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Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, or default repository analysis.","explore-code",{"claudeCode":12},"SKILL.md frontmatter at skills/explore-code/SKILL.md",[410,411,413,415],{"path":271,"priority":272},{"path":412,"priority":275},"references/explore-policy.md",{"path":414,"priority":288},"scripts/plan_code_changes.py",{"path":338,"priority":288},{"basePath":417,"description":418,"displayName":419,"installMethods":420,"rationale":421,"selectedPaths":422,"source":339,"sourceLanguage":340,"type":246},"skills/explore-run","Explore-lane experimental execution skill for deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with results summarized in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, or implicit experimentation.","explore-run",{"claudeCode":12},"SKILL.md frontmatter at skills/explore-run/SKILL.md",[423,424,426,428],{"path":271,"priority":272},{"path":425,"priority":275},"references/execution-policy.md",{"path":427,"priority":288},"scripts/plan_variants.py",{"path":338,"priority":288},{"basePath":430,"description":431,"displayName":432,"installMethods":433,"rationale":434,"selectedPaths":435,"source":339,"sourceLanguage":340,"type":246},"skills/minimal-run-and-audit","Trusted-lane execution and reporting skill for README-first AI repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, or end-to-end orchestration by itself.","minimal-run-and-audit",{"claudeCode":12},"SKILL.md frontmatter at skills/minimal-run-and-audit/SKILL.md",[436,437,439,441],{"path":271,"priority":272},{"path":438,"priority":275},"references/reporting-policy.md",{"path":440,"priority":288},"scripts/run_command.py",{"path":338,"priority":288},{"basePath":243,"description":443,"displayName":13,"installMethods":444,"rationale":445,"selectedPaths":446,"source":339,"sourceLanguage":340,"type":246},"Optional narrow helper skill for README-first AI repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.",{"claudeCode":12},"SKILL.md frontmatter at skills/paper-context-resolver/SKILL.md",[447,448],{"path":271,"priority":272},{"path":449,"priority":275},"references/paper-assisted-reproduction.md",{"basePath":451,"description":452,"displayName":453,"installMethods":454,"rationale":455,"selectedPaths":456,"source":339,"sourceLanguage":340,"type":246},"skills/repo-intake-and-plan","Narrow helper skill for README-first AI repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.","repo-intake-and-plan",{"claudeCode":12},"SKILL.md frontmatter at skills/repo-intake-and-plan/SKILL.md",[457,458,460,462],{"path":271,"priority":272},{"path":459,"priority":275},"references/repo-scan-rules.md",{"path":461,"priority":288},"scripts/extract_commands.py",{"path":463,"priority":288},"scripts/scan_repo.py",{"basePath":465,"description":466,"displayName":467,"installMethods":468,"rationale":469,"selectedPaths":470,"source":339,"sourceLanguage":340,"type":246},"skills/run-train","Trusted-lane training execution skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with status, checkpoint, and metric capture written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.","run-train",{"claudeCode":12},"SKILL.md frontmatter at skills/run-train/SKILL.md",[471,472,474,476],{"path":271,"priority":272},{"path":473,"priority":275},"references/training-policy.md",{"path":475,"priority":288},"scripts/run_training.py",{"path":338,"priority":288},{"basePath":478,"description":479,"displayName":480,"installMethods":481,"rationale":482,"selectedPaths":483,"source":339,"sourceLanguage":340,"type":246},"skills/safe-debug","Trusted-lane debug skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.","safe-debug",{"claudeCode":12},"SKILL.md frontmatter at skills/safe-debug/SKILL.md",[484,485,487],{"path":271,"priority":272},{"path":486,"priority":275},"references/debug-policy.md",{"path":488,"priority":288},"scripts/safe_debug.py",{"sources":490},[491],"manual",{"closedIssues90d":8,"description":493,"forks":233,"license":238,"openIssues90d":8,"pushedAt":235,"readmeSize":231,"stars":236,"topics":494},"",[],{"classifiedAt":496,"discoverAt":497,"extractAt":498,"githubAt":498,"updatedAt":496},1778692395631,1778692391648,1778692393876,[215,218,219,217,216],{"evaluatedAt":501,"extractAt":502,"updatedAt":241},1778692586693,1778692396032,[],[505,534,565,594,618,648],{"_creationTime":506,"_id":507,"community":508,"display":509,"identity":515,"providers":519,"relations":527,"tags":530,"workflow":531},1778699234184.6135,"k175frmf44tn80mcd6gvw1c1th86ngq9",{"reviewCount":8},{"description":510,"installMethods":511,"name":513,"sourceUrl":514},"Invoke parallel document-specialist agents for external web searches and documentation lookup",{"claudeCode":512},"Yeachan-Heo/oh-my-claudecode","external-context","https://github.com/Yeachan-Heo/oh-my-claudecode",{"basePath":516,"githubOwner":517,"githubRepo":518,"locale":340,"slug":513,"type":246},"skills/external-context","Yeachan-Heo","oh-my-claudecode",{"evaluate":520,"extract":526},{"promptVersionExtension":208,"promptVersionScoring":209,"score":521,"tags":522,"targetMarket":251,"tier":220},100,[523,218,216,524,525],"search","information-retrieval","multi-agent",{"commitSha":253},{"parentExtensionId":528,"repoId":529},"k17brg5egdw1jbncj1j4wfv3fh86n639","kd74zv63fryf9prygtq7gf4es986n22y",[218,524,525,216,523],{"evaluatedAt":532,"extractAt":533,"updatedAt":532},1778699449790,1778699234184,{"_creationTime":535,"_id":536,"community":537,"display":538,"identity":544,"providers":549,"relations":558,"tags":561,"workflow":562},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":539,"installMethods":540,"name":542,"sourceUrl":543},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":541},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":545,"githubOwner":546,"githubRepo":547,"locale":340,"slug":548,"type":246},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":550,"extract":557},{"promptVersionExtension":208,"promptVersionScoring":209,"score":521,"tags":551,"targetMarket":251,"tier":220},[552,553,554,215,555,556],"finance","trading","market-analysis","typescript","cli",{"commitSha":253,"license":238},{"parentExtensionId":559,"repoId":560},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[215,556,552,554,553,555],{"evaluatedAt":563,"extractAt":564,"updatedAt":563},1778701108877,1778696691708,{"_creationTime":566,"_id":567,"community":568,"display":569,"identity":575,"providers":579,"relations":585,"tags":589,"workflow":590},1778693819389.531,"k174n8dznk7k8dr9drb7fwx01586nm5t",{"reviewCount":8},{"description":570,"installMethods":571,"name":573,"sourceUrl":574},"AI交易记忆的领域知识 — 结果加权记忆 (OWM) 架构、5种记忆类型、回忆评分和行为分析。用于记录交易、回忆相似的上下文、分析性能或检查行为漂移。在 \"record trade\"、\"remember trade\"、\"recall\"、\"similar trades\"、\"performance\"、\"behavioral\"、\"disposition\"、\"affective state\"、\"confidence\" 时触发。",{"claudeCode":572},"mnemox-ai/tradememory-protocol","trading-memory","https://github.com/mnemox-ai/tradememory-protocol",{"basePath":576,"githubOwner":577,"githubRepo":578,"locale":18,"slug":573,"type":246},"tradememory-plugin/skills/trading-memory","mnemox-ai","tradememory-protocol",{"evaluate":580,"extract":584},{"promptVersionExtension":208,"promptVersionScoring":209,"score":521,"tags":581,"targetMarket":251,"tier":220},[553,215,582,552,583],"memory","python",{"commitSha":253},{"parentExtensionId":586,"repoId":587,"translatedFrom":588},"k170vxkqee48k2xq1v55a025nh86nzn7","kd73z11kfekksxyrs8ds0snacs86ncdy","k173a67a16bpq0e29wjd85v71986nx03",[215,552,582,583,553],{"evaluatedAt":591,"extractAt":592,"updatedAt":593},1778693719816,1778693539593,1778693819389,{"_creationTime":595,"_id":596,"community":597,"display":598,"identity":602,"providers":605,"relations":612,"tags":614,"workflow":615},1778693805112.8403,"k177f7s31ysk6nw1qw3sak1r3186n795",{"reviewCount":8},{"description":599,"installMethods":600,"name":601,"sourceUrl":574},"Evolution Engine 的领域知识 — 支持 LLM 从原始 OHLCV 数据中自主发现策略。涵盖生成-回测-选择-进化循环、向量化回测、样本外验证和策略梯度。在发现交易模式、运行回测、进化策略或审查进化日志时使用。由“evolve”、“discover patterns”、“backtest”、“evolution”、“strategy generation”、“candidate strategy”触发。",{"claudeCode":572},"TradeMemory Protocol",{"basePath":603,"githubOwner":577,"githubRepo":578,"locale":18,"slug":604,"type":246},"tradememory-plugin/skills/evolution-engine","evolution-engine",{"evaluate":606,"extract":611},{"promptVersionExtension":208,"promptVersionScoring":209,"score":521,"tags":607,"targetMarket":251,"tier":220},[553,215,582,608,609,610],"audit","compliance","llm",{"commitSha":253,"license":238},{"parentExtensionId":586,"repoId":587,"translatedFrom":613},"k171p5pgbfbm5g4k5sa3y4cj9s86m6hk",[215,608,609,610,582,553],{"evaluatedAt":616,"extractAt":592,"updatedAt":617},1778693678813,1778693805112,{"_creationTime":619,"_id":620,"community":621,"display":622,"identity":628,"providers":633,"relations":641,"tags":644,"workflow":645},1778691104676.0042,"k17c25w174y6873nhdh566etts86mv7m",{"reviewCount":8},{"description":623,"installMethods":624,"name":626,"sourceUrl":627},"Transform images with resize, crop, smart crop, upscale, remove background, and 20+ operations.",{"claudeCode":625},"iterationlayer/skills","Image Transformation API","https://github.com/iterationlayer/skills",{"basePath":629,"githubOwner":630,"githubRepo":631,"locale":340,"slug":632,"type":246},"skills/image-transformation-api","iterationlayer","skills","image-transformation-api",{"evaluate":634,"extract":640},{"promptVersionExtension":208,"promptVersionScoring":209,"score":521,"tags":635,"targetMarket":251,"tier":220},[636,637,638,639,215],"image","transformation","editing","api",{"commitSha":253,"license":238},{"parentExtensionId":642,"repoId":643},"k1721s0xmp59902ybtpakrrffn86n10s","kd76p4g2qmtrkgx99cnab3683d86n4g8",[215,639,638,636,637],{"evaluatedAt":646,"extractAt":647,"updatedAt":646},1778693613399,1778691104676,{"_creationTime":649,"_id":650,"community":651,"display":652,"identity":658,"providers":662,"relations":670,"tags":673,"workflow":674},1778693180473.0972,"k1716aj3p8agwq6vwvn5n19v8n86mps9",{"reviewCount":8},{"description":653,"installMethods":654,"name":656,"sourceUrl":657},"Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models. Use for invoice processing, receipt extraction, ID document analysis, and custom document models. Triggers: \"Document Intelligence\", \"DocumentIntelligenceClient\", \"form recognizer\", \"invoice extraction\", \"receipt OCR\", \"document analysis .NET\".\n",{"claudeCode":655},"microsoft/agent-skills","azure-ai-document-intelligence-dotnet","https://github.com/microsoft/agent-skills",{"basePath":659,"githubOwner":660,"githubRepo":661,"locale":340,"slug":656,"type":246},".github/plugins/azure-sdk-dotnet/skills/azure-ai-document-intelligence-dotnet","microsoft","agent-skills",{"evaluate":663,"extract":669},{"promptVersionExtension":208,"promptVersionScoring":209,"score":521,"tags":664,"targetMarket":251,"tier":220},[665,215,666,667,668],"azure","document-intelligence","dotnet","sdk",{"commitSha":253},{"parentExtensionId":671,"repoId":672},"k1795g6t3v2fg9whacs7xkm88186nxr1","kd77czgnv00rfjm815pcc5xx5986n5t8",[215,665,666,667,668],{"evaluatedAt":675,"extractAt":676,"updatedAt":675},1778693591440,1778693180473]