[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-mlflow-sagemaker-mlflow":3,"mdc--n3x17y-key":33,"related-org-mlflow-sagemaker-mlflow":506,"related-repo-mlflow-sagemaker-mlflow":667},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":31,"mdContent":32},"sagemaker-mlflow","connect to SageMaker Managed MLflow","Connect to SageMaker Managed MLflow (mlflow-app or mlflow-tracking-server ARN) as an MLflow backend, then hand off to the other MLflow skills. Triggers on a SageMaker MLflow ARN (arn:aws:sagemaker:...:mlflow-app\u002F... or arn:aws:sagemaker:...:mlflow-tracking-server\u002F...) or \"SageMaker Managed MLflow\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"mlflow","MLflow","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fmlflow.png",[12,14,17,20],{"name":9,"slug":8,"type":13},"tag",{"name":15,"slug":16,"type":13},"MLOps","mlops",{"name":18,"slug":19,"type":13},"AI Infrastructure","ai-infrastructure",{"name":21,"slug":22,"type":13},"AWS","aws",60,"https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fskills","2026-07-14T05:39:05.401801",null,19,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":26},[],"https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fskills\u002Ftree\u002FHEAD\u002Fsagemaker-mlflow","---\nname: sagemaker-mlflow\ndescription: >\n  Connect to SageMaker Managed MLflow (mlflow-app or mlflow-tracking-server ARN) as an\n  MLflow backend, then hand off to the other MLflow skills. Triggers on a SageMaker MLflow\n  ARN (arn:aws:sagemaker:...:mlflow-app\u002F... or arn:aws:sagemaker:...:mlflow-tracking-server\u002F...)\n  or \"SageMaker Managed MLflow\".\n---\n\n# Connect to SageMaker MLflow\n\nEstablishes the connection to SageMaker Managed MLflow (the `sagemaker-mlflow` plugin + an\nARN tracking URI + AWS credentials), then hands off to the requested MLflow skill\n(instrumenting-with-mlflow-tracing, agent-evaluation, retrieving-mlflow-traces,\nquerying-mlflow-metrics, etc.), which inherit the connection via `MLFLOW_TRACKING_URI`.\n\nRequires **Python >= 3.10** (mlflow >= 3.8). Validate and report the fix — never\nauto-install the plugin or acquire credentials (mirrors the repo's Databricks pattern).\n\n## Step 1: Preconditions\n- `python --version` -> must be 3.10+. If older, STOP and tell the user.\n- Confirm AWS credentials resolve (env vars, `aws configure` \u002F SSO, or an IAM role). If\n  they error (ExpiredToken \u002F no creds), STOP and ask the user to configure them.\n  (`scripts\u002Fverify_connection.py` also checks this, via boto3.)\n\n## Step 2: Install the plugin\n```bash\npip install sagemaker-mlflow\n```\nRequired even if `MLFLOW_TRACKING_URI` is already an ARN — without it,\n`mlflow.set_tracking_uri()` fails with `UnsupportedModelRegistryStoreURIException`.\n\n## Step 3: Select the resource ARN\n- **If the user already provided an ARN** (or `MLFLOW_TRACKING_URI` is set to\n  `arn:aws:sagemaker:...`), use it as-is.\n- **Otherwise, discover it** in the user's region:\n  ```bash\n  python scripts\u002Fdiscover_arns.py\n  ```\n  If exactly one is returned, use it; if several, list name\u002Fstatus\u002FARN and ask the user\n  which to use. Both `mlflow-app` and `mlflow-tracking-server` ARNs work; the ARN is also\n  shown on the resource's detail page in the SageMaker Studio console.\n\nThen export it (the ARN is region-bound — a cross-region ARN will not authenticate):\n```bash\nexport MLFLOW_TRACKING_URI=\"\u003Carn>\"    # mlflow-app\u002F\u003Cid> or mlflow-tracking-server\u002F\u003Cname>\nexport AWS_DEFAULT_REGION=\"\u003Cregion>\"  # AWS credentials resolved via the default chain (SigV4)\n```\n\n## Step 4: Verify\n```bash\npython scripts\u002Fverify_connection.py \"$MLFLOW_TRACKING_URI\"\n```\nChecks all three prerequisites (plugin \u002F credentials \u002F ARN connectivity) and exits\nnon-zero with a remediation hint on the first failure. Do not proceed until it passes.\n\nAfter the connection verifies, hand off to the requested MLflow skill — it inherits the\nconnected environment via `MLFLOW_TRACKING_URI`.\n\n## Troubleshooting\n- `UnsupportedModelRegistryStoreURIException` — the `sagemaker-mlflow` plugin is not\n  installed; `pip install sagemaker-mlflow`, then re-run `scripts\u002Fverify_connection.py`.\n- `AccessDenied` \u002F `403` — credentials are missing\u002Fexpired, or the principal lacks MLflow\n  permissions on the ARN. Check that credentials resolve, grant `sagemaker-mlflow` \u002F\n  `sagemaker` permissions, and confirm the ARN region matches `AWS_DEFAULT_REGION`.\n",{"data":34,"body":35},{"name":4,"description":6},{"type":36,"children":37},"root",[38,47,69,82,89,126,132,166,193,199,275,280,363,369,403,408,419,425,500],{"type":39,"tag":40,"props":41,"children":43},"element","h1",{"id":42},"connect-to-sagemaker-mlflow",[44],{"type":45,"value":46},"text","Connect to SageMaker MLflow",{"type":39,"tag":48,"props":49,"children":50},"p",{},[51,53,59,61,67],{"type":45,"value":52},"Establishes the connection to SageMaker Managed MLflow (the ",{"type":39,"tag":54,"props":55,"children":57},"code",{"className":56},[],[58],{"type":45,"value":4},{"type":45,"value":60}," plugin + an\nARN tracking URI + AWS credentials), then hands off to the requested MLflow skill\n(instrumenting-with-mlflow-tracing, agent-evaluation, retrieving-mlflow-traces,\nquerying-mlflow-metrics, etc.), which inherit the connection via ",{"type":39,"tag":54,"props":62,"children":64},{"className":63},[],[65],{"type":45,"value":66},"MLFLOW_TRACKING_URI",{"type":45,"value":68},".",{"type":39,"tag":48,"props":70,"children":71},{},[72,74,80],{"type":45,"value":73},"Requires ",{"type":39,"tag":75,"props":76,"children":77},"strong",{},[78],{"type":45,"value":79},"Python >= 3.10",{"type":45,"value":81}," (mlflow >= 3.8). Validate and report the fix — never\nauto-install the plugin or acquire credentials (mirrors the repo's Databricks pattern).",{"type":39,"tag":83,"props":84,"children":86},"h2",{"id":85},"step-1-preconditions",[87],{"type":45,"value":88},"Step 1: Preconditions",{"type":39,"tag":90,"props":91,"children":92},"ul",{},[93,105],{"type":39,"tag":94,"props":95,"children":96},"li",{},[97,103],{"type":39,"tag":54,"props":98,"children":100},{"className":99},[],[101],{"type":45,"value":102},"python --version",{"type":45,"value":104}," -> must be 3.10+. If older, STOP and tell the user.",{"type":39,"tag":94,"props":106,"children":107},{},[108,110,116,118,124],{"type":45,"value":109},"Confirm AWS credentials resolve (env vars, ",{"type":39,"tag":54,"props":111,"children":113},{"className":112},[],[114],{"type":45,"value":115},"aws configure",{"type":45,"value":117}," \u002F SSO, or an IAM role). If\nthey error (ExpiredToken \u002F no creds), STOP and ask the user to configure them.\n(",{"type":39,"tag":54,"props":119,"children":121},{"className":120},[],[122],{"type":45,"value":123},"scripts\u002Fverify_connection.py",{"type":45,"value":125}," also checks this, via boto3.)",{"type":39,"tag":83,"props":127,"children":129},{"id":128},"step-2-install-the-plugin",[130],{"type":45,"value":131},"Step 2: Install the plugin",{"type":39,"tag":133,"props":134,"children":139},"pre",{"className":135,"code":136,"language":137,"meta":138,"style":138},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","pip install sagemaker-mlflow\n","bash","",[140],{"type":39,"tag":54,"props":141,"children":142},{"__ignoreMap":138},[143],{"type":39,"tag":144,"props":145,"children":148},"span",{"class":146,"line":147},"line",1,[149,155,161],{"type":39,"tag":144,"props":150,"children":152},{"style":151},"--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B",[153],{"type":45,"value":154},"pip",{"type":39,"tag":144,"props":156,"children":158},{"style":157},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[159],{"type":45,"value":160}," install",{"type":39,"tag":144,"props":162,"children":163},{"style":157},[164],{"type":45,"value":165}," sagemaker-mlflow\n",{"type":39,"tag":48,"props":167,"children":168},{},[169,171,176,178,184,186,192],{"type":45,"value":170},"Required even if ",{"type":39,"tag":54,"props":172,"children":174},{"className":173},[],[175],{"type":45,"value":66},{"type":45,"value":177}," is already an ARN — without it,\n",{"type":39,"tag":54,"props":179,"children":181},{"className":180},[],[182],{"type":45,"value":183},"mlflow.set_tracking_uri()",{"type":45,"value":185}," fails with ",{"type":39,"tag":54,"props":187,"children":189},{"className":188},[],[190],{"type":45,"value":191},"UnsupportedModelRegistryStoreURIException",{"type":45,"value":68},{"type":39,"tag":83,"props":194,"children":196},{"id":195},"step-3-select-the-resource-arn",[197],{"type":45,"value":198},"Step 3: Select the resource ARN",{"type":39,"tag":90,"props":200,"children":201},{},[202,227],{"type":39,"tag":94,"props":203,"children":204},{},[205,210,212,217,219,225],{"type":39,"tag":75,"props":206,"children":207},{},[208],{"type":45,"value":209},"If the user already provided an ARN",{"type":45,"value":211}," (or ",{"type":39,"tag":54,"props":213,"children":215},{"className":214},[],[216],{"type":45,"value":66},{"type":45,"value":218}," is set to\n",{"type":39,"tag":54,"props":220,"children":222},{"className":221},[],[223],{"type":45,"value":224},"arn:aws:sagemaker:...",{"type":45,"value":226},"), use it as-is.",{"type":39,"tag":94,"props":228,"children":229},{},[230,235,237,257,259,265,267,273],{"type":39,"tag":75,"props":231,"children":232},{},[233],{"type":45,"value":234},"Otherwise, discover it",{"type":45,"value":236}," in the user's region:\n",{"type":39,"tag":133,"props":238,"children":240},{"className":135,"code":239,"language":137,"meta":138,"style":138},"python scripts\u002Fdiscover_arns.py\n",[241],{"type":39,"tag":54,"props":242,"children":243},{"__ignoreMap":138},[244],{"type":39,"tag":144,"props":245,"children":246},{"class":146,"line":147},[247,252],{"type":39,"tag":144,"props":248,"children":249},{"style":151},[250],{"type":45,"value":251},"python",{"type":39,"tag":144,"props":253,"children":254},{"style":157},[255],{"type":45,"value":256}," scripts\u002Fdiscover_arns.py\n",{"type":45,"value":258},"\nIf exactly one is returned, use it; if several, list name\u002Fstatus\u002FARN and ask the user\nwhich to use. Both ",{"type":39,"tag":54,"props":260,"children":262},{"className":261},[],[263],{"type":45,"value":264},"mlflow-app",{"type":45,"value":266}," and ",{"type":39,"tag":54,"props":268,"children":270},{"className":269},[],[271],{"type":45,"value":272},"mlflow-tracking-server",{"type":45,"value":274}," ARNs work; the ARN is also\nshown on the resource's detail page in the SageMaker Studio console.",{"type":39,"tag":48,"props":276,"children":277},{},[278],{"type":45,"value":279},"Then export it (the ARN is region-bound — a cross-region ARN will not authenticate):",{"type":39,"tag":133,"props":281,"children":283},{"className":135,"code":282,"language":137,"meta":138,"style":138},"export MLFLOW_TRACKING_URI=\"\u003Carn>\"    # mlflow-app\u002F\u003Cid> or mlflow-tracking-server\u002F\u003Cname>\nexport AWS_DEFAULT_REGION=\"\u003Cregion>\"  # AWS credentials resolved via the default chain (SigV4)\n",[284],{"type":39,"tag":54,"props":285,"children":286},{"__ignoreMap":138},[287,328],{"type":39,"tag":144,"props":288,"children":289},{"class":146,"line":147},[290,296,302,308,313,318,322],{"type":39,"tag":144,"props":291,"children":293},{"style":292},"--shiki-light:#9C3EDA;--shiki-default:#C792EA;--shiki-dark:#C792EA",[294],{"type":45,"value":295},"export",{"type":39,"tag":144,"props":297,"children":299},{"style":298},"--shiki-light:#90A4AE;--shiki-default:#EEFFFF;--shiki-dark:#BABED8",[300],{"type":45,"value":301}," MLFLOW_TRACKING_URI",{"type":39,"tag":144,"props":303,"children":305},{"style":304},"--shiki-light:#39ADB5;--shiki-default:#89DDFF;--shiki-dark:#89DDFF",[306],{"type":45,"value":307},"=",{"type":39,"tag":144,"props":309,"children":310},{"style":304},[311],{"type":45,"value":312},"\"",{"type":39,"tag":144,"props":314,"children":315},{"style":157},[316],{"type":45,"value":317},"\u003Carn>",{"type":39,"tag":144,"props":319,"children":320},{"style":304},[321],{"type":45,"value":312},{"type":39,"tag":144,"props":323,"children":325},{"style":324},"--shiki-light:#90A4AE;--shiki-light-font-style:italic;--shiki-default:#546E7A;--shiki-default-font-style:italic;--shiki-dark:#676E95;--shiki-dark-font-style:italic",[326],{"type":45,"value":327},"    # mlflow-app\u002F\u003Cid> or mlflow-tracking-server\u002F\u003Cname>\n",{"type":39,"tag":144,"props":329,"children":331},{"class":146,"line":330},2,[332,336,341,345,349,354,358],{"type":39,"tag":144,"props":333,"children":334},{"style":292},[335],{"type":45,"value":295},{"type":39,"tag":144,"props":337,"children":338},{"style":298},[339],{"type":45,"value":340}," AWS_DEFAULT_REGION",{"type":39,"tag":144,"props":342,"children":343},{"style":304},[344],{"type":45,"value":307},{"type":39,"tag":144,"props":346,"children":347},{"style":304},[348],{"type":45,"value":312},{"type":39,"tag":144,"props":350,"children":351},{"style":157},[352],{"type":45,"value":353},"\u003Cregion>",{"type":39,"tag":144,"props":355,"children":356},{"style":304},[357],{"type":45,"value":312},{"type":39,"tag":144,"props":359,"children":360},{"style":324},[361],{"type":45,"value":362},"  # AWS credentials resolved via the default chain (SigV4)\n",{"type":39,"tag":83,"props":364,"children":366},{"id":365},"step-4-verify",[367],{"type":45,"value":368},"Step 4: Verify",{"type":39,"tag":133,"props":370,"children":372},{"className":135,"code":371,"language":137,"meta":138,"style":138},"python scripts\u002Fverify_connection.py \"$MLFLOW_TRACKING_URI\"\n",[373],{"type":39,"tag":54,"props":374,"children":375},{"__ignoreMap":138},[376],{"type":39,"tag":144,"props":377,"children":378},{"class":146,"line":147},[379,383,388,393,398],{"type":39,"tag":144,"props":380,"children":381},{"style":151},[382],{"type":45,"value":251},{"type":39,"tag":144,"props":384,"children":385},{"style":157},[386],{"type":45,"value":387}," scripts\u002Fverify_connection.py",{"type":39,"tag":144,"props":389,"children":390},{"style":304},[391],{"type":45,"value":392}," \"",{"type":39,"tag":144,"props":394,"children":395},{"style":298},[396],{"type":45,"value":397},"$MLFLOW_TRACKING_URI",{"type":39,"tag":144,"props":399,"children":400},{"style":304},[401],{"type":45,"value":402},"\"\n",{"type":39,"tag":48,"props":404,"children":405},{},[406],{"type":45,"value":407},"Checks all three prerequisites (plugin \u002F credentials \u002F ARN connectivity) and exits\nnon-zero with a remediation hint on the first failure. Do not proceed until it passes.",{"type":39,"tag":48,"props":409,"children":410},{},[411,413,418],{"type":45,"value":412},"After the connection verifies, hand off to the requested MLflow skill — it inherits the\nconnected environment via ",{"type":39,"tag":54,"props":414,"children":416},{"className":415},[],[417],{"type":45,"value":66},{"type":45,"value":68},{"type":39,"tag":83,"props":420,"children":422},{"id":421},"troubleshooting",[423],{"type":45,"value":424},"Troubleshooting",{"type":39,"tag":90,"props":426,"children":427},{},[428,459],{"type":39,"tag":94,"props":429,"children":430},{},[431,436,438,443,445,451,453,458],{"type":39,"tag":54,"props":432,"children":434},{"className":433},[],[435],{"type":45,"value":191},{"type":45,"value":437}," — the ",{"type":39,"tag":54,"props":439,"children":441},{"className":440},[],[442],{"type":45,"value":4},{"type":45,"value":444}," plugin is not\ninstalled; ",{"type":39,"tag":54,"props":446,"children":448},{"className":447},[],[449],{"type":45,"value":450},"pip install sagemaker-mlflow",{"type":45,"value":452},", then re-run ",{"type":39,"tag":54,"props":454,"children":456},{"className":455},[],[457],{"type":45,"value":123},{"type":45,"value":68},{"type":39,"tag":94,"props":460,"children":461},{},[462,468,470,476,478,483,485,491,493,499],{"type":39,"tag":54,"props":463,"children":465},{"className":464},[],[466],{"type":45,"value":467},"AccessDenied",{"type":45,"value":469}," \u002F ",{"type":39,"tag":54,"props":471,"children":473},{"className":472},[],[474],{"type":45,"value":475},"403",{"type":45,"value":477}," — credentials are missing\u002Fexpired, or the principal lacks MLflow\npermissions on the ARN. Check that credentials resolve, grant ",{"type":39,"tag":54,"props":479,"children":481},{"className":480},[],[482],{"type":45,"value":4},{"type":45,"value":484}," \u002F\n",{"type":39,"tag":54,"props":486,"children":488},{"className":487},[],[489],{"type":45,"value":490},"sagemaker",{"type":45,"value":492}," permissions, and confirm the ARN region matches ",{"type":39,"tag":54,"props":494,"children":496},{"className":495},[],[497],{"type":45,"value":498},"AWS_DEFAULT_REGION",{"type":45,"value":68},{"type":39,"tag":501,"props":502,"children":503},"style",{},[504],{"type":45,"value":505},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":507,"total":666},[508,524,534,551,566,577,593,610,621,633,648,659],{"slug":509,"name":509,"fn":510,"description":511,"org":512,"tags":513,"stars":521,"repoUrl":522,"updatedAt":523},"setup","configure MLflow tracing","Configure MLflow tracing for Claude Code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[514,517,518],{"name":515,"slug":516,"type":13},"Claude Code","claude-code",{"name":9,"slug":8,"type":13},{"name":519,"slug":520,"type":13},"Tracing","tracing",27014,"https:\u002F\u002Fgithub.com\u002Fmlflow\u002Fmlflow","2026-07-14T05:39:00.297769",{"slug":525,"name":525,"fn":526,"description":527,"org":528,"tags":529,"stars":521,"repoUrl":522,"updatedAt":533},"status","display MLflow tracing configuration","Show the current MLflow tracing configuration for Claude Code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[530,531,532],{"name":515,"slug":516,"type":13},{"name":9,"slug":8,"type":13},{"name":519,"slug":520,"type":13},"2026-07-14T05:39:01.540537",{"slug":535,"name":535,"fn":536,"description":537,"org":538,"tags":539,"stars":23,"repoUrl":24,"updatedAt":550},"agent-evaluation","evaluate and optimize LLM agent output","Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong\u002Fincomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. IMPORTANT - Always also load the instrumenting-with-mlflow-tracing skill before starting any work. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[540,543,546,547],{"name":541,"slug":542,"type":13},"Agents","agents",{"name":544,"slug":545,"type":13},"Evals","evals",{"name":9,"slug":8,"type":13},{"name":548,"slug":549,"type":13},"Performance","performance","2026-07-14T05:39:15.600492",{"slug":552,"name":552,"fn":553,"description":554,"org":555,"tags":556,"stars":23,"repoUrl":24,"updatedAt":565},"analyzing-mlflow-session","analyze MLflow chat conversation sessions","Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns. Triggers on \"analyze this session\", \"what happened in this conversation\", \"debug session\", \"review chat history\", \"where did this chat go wrong\", \"session traces\", \"analyze chat\", \"debug this chat\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[557,560,561,564],{"name":558,"slug":559,"type":13},"Debugging","debugging",{"name":9,"slug":8,"type":13},{"name":562,"slug":563,"type":13},"Observability","observability",{"name":519,"slug":520,"type":13},"2026-07-14T05:39:10.542342",{"slug":567,"name":567,"fn":568,"description":569,"org":570,"tags":571,"stars":23,"repoUrl":24,"updatedAt":576},"analyzing-mlflow-trace","analyze MLflow traces","Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on \"analyze this trace\", \"what went wrong with this trace\", \"debug trace\", \"investigate trace\", \"why did this trace fail\", \"root cause this trace\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[572,573,574,575],{"name":558,"slug":559,"type":13},{"name":9,"slug":8,"type":13},{"name":562,"slug":563,"type":13},{"name":519,"slug":520,"type":13},"2026-07-14T05:39:02.874441",{"slug":578,"name":578,"fn":579,"description":580,"org":581,"tags":582,"stars":23,"repoUrl":24,"updatedAt":592},"fix-agent-issue","fix and update AI agent behavior","Drives a disciplined explore → plan → implement → verify loop for changing an AI agent's behavior with confidence — whether fixing a reported failure or introducing a new requirement, business rule, or policy. Grounds the diagnosis in MLflow traces, codifies the desired behavior as a regression test suite (`mlflow.genai.evaluate` assertions in `@mlflow.test` pytest tests), and iterates the agent — not the test — until green, resisting quick system-prompt patches when the real fix is upstream (missing tool, retrieval source, or capability). Use whenever the user wants to fix or change how an agent behaves — e.g. \"fix this issue in my agent\", \"this answer is wrong\", \"the agent is hallucinating\", \"improve my agent based on this trace\", \"make the agent do X instead of Y\", \"I want the agent to lead with\u002Fprioritize\u002Frecommend X\", \"new business rule: the agent should X\", \"always\u002Fnever do X\", \"change the agent's default behavior\" — or shares a trace they want addressed.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[583,584,587,588,591],{"name":541,"slug":542,"type":13},{"name":585,"slug":586,"type":13},"Best Practices","best-practices",{"name":558,"slug":559,"type":13},{"name":589,"slug":590,"type":13},"Engineering","engineering",{"name":9,"slug":8,"type":13},"2026-07-30T05:53:39.749464",{"slug":594,"name":594,"fn":595,"description":596,"org":597,"tags":598,"stars":23,"repoUrl":24,"updatedAt":609},"instrumenting-with-mlflow-tracing","instrument Python and TypeScript with MLflow Tracing","Instruments Python and TypeScript code with MLflow Tracing for observability. Must be loaded when setting up tracing as part of any workflow including agent evaluation. Triggers on adding tracing, instrumenting agents\u002FLLM apps, getting started with MLflow tracing, tracing specific frameworks (LangGraph, LangChain, OpenAI, Gemini, DSPy, CrewAI, AutoGen), or when another skill references tracing setup. Examples - \"How do I add tracing?\", \"Instrument my agent\", \"Trace my LangChain app\", \"Set up tracing for evaluation\"",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[599,600,601,602,603,605,606],{"name":541,"slug":542,"type":13},{"name":544,"slug":545,"type":13},{"name":9,"slug":8,"type":13},{"name":562,"slug":563,"type":13},{"name":604,"slug":251,"type":13},"Python",{"name":519,"slug":520,"type":13},{"name":607,"slug":608,"type":13},"TypeScript","typescript","2026-07-20T05:58:52.968218",{"slug":611,"name":611,"fn":612,"description":613,"org":614,"tags":615,"stars":23,"repoUrl":24,"updatedAt":620},"mlflow-agent","dispatch MLflow workflows and agent tasks","Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: \"use mlflow\", \"help with mlflow\", \"mlflow agent\", \"add mlflow to my project\", \"trace my agent\", \"evaluate my agent\", or any MLflow task without a specific skill in mind.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[616,617,618,619],{"name":541,"slug":542,"type":13},{"name":9,"slug":8,"type":13},{"name":15,"slug":16,"type":13},{"name":519,"slug":520,"type":13},"2026-07-14T05:39:04.133424",{"slug":622,"name":622,"fn":623,"description":624,"org":625,"tags":626,"stars":23,"repoUrl":24,"updatedAt":632},"mlflow-onboarding","onboard users to MLflow workflows","Onboards users to MLflow by determining their use case (GenAI agents\u002Fapps or traditional ML\u002Fdeep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on \"get started with MLflow\", \"set up MLflow\", \"onboard to MLflow\", \"add MLflow to my project\", \"how do I use MLflow\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[627,628,629],{"name":9,"slug":8,"type":13},{"name":15,"slug":16,"type":13},{"name":630,"slug":631,"type":13},"Onboarding","onboarding","2026-07-14T05:39:06.681003",{"slug":634,"name":634,"fn":635,"description":636,"org":637,"tags":638,"stars":23,"repoUrl":24,"updatedAt":647},"querying-mlflow-metrics","fetch trace metrics from MLflow servers","Fetches aggregated trace metrics (token usage, latency, trace counts, quality evaluations) from MLflow tracking servers. Triggers on requests to show metrics, analyze token usage, view LLM costs, check usage trends, or query trace statistics.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[639,642,645,646],{"name":640,"slug":641,"type":13},"Analytics","analytics",{"name":643,"slug":644,"type":13},"Metrics","metrics",{"name":9,"slug":8,"type":13},{"name":562,"slug":563,"type":13},"2026-07-14T05:39:13.07056",{"slug":649,"name":649,"fn":650,"description":651,"org":652,"tags":653,"stars":23,"repoUrl":24,"updatedAt":658},"retrieving-mlflow-traces","retrieve and query MLflow traces","Retrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status\u002Ftags\u002Fmetadata\u002Fexecution time, query traces, or debug failed traces. Triggers on \"get trace\", \"search traces\", \"find failed traces\", \"filter traces by\", \"traces slower than\", \"query MLflow traces\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[654,655,656,657],{"name":558,"slug":559,"type":13},{"name":9,"slug":8,"type":13},{"name":562,"slug":563,"type":13},{"name":519,"slug":520,"type":13},"2026-07-14T05:39:09.22888",{"slug":4,"name":4,"fn":5,"description":6,"org":660,"tags":661,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[662,663,664,665],{"name":18,"slug":19,"type":13},{"name":21,"slug":22,"type":13},{"name":9,"slug":8,"type":13},{"name":15,"slug":16,"type":13},13,{"items":668,"total":721},[669,676,683,690,698,708,715],{"slug":535,"name":535,"fn":536,"description":537,"org":670,"tags":671,"stars":23,"repoUrl":24,"updatedAt":550},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[672,673,674,675],{"name":541,"slug":542,"type":13},{"name":544,"slug":545,"type":13},{"name":9,"slug":8,"type":13},{"name":548,"slug":549,"type":13},{"slug":552,"name":552,"fn":553,"description":554,"org":677,"tags":678,"stars":23,"repoUrl":24,"updatedAt":565},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[679,680,681,682],{"name":558,"slug":559,"type":13},{"name":9,"slug":8,"type":13},{"name":562,"slug":563,"type":13},{"name":519,"slug":520,"type":13},{"slug":567,"name":567,"fn":568,"description":569,"org":684,"tags":685,"stars":23,"repoUrl":24,"updatedAt":576},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[686,687,688,689],{"name":558,"slug":559,"type":13},{"name":9,"slug":8,"type":13},{"name":562,"slug":563,"type":13},{"name":519,"slug":520,"type":13},{"slug":578,"name":578,"fn":579,"description":580,"org":691,"tags":692,"stars":23,"repoUrl":24,"updatedAt":592},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[693,694,695,696,697],{"name":541,"slug":542,"type":13},{"name":585,"slug":586,"type":13},{"name":558,"slug":559,"type":13},{"name":589,"slug":590,"type":13},{"name":9,"slug":8,"type":13},{"slug":594,"name":594,"fn":595,"description":596,"org":699,"tags":700,"stars":23,"repoUrl":24,"updatedAt":609},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[701,702,703,704,705,706,707],{"name":541,"slug":542,"type":13},{"name":544,"slug":545,"type":13},{"name":9,"slug":8,"type":13},{"name":562,"slug":563,"type":13},{"name":604,"slug":251,"type":13},{"name":519,"slug":520,"type":13},{"name":607,"slug":608,"type":13},{"slug":611,"name":611,"fn":612,"description":613,"org":709,"tags":710,"stars":23,"repoUrl":24,"updatedAt":620},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[711,712,713,714],{"name":541,"slug":542,"type":13},{"name":9,"slug":8,"type":13},{"name":15,"slug":16,"type":13},{"name":519,"slug":520,"type":13},{"slug":622,"name":622,"fn":623,"description":624,"org":716,"tags":717,"stars":23,"repoUrl":24,"updatedAt":632},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[718,719,720],{"name":9,"slug":8,"type":13},{"name":15,"slug":16,"type":13},{"name":630,"slug":631,"type":13},11]