",{},[119,201,160],{},[198,203,204],{},"Domain expert system prompt",[198,206,207],{},[208,209,41],"a",{"href":42},[180,211,212,217,220],{},[198,213,214],{},[119,215,216],{},"personas\u002F",[198,218,219],{},"Execution modes with tool filters",[198,221,222],{},[208,223,35],{"href":36},[180,225,226,231,234],{},[198,227,228],{},[119,229,230],{},"skills\u002F",[198,232,233],{},"Reusable multi-step subroutines",[198,235,236],{},[208,237,45],{"href":46},[180,239,240,245,248],{},[198,241,242],{},[119,243,244],{},"commands\u002F",[198,246,247],{},"Slash command entry points",[198,249,250],{},[208,251,23],{"href":24},[180,253,254,259,262],{},[198,255,256],{},[119,257,258],{},"curation\u002F",[198,260,261],{},"Output and export templates",[198,263,264],{},[208,265,29],{"href":30},[86,267,164],{"id":268},"packjson",[146,270,274],{"className":271,"code":272,"language":273,"meta":154,"style":154},"language-json shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","{\n  \"name\": \"my-pack\",\n  \"version\": \"0.1.0\",\n  \"description\": \"A pack for my domain\",\n  \"prompt\": \"prompt.md\",\n  \"mcpServers\": {\n    \"brave-search\": {\n      \"package\": \"@brave\u002Fbrave-search-mcp-server\",\n      \"required\": false\n    }\n  },\n  \"personas\": {\n    \"analyst\": { \"prompt\": \"personas\u002Fanalyst.md\" }\n  },\n  \"skills\": {\n    \"deep-research\": { \"instructions\": \"skills\u002Fdeep-research.md\" }\n  }\n}\n","json",[119,275,276,285,313,334,355,375,390,406,429,444,450,456,470,503,508,522,554,560],{"__ignoreMap":154},[277,278,281],"span",{"class":279,"line":280},"line",1,[277,282,284],{"class":283},"sMK4o","{\n",[277,286,288,291,295,298,301,304,308,310],{"class":279,"line":287},2,[277,289,290],{"class":283},"  \"",[277,292,294],{"class":293},"spNyl","name",[277,296,297],{"class":283},"\"",[277,299,300],{"class":283},":",[277,302,303],{"class":283}," \"",[277,305,307],{"class":306},"sfazB","my-pack",[277,309,297],{"class":283},[277,311,312],{"class":283},",\n",[277,314,316,318,321,323,325,327,330,332],{"class":279,"line":315},3,[277,317,290],{"class":283},[277,319,320],{"class":293},"version",[277,322,297],{"class":283},[277,324,300],{"class":283},[277,326,303],{"class":283},[277,328,329],{"class":306},"0.1.0",[277,331,297],{"class":283},[277,333,312],{"class":283},[277,335,337,339,342,344,346,348,351,353],{"class":279,"line":336},4,[277,338,290],{"class":283},[277,340,341],{"class":293},"description",[277,343,297],{"class":283},[277,345,300],{"class":283},[277,347,303],{"class":283},[277,349,350],{"class":306},"A pack for my domain",[277,352,297],{"class":283},[277,354,312],{"class":283},[277,356,358,360,363,365,367,369,371,373],{"class":279,"line":357},5,[277,359,290],{"class":283},[277,361,362],{"class":293},"prompt",[277,364,297],{"class":283},[277,366,300],{"class":283},[277,368,303],{"class":283},[277,370,160],{"class":306},[277,372,297],{"class":283},[277,374,312],{"class":283},[277,376,378,380,383,385,387],{"class":279,"line":377},6,[277,379,290],{"class":283},[277,381,382],{"class":293},"mcpServers",[277,384,297],{"class":283},[277,386,300],{"class":283},[277,388,389],{"class":283}," {\n",[277,391,393,396,400,402,404],{"class":279,"line":392},7,[277,394,395],{"class":283},"    \"",[277,397,399],{"class":398},"sBMFI","brave-search",[277,401,297],{"class":283},[277,403,300],{"class":283},[277,405,389],{"class":283},[277,407,409,412,416,418,420,422,425,427],{"class":279,"line":408},8,[277,410,411],{"class":283},"      \"",[277,413,415],{"class":414},"sbssI","package",[277,417,297],{"class":283},[277,419,300],{"class":283},[277,421,303],{"class":283},[277,423,424],{"class":306},"@brave\u002Fbrave-search-mcp-server",[277,426,297],{"class":283},[277,428,312],{"class":283},[277,430,432,434,437,439,441],{"class":279,"line":431},9,[277,433,411],{"class":283},[277,435,436],{"class":414},"required",[277,438,297],{"class":283},[277,440,300],{"class":283},[277,442,443],{"class":283}," false\n",[277,445,447],{"class":279,"line":446},10,[277,448,449],{"class":283},"    }\n",[277,451,453],{"class":279,"line":452},11,[277,454,455],{"class":283},"  },\n",[277,457,459,461,464,466,468],{"class":279,"line":458},12,[277,460,290],{"class":283},[277,462,463],{"class":293},"personas",[277,465,297],{"class":283},[277,467,300],{"class":283},[277,469,389],{"class":283},[277,471,473,475,478,480,482,485,487,489,491,493,495,498,500],{"class":279,"line":472},13,[277,474,395],{"class":283},[277,476,477],{"class":398},"analyst",[277,479,297],{"class":283},[277,481,300],{"class":283},[277,483,484],{"class":283}," {",[277,486,303],{"class":283},[277,488,362],{"class":414},[277,490,297],{"class":283},[277,492,300],{"class":283},[277,494,303],{"class":283},[277,496,497],{"class":306},"personas\u002Fanalyst.md",[277,499,297],{"class":283},[277,501,502],{"class":283}," }\n",[277,504,506],{"class":279,"line":505},14,[277,507,455],{"class":283},[277,509,511,513,516,518,520],{"class":279,"line":510},15,[277,512,290],{"class":283},[277,514,515],{"class":293},"skills",[277,517,297],{"class":283},[277,519,300],{"class":283},[277,521,389],{"class":283},[277,523,525,527,530,532,534,536,538,541,543,545,547,550,552],{"class":279,"line":524},16,[277,526,395],{"class":283},[277,528,529],{"class":398},"deep-research",[277,531,297],{"class":283},[277,533,300],{"class":283},[277,535,484],{"class":283},[277,537,303],{"class":283},[277,539,540],{"class":414},"instructions",[277,542,297],{"class":283},[277,544,300],{"class":283},[277,546,303],{"class":283},[277,548,549],{"class":306},"skills\u002Fdeep-research.md",[277,551,297],{"class":283},[277,553,502],{"class":283},[277,555,557],{"class":279,"line":556},17,[277,558,559],{"class":283},"  }\n",[277,561,563],{"class":279,"line":562},18,[277,564,565],{"class":283},"}\n",[86,567,569],{"id":568},"loading-packs","Loading Packs",[146,571,575],{"className":572,"code":573,"language":574,"meta":154,"style":154},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Load the default starter pack\nwmind\n\n# Load a specific pack\nwmind --pack researcher\n\n# Load a pack from a local directory\nwmind --pack .\u002Fmy-custom-pack\n\n# Load multiple packs\nwmind --pack starter --pack my-custom-pack\n","bash",[119,576,577,583,588,594,599,610,614,619,628,632,637],{"__ignoreMap":154},[277,578,579],{"class":279,"line":280},[277,580,582],{"class":581},"sHwdD","# Load the default starter pack\n",[277,584,585],{"class":279,"line":287},[277,586,587],{"class":398},"wmind\n",[277,589,590],{"class":279,"line":315},[277,591,593],{"emptyLinePlaceholder":592},true,"\n",[277,595,596],{"class":279,"line":336},[277,597,598],{"class":581},"# Load a specific pack\n",[277,600,601,604,607],{"class":279,"line":357},[277,602,603],{"class":398},"wmind",[277,605,606],{"class":306}," --pack",[277,608,609],{"class":306}," researcher\n",[277,611,612],{"class":279,"line":377},[277,613,593],{"emptyLinePlaceholder":592},[277,615,616],{"class":279,"line":392},[277,617,618],{"class":581},"# Load a pack from a local directory\n",[277,620,621,623,625],{"class":279,"line":408},[277,622,603],{"class":398},[277,624,606],{"class":306},[277,626,627],{"class":306}," .\u002Fmy-custom-pack\n",[277,629,630],{"class":279,"line":431},[277,631,593],{"emptyLinePlaceholder":592},[277,633,634],{"class":279,"line":446},[277,635,636],{"class":581},"# Load multiple packs\n",[277,638,639,641,643,646,648],{"class":279,"line":452},[277,640,603],{"class":398},[277,642,606],{"class":306},[277,644,645],{"class":306}," starter",[277,647,606],{"class":306},[277,649,650],{"class":306}," my-custom-pack\n",[86,652,654],{"id":653},"building-a-pack","Building a Pack",[656,657,658,668,676,687,695,702],"ol",{},[105,659,660,663,664,667],{},[108,661,662],{},"Copy the starter pack"," -- it's in ",[119,665,666],{},"packs\u002Fstarter\u002F"," of the Working Mind repository",[105,669,670,675],{},[108,671,672,673],{},"Edit ",[119,674,160],{}," -- write a domain expert system prompt",[105,677,678,681,682,684,685],{},[108,679,680],{},"Add MCP servers"," -- declare them in ",[119,683,164],{}," under ",[119,686,382],{},[105,688,689,692,693],{},[108,690,691],{},"Add skills"," -- create markdown files in ",[119,694,230],{},[105,696,697,692,700],{},[108,698,699],{},"Add personas",[119,701,216],{},[105,703,704,707,708],{},[108,705,706],{},"Test"," -- run ",[119,709,710],{},"wmind --pack .\u002Fpath\u002Fto\u002Fmy-pack",[86,712,714],{"id":713},"why-packs-work-the-expert-curated-advantage","Why Packs Work: The Expert-Curated Advantage",[91,716,717,718,721],{},"The pack system is built on a specific hypothesis: ",[108,719,720],{},"domain experts who curate their own knowledge graphs produce the highest quality training data that exists."," Not synthetic data. Not crowdsourced annotations. Not GPT-4-generated pairs. Expert-curated, iterated-to-correctness, graph-structured knowledge.",[723,724,726],"h3",{"id":725},"the-loop","The Loop",[656,728,729,732,735,738,741],{},[105,730,731],{},"You are a domain expert. You know your field.",[105,733,734],{},"You instruct Working Mind. It saves what you teach it.",[105,736,737],{},"You ask questions. It answers from the graph. You know immediately if the answer is right.",[105,739,740],{},"If it's wrong, you fix the graph. The next answer is right.",[105,742,743],{},"When the answers are consistently perfect, the graph is done.",[91,745,746],{},"That graph -- verified by the only person qualified to verify it -- is the single best source of domain training data. Every entity was explicitly created. Every observation was verified. Every relation was intentional. No other data pipeline produces this quality.",[723,748,750],{"id":749},"from-graph-to-fine-tuned-model","From Graph to Fine-Tuned Model",[91,752,753,754],{},"The pack hypothesis extends further: ",[108,755,756],{},"if the graph produces perfect answers for a domain expert, it contains enough structure to fine-tune a small model that answers those same questions without the graph.",[91,758,759],{},"Research supports this:",[174,761,762,772],{},[177,763,764],{},[180,765,766,769],{},[183,767,768],{},"Evidence",[183,770,771],{},"Source",[193,773,774,782,790,798,806],{},[180,775,776,779],{},[198,777,778],{},"Break-even at ~100 labeled samples",[198,780,781],{},"Multiple papers",[180,783,784,787],{},[198,785,786],{},"OPT-350M beats ChatGPT on tool calling (+3x)",[198,788,789],{},"arxiv:2305.15044",[180,791,792,795],{},[198,793,794],{},"Lawma-8B beats Claude 3.7 Sonnet on legal (+9pp)",[198,796,797],{},"arxiv:2501.14013",[180,799,800,803],{},[198,801,802],{},"500 curated examples > 5,000 auto-generated",[198,804,805],{},"Consistent across fine-tuning literature",[180,807,808,811],{},[198,809,810],{},"KG data needs no GPT-4 quality check -- it is already curated",[198,812,813],{},"KG2Tool (arxiv:2506.21071)",[91,815,816,817,820],{},"The key insight: ",[108,818,819],{},"shit in, shit out cannot be prevented by any pipeline."," But a domain expert who iterates until the graph is right eliminates the problem at the source. The data quality is solved by definition -- not by validation, not by filtering, not by synthetic quality checks, but by the expert who built it.",[91,822,823,824,827],{},"When your graph produces perfect answers, you export it. The ",[119,825,826],{},".oexp"," format carries the full graph, taxonomy, persona lenses, and readiness assessment. From there, instruction pairs are generated and a small model is fine-tuned. The result: a domain champion that knows your field better than any frontier model -- because it was trained on knowledge you verified yourself.",[723,829,831],{"id":830},"what-we-know-and-whats-next","What We Know and What's Next",[91,833,834],{},[108,835,836],{},"What works now:",[102,838,839,842,845,848,851,854],{},[105,840,841],{},"The pack format loads correctly",[105,843,844],{},"MCP servers wire up as declared",[105,846,847],{},"Skills activate and inject instructions",[105,849,850],{},"Personas change agent behavior",[105,852,853],{},"Domain-specific packs measurably improve tool selection and task completion",[105,855,856],{},"Expert users who iterate get consistent, correct answers from their graphs",[91,858,859],{},[108,860,861],{},"What's in development:",[102,863,864,870,873],{},[105,865,866,867,869],{},"The graph-to-fine-tune pipeline (",[119,868,826],{}," export + instruction pair generation)",[105,871,872],{},"Persona-driven export (same graph, multiple training data flavors)",[105,874,875],{},"Readiness checks (is your graph deep enough for fine-tuning?)",[91,877,878],{},[108,879,880],{},"The path:",[102,882,883,886,889],{},[105,884,885],{},"Today: build your graph, verify your answers, compound your knowledge",[105,887,888],{},"Next: export your graph as training data",[105,890,891],{},"Future: fine-tune a domain champion from your expertise",[86,893,895],{"id":894},"research-context","Research Context",[91,897,898],{},"The pack architecture draws on and aligns with several active research threads:",[174,900,901,911],{},[177,902,903],{},[180,904,905,908],{},[183,906,907],{},"Paper",[183,909,910],{},"Connection to Packs",[193,912,913,926,938,950,962,974,986,998],{},[180,914,915,923],{},[198,916,917],{},[208,918,922],{"href":919,"rel":920},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.20867",[921],"nofollow","SoK: Agentic Skills (Jiang et al. 2026)",[198,924,925],{},"Maps the full lifecycle of agentic skills. Packs implement several of their seven design patterns: metadata-driven disclosure, natural-language skills, and git-based distribution. Their security analysis validates our decision to keep packs local-only.",[180,927,928,935],{},[198,929,930],{},[208,931,934],{"href":932,"rel":933},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2512.16301",[921],"Adaptation of Agentic AI (Jiang et al. 2025)",[198,936,937],{},"Four-paradigm framework for agent adaptation. Packs are primarily T1 (reusable, agent-agnostic modules). Persona-driven tool filtering moves toward T2 (agent-supervised modules).",[180,939,940,947],{},[198,941,942],{},[208,943,946],{"href":944,"rel":945},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.03279",[921],"Agentic Proposing (Jiao et al. 2026)",[198,948,949],{},"Compositional skill synthesis outperforms monolithic approaches. Pack skills implement this: each skill is a composable subroutine the agent activates on demand.",[180,951,952,959],{},[198,953,954],{},[208,955,958],{"href":956,"rel":957},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.05860",[921],"MACRO (Fan et al. 2026)",[198,960,961],{},"Agents that discover and register composite tools from execution trajectories outperform static tool sets. Packs should evolve from declarative to self-improving.",[180,963,964,971],{},[198,965,966],{},[208,967,970],{"href":968,"rel":969},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.13220",[921],"EvoTest (He et al. 2025)",[198,972,973],{},"Evolves the entire agent configuration after every episode. Packs are a static snapshot of this -- future versions could auto-tune based on session outcomes.",[180,975,976,983],{},[198,977,978],{},[208,979,982],{"href":980,"rel":981},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2409.03215",[921],"xLAM (Zhang et al. 2024)",[198,984,985],{},"Purpose-trained action models that outperform GPT-4 on tool use. Packs take the opposite approach: configure the environment, not the model. Both paths are complementary.",[180,987,988,995],{},[198,989,990],{},[208,991,994],{"href":992,"rel":993},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2604.11557",[921],"UniToolCall (Liang et al. 2026)",[198,996,997],{},"Fine-tuned 8B models achieve 93% tool-call precision with unified training data. Packs provide the domain-specific tool schemas that make such fine-tuning possible.",[180,999,1000,1007],{},[198,1001,1002],{},[208,1003,1006],{"href":1004,"rel":1005},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.21071",[921],"KG2Tool (arxiv 2506.21071)",[198,1008,1009],{},"KG-constructed data does not need quality checking because KGs are already curated. 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