[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-lllllllama-run-train-zh-CN":3,"guides-for-lllllllama-run-train":503,"similar-k17503d8expsj5cqt4c9mg6hvs86mg50-zh-CN":504},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":242,"isFallback":227,"parentExtension":247,"providers":248,"relations":254,"repo":257,"tags":499,"workflow":500},1778692736567.7776,"k17503d8expsj5cqt4c9mg6hvs86mg50",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"用于深度学习研究代码库的可信通道训练执行技能。在文档化或选定的训练命令应被保守运行时使用，以进行启动验证、短时运行验证、全面启动或恢复，并将状态、检查点和指标捕获写入标准化的 `train_outputs/`。请勿用于环境设置、探索性扫描、投机性想法实现或端到端编排。",{"claudeCode":12},"lllllllama/ai-paper-reproduction-skill","run-train","https://github.com/lllllllama/ai-paper-reproduction-skill",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":225,"workflow":240},1778692736567.7778,"kn7a935af794509xfab4vgy7q986mtyn","zh-CN",{"checks":20,"evaluatedAt":193,"extensionSummary":194,"features":195,"nonGoals":201,"promptVersionExtension":206,"promptVersionScoring":207,"purpose":208,"rationale":209,"score":210,"summary":211,"tags":212,"tier":219,"useCases":220},[21,26,29,32,36,39,44,48,51,54,58,62,65,69,72,75,78,81,84,87,91,95,99,104,108,111,115,118,122,125,128,131,134,137,140,144,148,151,154,158,161,164,167,170,174,177,180,183,186,190],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","描述清楚地指出了一个具体问题：在研究代码库中保守地运行深度学习训练命令以进行验证和状态捕获。",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling 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进行参数验证，确保了基本的输入完整性。",{"category":66,"check":109,"severity":24,"summary":110},"Unguarded Destructive Operations","该脚本的主要功能是执行用户提供的命令，但它本身不会在没有用户调用的情况下执行破坏性操作。",{"category":112,"check":113,"severity":24,"summary":114},"Code Execution","Error Handling","该脚本能很好地处理文件未找到错误、超时和非零退出码，并在输出 JSON 中报告它们。",{"category":112,"check":116,"severity":24,"summary":117},"Logging","该脚本将结构化的 JSON 负载（包含执行详细信息和日志）输出到 stdout，作为审计记录。",{"category":119,"check":120,"severity":42,"summary":121},"Compliance","GDPR","该技能仅执行命令和捕获输出；它不处理个人数据。",{"category":119,"check":123,"severity":24,"summary":124},"Target market","该技能是一个通用的训练执行工具，没有区域或管辖权逻辑，使其具有全球适用性。",{"category":92,"check":126,"severity":24,"summary":127},"Runtime stability","该脚本使用了标准的 Python 库和实践，确保了在具有 Python 3 的系统上的跨平台兼容性。",{"category":45,"check":129,"severity":24,"summary":130},"README","代码库的 README 文件提供了关于这些技能的详细信息，包括安装和目的，并且特定技能的 SKILL.md 文件清晰明了。",{"category":33,"check":132,"severity":42,"summary":133},"Tool surface 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Use when the user wants an end-to-end, minimal-trustworthy reproduction flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, or broad research assistance outside repository-grounded reproduction.","ai-research-reproduction",{"claudeCode":12},"SKILL.md frontmatter at skills/ai-research-reproduction/SKILL.md",[348,349,351,353,355,357,359,361,363,365,367,369],{"path":271,"priority":272},{"path":350,"priority":288},"assets/COMMANDS.template.md",{"path":352,"priority":288},"assets/LOG.template.md",{"path":354,"priority":288},"assets/PATCHES.template.md",{"path":356,"priority":288},"assets/SUMMARY.template.md",{"path":358,"priority":288},"assets/status.template.json",{"path":360,"priority":275},"references/architecture.md",{"path":362,"priority":275},"references/language-policy.md",{"path":364,"priority":275},"references/output-spec.md",{"path":366,"priority":275},"references/patch-policy.md",{"path":368,"priority":275},"references/research-safety-principles.md",{"path":370,"priority":288},"scripts/orchestrate_repro.py",{"basePath":372,"description":373,"displayName":374,"installMethods":375,"rationale":376,"selectedPaths":377,"source":339,"sourceLanguage":340,"type":246},"skills/analyze-project","Trusted-lane analysis skill for deep learning research repositories. Use when the user wants to read and understand a repository, inspect model structure and training or inference entrypoints, review configs and insertion points, or flag suspicious implementation patterns without modifying code or running heavy jobs. Do not use for active command execution, broad refactoring, speculative code adaptation, or automatic bug fixing.","analyze-project",{"claudeCode":12},"SKILL.md frontmatter at skills/analyze-project/SKILL.md",[378,379,381],{"path":271,"priority":272},{"path":380,"priority":275},"references/analysis-policy.md",{"path":382,"priority":288},"scripts/analyze_project.py",{"basePath":384,"description":385,"displayName":386,"installMethods":387,"rationale":388,"selectedPaths":389,"source":339,"sourceLanguage":340,"type":246},"skills/env-and-assets-bootstrap","Environment and assets sub-skill for README-first AI repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.","env-and-assets-bootstrap",{"claudeCode":12},"SKILL.md frontmatter at skills/env-and-assets-bootstrap/SKILL.md",[390,391,393,395,397,399,401],{"path":271,"priority":272},{"path":392,"priority":275},"references/assets-policy.md",{"path":394,"priority":275},"references/env-policy.md",{"path":396,"priority":288},"scripts/bootstrap_env.py",{"path":398,"priority":288},"scripts/bootstrap_env.sh",{"path":400,"priority":288},"scripts/plan_setup.py",{"path":402,"priority":288},"scripts/prepare_assets.py",{"basePath":404,"description":405,"displayName":406,"installMethods":407,"rationale":408,"selectedPaths":409,"source":339,"sourceLanguage":340,"type":246},"skills/explore-code","Explore-lane code adaptation skill for deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together low-risk migration ideas with summary-only records in `explore_outputs/`. 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":443,"description":444,"displayName":445,"installMethods":446,"rationale":447,"selectedPaths":448,"source":339,"sourceLanguage":340,"type":246},"skills/paper-context-resolver","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.","paper-context-resolver",{"claudeCode":12},"SKILL.md frontmatter at skills/paper-context-resolver/SKILL.md",[449,450],{"path":271,"priority":272},{"path":451,"priority":275},"references/paper-assisted-reproduction.md",{"basePath":453,"description":454,"displayName":455,"installMethods":456,"rationale":457,"selectedPaths":458,"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",[459,460,462,464],{"path":271,"priority":272},{"path":461,"priority":275},"references/repo-scan-rules.md",{"path":463,"priority":288},"scripts/extract_commands.py",{"path":465,"priority":288},"scripts/scan_repo.py",{"basePath":243,"description":467,"displayName":13,"installMethods":468,"rationale":469,"selectedPaths":470,"source":339,"sourceLanguage":340,"type":246},"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.",{"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,[213,217,218,215,214,216],{"evaluatedAt":501,"extractAt":502,"updatedAt":241},1778692620717,1778692396032,[],[505,536,565,592,622,648],{"_creationTime":506,"_id":507,"community":508,"display":509,"identity":515,"providers":519,"relations":529,"tags":532,"workflow":533},1778693180473.1174,"k17fm8t65dw1y28823kj8ce3bn86mgqg",{"reviewCount":8},{"description":510,"installMethods":511,"name":513,"sourceUrl":514},"Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.\nTriggers: \"azure-monitor-query\", \"LogsQueryClient\", \"MetricsQueryClient\", \"Log Analytics\", \"Kusto queries\", \"Azure metrics\".\n",{"claudeCode":512},"microsoft/agent-skills","azure-monitor-query-py","https://github.com/microsoft/agent-skills",{"basePath":516,"githubOwner":517,"githubRepo":518,"locale":340,"slug":513,"type":246},".github/plugins/azure-sdk-python/skills/azure-monitor-query-py","microsoft","agent-skills",{"evaluate":520,"extract":528},{"promptVersionExtension":206,"promptVersionScoring":207,"score":521,"tags":522,"targetMarket":251,"tier":527},100,[523,217,524,525,526,218],"azure","logs","metrics","sdk","verified",{"commitSha":253},{"parentExtensionId":530,"repoId":531},"k171mfx6atvhq1bkhpky84v4b186n9qd","kd77czgnv00rfjm815pcc5xx5986n5t8",[523,524,525,217,218,526],{"evaluatedAt":534,"extractAt":535,"updatedAt":534},1778695102758,1778693180473,{"_creationTime":537,"_id":538,"community":539,"display":540,"identity":546,"providers":550,"relations":558,"tags":561,"workflow":562},1778695116697.1838,"k17c6fx43mgkj95s4yzww50w5s86nb65",{"reviewCount":8},{"description":541,"installMethods":542,"name":544,"sourceUrl":545},"High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.",{"claudeCode":543},"Orchestra-Research/AI-Research-SKILLs","pytorch-lightning","https://github.com/Orchestra-Research/AI-Research-SKILLs",{"basePath":547,"githubOwner":548,"githubRepo":549,"locale":340,"slug":544,"type":246},"08-distributed-training/pytorch-lightning","Orchestra-Research","AI-Research-SKILLs",{"evaluate":551,"extract":557},{"promptVersionExtension":206,"promptVersionScoring":207,"score":210,"tags":552,"targetMarket":251,"tier":527},[553,554,214,555,556,213],"pytorch","lightning","distributed-training","mlops",{"commitSha":253},{"parentExtensionId":559,"repoId":560},"k17155ws9qc0hw7a568bg79sfd86max8","kd70hj1y80mhra5xm5g188j5n586mg18",[213,555,554,556,553,214],{"evaluatedAt":563,"extractAt":564,"updatedAt":563},1778696329359,1778695116697,{"_creationTime":566,"_id":567,"community":568,"display":569,"identity":575,"providers":579,"relations":586,"tags":588,"workflow":589},1778691799740.4905,"k17c27dcgjsqmxeggb19stv4xn86mf1z",{"reviewCount":8},{"description":570,"installMethods":571,"name":573,"sourceUrl":574},"Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.",{"claudeCode":572},"K-Dense-AI/claude-scientific-skills","PyTorch Lightning","https://github.com/K-Dense-AI/claude-scientific-skills",{"basePath":576,"githubOwner":577,"githubRepo":578,"locale":340,"slug":544,"type":246},"scientific-skills/pytorch-lightning","K-Dense-AI","claude-scientific-skills",{"evaluate":580,"extract":584},{"promptVersionExtension":206,"promptVersionScoring":207,"score":521,"tags":581,"targetMarket":251,"tier":527},[553,213,582,218,583],"machine-learning","framework",{"commitSha":253,"license":585},"Apache-2.0",{"repoId":587},"kd79rphh5gexy91xmpxc05h5mh86mm9r",[213,583,582,218,553],{"evaluatedAt":590,"extractAt":591,"updatedAt":590},1778693958717,1778691799740,{"_creationTime":593,"_id":594,"community":595,"display":596,"identity":602,"providers":606,"relations":615,"tags":618,"workflow":619},1778695548458.3782,"k17a4rtftm1z500gdcksks32wx86n9p2",{"reviewCount":8},{"description":597,"installMethods":598,"name":600,"sourceUrl":601},"Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.\n",{"claudeCode":599},"pjt222/agent-almanac","monitor-data-integrity","https://github.com/pjt222/agent-almanac",{"basePath":603,"githubOwner":604,"githubRepo":605,"locale":340,"slug":600,"type":246},"skills/monitor-data-integrity","pjt222","agent-almanac",{"evaluate":607,"extract":614},{"promptVersionExtension":206,"promptVersionScoring":207,"score":521,"tags":608,"targetMarket":251,"tier":527},[609,610,611,612,217,613],"compliance","gxp","data-integrity","alcoa","anomaly-detection",{"commitSha":253},{"parentExtensionId":616,"repoId":617},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[612,613,609,611,610,217],{"evaluatedAt":620,"extractAt":621,"updatedAt":620},1778699562914,1778695548458,{"_creationTime":623,"_id":624,"community":625,"display":626,"identity":632,"providers":635,"relations":642,"tags":644,"workflow":645},1778694578248.1062,"k17e56dzsqh7qked458bjbs0e586n21y",{"reviewCount":8},{"description":627,"installMethods":628,"name":630,"sourceUrl":631},"Query Netdata Cloud via its REST API -- metrics, logs (systemd-journal / windows-events / otel-logs), topology graphs (topology:snmp), network flows (flows:netflow), alerts, dynamic configuration (DynCfg), and generic Functions on a node. Use when the user asks about querying Netdata Cloud, fetching metrics from the cloud, querying logs / topology / netflow / sflow / ipfix through Cloud, listing or modifying configurations via DynCfg, calling agent Functions through Cloud, listing spaces/rooms/nodes, or building a curl command against `app.netdata.cloud`. Pairs with the `query-netdata-agents` skill when direct-agent access is needed.",{"claudeCode":629},"netdata/netdata","query-netdata-cloud","https://github.com/netdata/netdata",{"basePath":633,"githubOwner":634,"githubRepo":634,"locale":340,"slug":630,"type":246},"docs/netdata-ai/skills/query-netdata-cloud","netdata",{"evaluate":636,"extract":641},{"promptVersionExtension":206,"promptVersionScoring":207,"score":521,"tags":637,"targetMarket":251,"tier":527},[634,638,217,525,524,639,640],"api","topology","rest",{"commitSha":253},{"repoId":643},"kd70yp91ybn40a638h3hzz6nbd86m2cw",[638,524,525,217,634,640,639],{"evaluatedAt":646,"extractAt":647,"updatedAt":646},1778694825298,1778694578248,{"_creationTime":649,"_id":650,"community":651,"display":652,"identity":658,"providers":662,"relations":670,"tags":672,"workflow":673},1778694240519.7402,"k172jnxq28h65x6zn1p19r731586md2x",{"reviewCount":8},{"description":653,"installMethods":654,"name":656,"sourceUrl":657},"Track skill performance and emerging patterns",{"claudeCode":655},"mshadmanrahman/pm-pilot","meta-observer","https://github.com/mshadmanrahman/pm-pilot",{"basePath":659,"githubOwner":660,"githubRepo":661,"locale":340,"slug":656,"type":246},"skills/productivity/meta-observer","mshadmanrahman","pm-pilot",{"evaluate":663,"extract":669},{"promptVersionExtension":206,"promptVersionScoring":207,"score":521,"tags":664,"targetMarket":251,"tier":527},[217,665,666,667,668],"analytics","productivity","logging","skills",{"commitSha":253},{"repoId":671},"kd728wqst6vwd95ymycxb97nrx86mnsn",[665,667,217,666,668],{"evaluatedAt":674,"extractAt":675,"updatedAt":674},1778694605108,1778694240519]