[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-jetbrains-datalore-notebook":3,"mdc-vfihpt-key":32,"related-repo-jetbrains-datalore-notebook":1853,"related-org-jetbrains-datalore-notebook":1861},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":22,"repoUrl":23,"updatedAt":24,"license":25,"forks":22,"topics":26,"repo":27,"sourceUrl":30,"mdContent":31},"datalore-notebook","manage and execute Datalore notebooks","Work with Datalore notebooks through the bundled CLI. Read, write, and execute notebook cells to perform data analysis, build visualizations, query databases, and manage notebook files. Use when user shares a Datalore notebook URL, asks to \"analyze data in Datalore\", \"run a notebook\", \"explore a dataset\", \"write SQL against attached databases\", or \"create notebook cells\". Requires uv.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"jetbrains","JetBrains","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fjetbrains.png",[12,16,19],{"name":13,"slug":14,"type":15},"Data Visualization","data-visualization","tag",{"name":17,"slug":18,"type":15},"Data Analysis","data-analysis",{"name":20,"slug":21,"type":15},"Notebooks","notebooks",0,"https:\u002F\u002Fgithub.com\u002FJetBrains\u002Fdatalore-skills","2026-07-17T06:06:54.178806",null,[],{"repoUrl":23,"stars":22,"forks":22,"topics":28,"description":29},[],"AI Agent skills for Datalore","https:\u002F\u002Fgithub.com\u002FJetBrains\u002Fdatalore-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fdatalore-notebook","---\nname: datalore-notebook\ndescription: >\n  Work with Datalore notebooks through the bundled CLI. Read, write, and execute\n  notebook cells to perform data analysis, build visualizations, query databases,\n  and manage notebook files. Use when user shares a Datalore notebook URL, asks to\n  \"analyze data in Datalore\", \"run a notebook\", \"explore a dataset\", \"write SQL\n  against attached databases\", or \"create notebook cells\". Requires uv.\ncompatibility: >\n  Requires macOS\u002FLinux with uv installed. Authenticates with\n  DATALORE_API_TOKEN, OAuth PKCE browser login, or notebook-scoped system keychain credentials via the bundled scripts\u002Fdatalore CLI.\nmetadata:\n  author: JetBrains\n  version: 0.0.1\n---\n\n# Datalore Notebook Agent\n\nUse the bundled `scripts\u002Fdatalore` CLI for notebook work. It is a `uv run --script` CLI with pinned inline Python dependencies, wraps the public Notebook API, stores notebook identity in `.datalore-session`, and authenticates with `DATALORE_API_TOKEN` first, otherwise system keychain credentials saved per notebook path. When no valid credential is available, `init` starts OAuth PKCE browser login and saves the resulting notebook credentials.\n\n## Available scripts\n\n- `scripts\u002Fdatalore` - CLI for Datalore notebook cells, files, databases, kernel, and worksheet operations. Run notebook commands through this script. For setup, run the absolute path to `scripts\u002Fdatalore init \u003Cnotebook-url>` from the task workspace so `.datalore-session` is created there. To remove local access for the current notebook, run `scripts\u002Fdatalore logout`; to remove credentials for another notebook, run `scripts\u002Fdatalore logout \u003Cnotebook-url>`.\n\n## Setup\n\nInitialize from the workspace where the agent will work on this notebook. The CLI writes `.datalore-session` to the current directory, so do not run `init` from the installed skill directory unless that is intentionally the session workspace.\n\n```bash\n# keychain-free setups; CLI will not read\u002Fwrite keychain when this is set\nexport DATALORE_API_TOKEN=\"\u003Ctoken>\"\n\ncd \u003Ctask-workspace>\n# Assuming the skill is installed in ~\u002F.agents. Adjust the absolute path according to your setup.\n~\u002F.agents\u002Fskills\u002Fdatalore-notebook\u002Fscripts\u002Fdatalore init \u003Cnotebook-url>\n```\n\nIf `DATALORE_API_TOKEN` is not set, `init` starts OAuth PKCE browser login and saves the resulting token in the keychain under the notebook path. Run `init` yourself from the task workspace and relay the printed browser URL if the browser does not open automatically. Do not ask the user to paste tokens into chat.\n\nIf a valid command fails for authentication, run `init` yourself FROM THE CURRENT DIRECTORY. Do not continue with token discovery. Ask the user to run `init` manually only if the CLI cannot start OAuth, the OAuth callback times out, keychain access fails, or the user needs to complete browser authorization outside the agent environment.\n\nPut `--json` before the command for machine-readable responses. This applies to API commands that return structured data; `file read` and `file download` always stream raw file bytes to stdout, so do not use `--json` with them. Prefer the CLI over raw API calls; use `references\u002Fapi-reference.md` only for schema details.\n\n## Workflow\n\n1. Orient first, treating authentication as a hard gate.\nIf no `.datalore-session` exists or authentication fails, run `init` FROM THE CURRENT DIRECTORY. `init` can start OAuth PKCE browser login and prints an authorization URL before waiting for the callback, so do not stop just because the environment is non-interactive. Stop and ask the user to run `init` manually only after the automatic `init` attempt fails in a way the agent cannot complete.\n\n```bash\nscripts\u002Fdatalore cells --full\nscripts\u002Fdatalore --json cells --include-outputs\n```\n\n2. Discover data before coding. Do not guess file formats, table names, or columns.\n\n```bash\nscripts\u002Fdatalore files --directory data\u002Fnotebook_files\nscripts\u002Fdatalore databases\nscripts\u002Fdatalore db condensed \u003CdatabaseId>\nscripts\u002Fdatalore db schema \u003CdatabaseId> --depth 1\nscripts\u002Fdatalore db search \u003CdatabaseId> \u003Cquery>\n```\n\n3. Create\u002Fedit cells using stdin for multi-line source.\n\n```bash\nscripts\u002Fdatalore cell create --type CODE --after \u003Ccell-id> --wait \u003C\u003C 'EOF'\nprint(\"hello\")\nEOF\n\nscripts\u002Fdatalore cell edit \u003Ccell-id> --wait \u003C\u003C 'EOF'\nprint(\"updated\")\nEOF\n```\n\nFor SQL cells:\n\n```bash\nscripts\u002Fdatalore cell create --type SQL --database-id \u003CdatabaseId> --variable result_df --wait \u003C\u003C 'EOF'\nselect *\nfrom public.orders\nlimit 10\nEOF\n```\n\nFor markdown:\n\n```bash\nscripts\u002Fdatalore cell create --type MARKDOWN --before \u003Cfirst-cell-id> \u003C\u003C 'EOF'\n# Analysis\nEOF\n```\n\n4. Run, inspect, and recover.\n\n```bash\nscripts\u002Fdatalore cell run \u003Ccell-id> --wait\nscripts\u002Fdatalore cell outputs \u003Ccell-id>\nscripts\u002Fdatalore kernel status\nscripts\u002Fdatalore kernel interrupt\n```\n\nTreat `VALID` and `WARNING` as success. For `ERROR`, inspect traceback\u002Fstdout\u002Fstderr, fix, and rerun. For `TIMEOUT`, the cell may still be running; use `--wait` for single-cell commands when you need longer polling, and for `cells run` inspect outputs or interrupt before continuing. In non-obvious cases, when using `--json` for commands that execute cells, check the returned execution status rather than relying only on process exit code. After errors, assume kernel state may be partially mutated.\n\n5. Clean up before finishing. Only delete cells or files created during the current task.\n\n```bash\nscripts\u002Fdatalore cells --full\nscripts\u002Fdatalore cell delete \u003Ccell-id>\nscripts\u002Fdatalore file delete \u003Cpath>\n```\n\n## Commands\n\n```bash\nscripts\u002Fdatalore --json \u003Ccommand> ...\nscripts\u002Fdatalore logout [notebook-url]\nscripts\u002Fdatalore cell get \u003Ccell-id>\nscripts\u002Fdatalore cells run \u003Ccell-id>...\nscripts\u002Fdatalore files --directory data\u002Fnotebook_files\nscripts\u002Fdatalore file text \u003Cpath> --max-lines 20\nscripts\u002Fdatalore file read \u003Cpath>  # raw bytes; no --json\nscripts\u002Fdatalore file upload \u003Clocal-path> --directory data\u002Fnotebook_files\nscripts\u002Fdatalore cell create --type CONTROL --control-json '{\"controlType\":\"TEXT_INPUT\",\"label\":\"Name\",\"variable\":\"name\",\"value\":\"Alice\",\"multiline\":false}' --wait\nscripts\u002Fdatalore cell update-control \u003Ccell-id> --control-json '{\"controlType\":\"TEXT_INPUT\",\"label\":\"Name\",\"variable\":\"name\",\"value\":\"Bob\",\"multiline\":false}' --wait\nscripts\u002Fdatalore worksheet create --name \"Analysis\"\n```\n\n`worksheet create` is persistent; use it only when a new tab is intended.\n\nCONTROL cells are executable in single-cell commands (`cell create --type CONTROL --wait`, `cell run`, and `cell update-control --wait`) because running them assigns the configured value to the kernel variable. Batch `cells run` is limited to CODE and SQL cells.\n\n## Troubleshooting\n\n- No credentials or 401: Run `\u002Fabsolute\u002Fpath\u002Fto\u002Fdatalore-notebook\u002Fscripts\u002Fdatalore init \u003Cnotebook-url>` from the task workspace. If OAuth PKCE prints an authorization URL, relay it to the user. Ask the user to run the command manually only if the agent-run `init` cannot complete.\n- Empty\u002Funhelpful output: retry with `--json` or `--verbose`.\n- File endpoints fail before computation starts: run `scripts\u002Fdatalore cell create --type CODE --wait --source \"print('ready')\"` and retry.\n- Insert at top: use `--before \u003Cfirst-cell-id>`. Omitting `--before`\u002F`--after` appends.\n",{"data":33,"body":37},{"name":4,"description":6,"compatibility":34,"metadata":35},"Requires macOS\u002FLinux with uv installed. Authenticates with DATALORE_API_TOKEN, OAuth PKCE browser login, or notebook-scoped system keychain credentials via the bundled scripts\u002Fdatalore CLI.\n",{"author":9,"version":36},"0.0.1",{"type":38,"children":39},"root",[40,49,96,103,149,155,174,312,338,357,401,407,451,497,505,675,683,837,842,947,952,1028,1036,1145,1204,1212,1300,1306,1710,1721,1757,1763,1847],{"type":41,"tag":42,"props":43,"children":45},"element","h1",{"id":44},"datalore-notebook-agent",[46],{"type":47,"value":48},"text","Datalore Notebook Agent",{"type":41,"tag":50,"props":51,"children":52},"p",{},[53,55,62,64,70,72,78,80,86,88,94],{"type":47,"value":54},"Use the bundled ",{"type":41,"tag":56,"props":57,"children":59},"code",{"className":58},[],[60],{"type":47,"value":61},"scripts\u002Fdatalore",{"type":47,"value":63}," CLI for notebook work. 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For setup, run the absolute path to ",{"type":41,"tag":56,"props":119,"children":121},{"className":120},[],[122],{"type":47,"value":123},"scripts\u002Fdatalore init \u003Cnotebook-url>",{"type":47,"value":125}," from the task workspace so ",{"type":41,"tag":56,"props":127,"children":129},{"className":128},[],[130],{"type":47,"value":77},{"type":47,"value":132}," is created there. To remove local access for the current notebook, run ",{"type":41,"tag":56,"props":134,"children":136},{"className":135},[],[137],{"type":47,"value":138},"scripts\u002Fdatalore logout",{"type":47,"value":140},"; to remove credentials for another notebook, run ",{"type":41,"tag":56,"props":142,"children":144},{"className":143},[],[145],{"type":47,"value":146},"scripts\u002Fdatalore logout \u003Cnotebook-url>",{"type":47,"value":148},".",{"type":41,"tag":97,"props":150,"children":152},{"id":151},"setup",[153],{"type":47,"value":154},"Setup",{"type":41,"tag":50,"props":156,"children":157},{},[158,160,165,167,172],{"type":47,"value":159},"Initialize from the workspace where the agent will work on this notebook. The CLI writes ",{"type":41,"tag":56,"props":161,"children":163},{"className":162},[],[164],{"type":47,"value":77},{"type":47,"value":166}," to the current directory, so do not run ",{"type":41,"tag":56,"props":168,"children":170},{"className":169},[],[171],{"type":47,"value":93},{"type":47,"value":173}," from the installed skill directory unless that is intentionally the session workspace.",{"type":41,"tag":175,"props":176,"children":181},"pre",{"className":177,"code":178,"language":179,"meta":180,"style":180},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# keychain-free setups; CLI will not read\u002Fwrite keychain when this is set\nexport DATALORE_API_TOKEN=\"\u003Ctoken>\"\n\ncd \u003Ctask-workspace>\n# Assuming the skill is installed in ~\u002F.agents. 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Reach for this skill whenever the task involves authoring or modifying `\u003Clang>\u002FlanguageModels\u002Fbehavior.mps`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1896,1897],{"name":1870,"slug":1871,"type":15},{"name":1876,"slug":1877,"type":15},"2026-07-13T06:45:21.757084",{"slug":1900,"name":1900,"fn":1901,"description":1902,"org":1903,"tags":1904,"stars":1878,"repoUrl":1879,"updatedAt":1909},"mps-aspect-constraints","define JetBrains MPS language constraints","Use when defining or editing MPS language constraints — property validators \u002F setters \u002F getters, referent search scopes (imperative or inherited via `ScopeProvider.getScope`), `referentSetHandler` side effects, default-scope blocks, `canBeChild` \u002F `canBeParent` \u002F `canBeAncestor` \u002F `canBeRoot` placement rules, `defaultConcreteConcept` for abstract concepts, `set \u003Cread-only>` and `{name}` aliasing, and scope helpers (`SimpleRoleScope`, `ListScope`, `CompositeScope`, `HidingByNameScope`). Reach for this skill whenever the task involves authoring or modifying `\u003Clang>\u002FlanguageModels\u002Fconstraints.mps`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1905,1906],{"name":1870,"slug":1871,"type":15},{"name":1907,"slug":1908,"type":15},"Code Analysis","code-analysis","2026-07-23T05:41:33.639365",{"slug":1911,"name":1911,"fn":1912,"description":1913,"org":1914,"tags":1915,"stars":1878,"repoUrl":1879,"updatedAt":1917},"mps-aspect-dataflow","define and debug MPS dataflow builders","Use when defining or debugging MPS dataflow builders for a concept — control\u002Fdata flow declarations that drive reachability analysis and variable-use checking. Covers DataFlowBuilderDeclaration, BuilderBlock, emit instructions (code for, jump, ifjump, label, read, write, ret, mayBeUnreachable), positions (AfterPosition, BeforePosition, LabelPosition), the jetbrains.mps.lang.dataFlow language, the NodeParameter implicit, BL+smodel usage inside builder bodies, and IBuilderMode for advanced analyses such as nullable\u002Fnon-null tracking.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1916],{"name":17,"slug":18,"type":15},"2026-07-13T06:45:19.114674",{"slug":1919,"name":1919,"fn":1920,"description":1921,"org":1922,"tags":1923,"stars":1878,"repoUrl":1879,"updatedAt":1930},"mps-aspect-editor","define MPS editor layouts","Use when creating or changing MPS editor definitions — the overall workflow from scaffolding a `ConceptEditorDeclaration` through componentizing reusable `EditorComponentDeclaration`s, refining cell models and cell layouts, applying style sheets and indent-layout style items, wiring smart references, leveraging inheritance via super-concepts and interfaces, inspecting (`print_node_json`, `show_node_representation`) and validating (`check_root_node_problems`). Covers `jetbrains.mps.lang.editor` cell models (`CellModel_RefNode`\u002F`CellModel_RefNodeList`\u002F`CellModel_RefCell`\u002F`CellModel_Property`\u002F`CellModel_Constant`), layout choices, and JSON blueprints for common editor shapes. For the non-layout side (action maps, keymaps, transformation\u002Fsubstitute menus) use `mps-aspect-editor-menus-and-keymaps`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1924,1927],{"name":1925,"slug":1926,"type":15},"Design","design",{"name":1928,"slug":1929,"type":15},"UI Components","ui-components","2026-07-23T05:41:56.638151",{"slug":1932,"name":1932,"fn":1933,"description":1934,"org":1935,"tags":1936,"stars":1878,"repoUrl":1879,"updatedAt":1939},"mps-aspect-editor-menus-and-keymaps","author MPS editor menus and keymaps","Use when authoring the **non-layout** parts of the MPS editor aspect — what happens when the user types, presses a key, triggers completion, pastes, or invokes a context action. Covers action maps (`CellActionMapDeclaration`), cell keymaps (`CellKeyMapDeclaration`), transformation menus (`TransformationMenu_Default` \u002F `_Named` \u002F `_Contribution`), substitute menus (`SubstituteMenu_Default` \u002F `SubstituteMenu` \u002F contributions), side transforms (LEFT\u002FRIGHT), legacy cell menus, paste wrappers and copy-paste handlers (in the actions language), completion styling, reference presentation, two-step deletion, and the editor selection API. Trigger terms: `actionMap`, `keyMap`, `delete_action_id`, `transformationMenu`, `substituteMenu`, `Ctrl+Space`, `Ctrl+Alt+B`, side transform, paste wrapper, completion styling, `PasteWrappers`, `CopyPasteHandlers`. For the **layout** side (cells, layouts, style sheets) use `mps-aspect-editor` instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1937,1938],{"name":1876,"slug":1877,"type":15},{"name":1928,"slug":1929,"type":15},"2026-07-23T05:41:49.666535",{"slug":1941,"name":1941,"fn":1942,"description":1943,"org":1944,"tags":1945,"stars":1878,"repoUrl":1879,"updatedAt":1948},"mps-aspect-generation-plan","modify MPS generation plans","Use when defining or modifying an MPS generation plan — explicit ordering of generators, checkpoints for cross-model reference resolution, forks for parallel branches, IncludePlan composition, conditional PlanContribution activation, ParameterEquals\u002FConceptListSelector fork selectors, and InitModelAttributes for targetFacet routing. Apply when working with @genplan models, the jetbrains.mps.lang.generator.plan language, attaching plans via DevKits or the Custom generation facet, or debugging cross-model mapping label resolution.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1946,1947],{"name":1870,"slug":1871,"type":15},{"name":1876,"slug":1877,"type":15},"2026-07-13T06:44:59.507855",{"slug":1950,"name":1950,"fn":1951,"description":1952,"org":1953,"tags":1954,"stars":1878,"repoUrl":1879,"updatedAt":1958},"mps-aspect-generator","define JetBrains MPS generator rules","Use when defining or modifying MPS generators — author a generator module, add or edit root\u002Freduction\u002Fweaving\u002Fpattern mapping rules, attach template macros ($COPY_SRC, $LOOP, $IF, $PROPERTY, $REF, $SWITCH, $MAP_SRC, $WEAVE, $INSERT, $LABEL, $TRACE, $VAR), wire mapping labels, build template switches, write pre\u002Fpost mapping scripts, navigate `genContext`, or debug \"rule didn't fire\", missing references, empty output, infinite reduction loops, and generated-Java compile failures.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1955,1956,1957],{"name":1870,"slug":1871,"type":15},{"name":1907,"slug":1908,"type":15},{"name":1876,"slug":1877,"type":15},"2026-07-17T06:06:58.042999",{"slug":1960,"name":1960,"fn":1961,"description":1962,"org":1963,"tags":1964,"stars":1878,"repoUrl":1879,"updatedAt":1967},"mps-aspect-intentions","define and edit MPS intentions","Use when defining or editing MPS intentions (the Alt+Enter context-action aspect) — adding `IntentionDeclaration` roots, parameterized or surround-with variants, description\u002FisApplicable\u002Fexecute blocks, child-filter functions, factory-initialized AST splicing, or debugging why an intention is not offered. Lives in the language's `intentions` model and uses `jetbrains.mps.lang.intentions`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1965,1966],{"name":1870,"slug":1871,"type":15},{"name":1876,"slug":1877,"type":15},"2026-07-23T05:41:48.692899",{"slug":1969,"name":1969,"fn":1970,"description":1971,"org":1972,"tags":1973,"stars":1878,"repoUrl":1879,"updatedAt":1980},"mps-aspect-migrations","author and debug MPS migration scripts","Use when authoring or debugging MPS migration scripts that upgrade user models after a language definition changes — covers jetbrains.mps.lang.migration (MigrationScript class-based, PureMigrationScript declarative, MoveConcept\u002FMoveContainmentLink\u002FMoveReferenceLink\u002FMoveProperty, ordering via OrderDependency, data exchange via putData\u002FgetData, RefactoringLog, ConceptMigrationReference) and jetbrains.mps.lang.script Enhancement Scripts (MigrationScript with MigrationScriptPart_Instance, ExtractInterfaceMigration, FactoryMigrationScriptPart, CommentMigrationScriptPart) — when a model needs version-gated upgrade, concept rename or removal, link or property rename, instance-level transformation, or composition of migration steps.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1974,1977],{"name":1975,"slug":1976,"type":15},"Debugging","debugging",{"name":1978,"slug":1979,"type":15},"Migration","migration","2026-07-13T06:45:20.372122",{"slug":1982,"name":1982,"fn":1983,"description":1984,"org":1985,"tags":1986,"stars":1878,"repoUrl":1879,"updatedAt":1990},"mps-aspect-structure-concepts","define concepts in MPS structure aspect","Define concepts, interface concepts, enumerations, and constrained data types in an MPS language's `structure` aspect. Covers smart-reference detection, alias rules, cardinality, INamedConcept usage, bulk creation, and the full `mps_mcp_alter_structure` \u002F `mps_mcp_query_structure` reference. Use when authoring or modifying a language's structure model.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1987],{"name":1988,"slug":1989,"type":15},"Data Modeling","data-modeling","2026-07-23T05:41:30.705975",188]