[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7DF15136MDRBYSTDHQ2JFSM":3,"skill-same:01M7DF15136MDRBYSTDHQ2JFSM":122},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":15,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":28,"commitSha":29,"skillDirectory":30,"forksCount":13,"repositoryHtmlUrl":31,"repositoryHomepageUrl":6,"ownerHtmlUrl":32,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":33,"install":34,"resources":47},"01M7DF15136MDRBYSTDHQ2JFSM","neuromosaic",null,"Create neuromosaic fMRI map montages, atlas-annotated cluster reports, or interactive explorers from existing statistical outputs. Use without refitting analyses; distinguish display thresholds from statistical inference.","bbuchsbaum\u002Fbrbskills","bbuchsbaum","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F53819?v=4","01M7DEQDWCB5Q14MV6V9QK719R","01M7DEQDX2QYX57H9RFJGRH22C",0,"MIT","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Fneuromosaic\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Fneuromosaic\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: neuromosaic\ndescription: \"Create neuromosaic fMRI map montages, atlas-annotated cluster reports, or interactive explorers from existing statistical outputs. Use without refitting analyses; distinguish display thresholds from statistical inference.\"\nlicense: MIT\ncompatibility: Requires local filesystem access; R for package execution and Python 3.10+ for optional helpers.\nmetadata:\n  version: \"0.1.0\"\n  reviewed: \"2026-09-27\"\n---\n\n# fMRI reports with neuromosaic\n\nAccept existing maps plus their inference\u002Fprovenance metadata. This stage does\nnot require bidser, fmrireg, or fmrigds, and never refits a model merely to render\nit. Read [report construction](references\u002Freports.md) for the relevant output.\nFor a new report read the [operating contract](references\u002Foperating-contract.md);\nverify [capabilities](references\u002Fcapabilities.md) if the installed API differs.\n\n## Establish what the maps mean\n\nIdentify quantity (effect, SE\u002Fvariance, statistic, p\u002Fq), test distribution and df,\ncontrast\u002Fdirection, spatial frame and grid, background\u002Fatlas compatibility, and\nthe inferential mask\u002Fcorrection\u002Falpha. Follow [artifact contracts](references\u002Fartifact-contracts.md).\nA name such as `z_map` is not evidence of a z distribution. Unknown correction\nmust be labeled unknown or exploratory, never guessed from a threshold.\n\nUse a map manifest with `analysis_id`, `map_id`, `path`, `role`, `quantity`,\n`distribution`, and `label` to group primary tests with effects and uncertainty.\n`assets\u002Freport_template.R` is a source-reviewed starting function. Prefer a\nstatic HTML report initially; PDF depends on additional rendering tools.\n\n## Render faithfully\n\nShow effects and uncertainty as well as thresholded statistic views. Use matched\nscales where comparisons are intended, readable legends\u002Funits, valid masks and\natlas labels, and both relevant signs. Keep cluster connectivity, minimum extent,\nand threshold semantics explicit. A voxel threshold plus minimum cluster size\nis a display rule, not cluster-corrected inference. Never tune display choices\nuntil an anticipated finding appears. Keep null\u002Fempty results and missing inputs\nvisible. Regions selected on the same statistic may have descriptive signal\nplots, but those are not independent confirmatory tests.\n\nIf inference lives in an explicit rejection mask, verify that the chosen rendering\npath actually respects it; otherwise create clearly labeled masked **display\ncopies** without replacing the original maps. Retain the full correction metadata.\nDo not assume an undocumented renderer parameter accepts a q-map or rejection mask.\n\n## Interactive and sharing decisions\n\nRead [privacy and interpretation](references\u002Fprivacy.md) before adding embedded\ninteractive volumes, subject-linked plots, or external sharing. The reviewed\n`montage_interactive()` can embed recoverable voxel values. Local HTML and no CDN\ndo not make an artifact deidentified or safe to upload. Request explicit approval\nfor its disclosure level. Reader-adjusted thresholds are exploratory and must\nnot silently change the authoritative inference\u002Freport defaults.\n\n## Validate and deliver\n\nInspect representative pages\u002Fpanels and map alignment, labels, signs, scale,\nlegends, empty states, and links. When available, compare static and interactive\nsemantic content, not pixel identity. List all generated files and any companion\nasset directory, rendering environment, input hashes, and privacy limitations.\nDo not claim successful rendering from code generation alone. Return the report\nand its data\u002Finference manifest; no additional modeling or cohort interview.\n","# fMRI reports with neuromosaic\n\nAccept existing maps plus their inference\u002Fprovenance metadata. This stage does\nnot require bidser, fmrireg, or fmrigds, and never refits a model merely to render\nit. Read [report construction](references\u002Freports.md) for the relevant output.\nFor a new report read the [operating contract](references\u002Foperating-contract.md);\nverify [capabilities](references\u002Fcapabilities.md) if the installed API differs.\n\n## Establish what the maps mean\n\nIdentify quantity (effect, SE\u002Fvariance, statistic, p\u002Fq), test distribution and df,\ncontrast\u002Fdirection, spatial frame and grid, background\u002Fatlas compatibility, and\nthe inferential mask\u002Fcorrection\u002Falpha. Follow [artifact contracts](references\u002Fartifact-contracts.md).\nA name such as `z_map` is not evidence of a z distribution. Unknown correction\nmust be labeled unknown or exploratory, never guessed from a threshold.\n\nUse a map manifest with `analysis_id`, `map_id`, `path`, `role`, `quantity`,\n`distribution`, and `label` to group primary tests with effects and uncertainty.\n`assets\u002Freport_template.R` is a source-reviewed starting function. Prefer a\nstatic HTML report initially; PDF depends on additional rendering tools.\n\n## Render faithfully\n\nShow effects and uncertainty as well as thresholded statistic views. Use matched\nscales where comparisons are intended, readable legends\u002Funits, valid masks and\natlas labels, and both relevant signs. Keep cluster connectivity, minimum extent,\nand threshold semantics explicit. A voxel threshold plus minimum cluster size\nis a display rule, not cluster-corrected inference. Never tune display choices\nuntil an anticipated finding appears. Keep null\u002Fempty results and missing inputs\nvisible. Regions selected on the same statistic may have descriptive signal\nplots, but those are not independent confirmatory tests.\n\nIf inference lives in an explicit rejection mask, verify that the chosen rendering\npath actually respects it; otherwise create clearly labeled masked **display\ncopies** without replacing the original maps. Retain the full correction metadata.\nDo not assume an undocumented renderer parameter accepts a q-map or rejection mask.\n\n## Interactive and sharing decisions\n\nRead [privacy and interpretation](references\u002Fprivacy.md) before adding embedded\ninteractive volumes, subject-linked plots, or external sharing. The reviewed\n`montage_interactive()` can embed recoverable voxel values. Local HTML and no CDN\ndo not make an artifact deidentified or safe to upload. Request explicit approval\nfor its disclosure level. Reader-adjusted thresholds are exploratory and must\nnot silently change the authoritative inference\u002Freport defaults.\n\n## Validate and deliver\n\nInspect representative pages\u002Fpanels and map alignment, labels, signs, scale,\nlegends, empty states, and links. When available, compare static and interactive\nsemantic content, not pixel identity. List all generated files and any companion\nasset directory, rendering environment, input hashes, and privacy limitations.\nDo not claim successful rendering from code generation alone. Return the report\nand its data\u002Finference manifest; no additional modeling or cohort interview.\n",{"name":5,"license":14,"metadata":24,"description":7,"compatibility":27},{"version":25,"reviewed":26},"0.1.0","2026-09-27","Requires local filesystem access; R for package execution and Python 3.10+ for optional helpers.","main","a5bd39077217516e1456ede5fa15cefb7670d28c","collections\u002Ffmri-workbench\u002Fskills\u002Fneuromosaic","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum","2026-11-11 02:55:31",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add bbuchsbaum\u002Fbrbskills --skill neuromosaic",{"tool":40,"command":41},"bunx","bunx skills add bbuchsbaum\u002Fbrbskills --skill neuromosaic",{"tool":43,"command":44},"pnpm","pnpm dlx skills add bbuchsbaum\u002Fbrbskills --skill 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diagnose, fit, or export first-level task-fMRI GLMs with fmrireg, including HRFs, nuisance baselines, contrasts, and native batch jobs. Use independently of group analysis and reports; not for preprocessing.","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Ffmrireg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Ffmrireg\u002FSKILL.md",[],[],{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":135,"sourceHtmlUrl":136,"categories":137,"keywords":138,"publishedAt":19},"01M7DF0WV14ZWXQKSS0MRY2XWY","fmrigds","Build and validate group-level fMRI analyses with fmrigds from existing effect\u002Fstatistic maps or tables: covariates, uncertainty, repeated measures, corrections, and cohort examination. Does not require first-level refitting.","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Ffmrigds\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Ffmrigds\u002FSKILL.md",[],[],{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":143,"sourceHtmlUrl":144,"categories":145,"keywords":146,"publishedAt":19},"01M7DF0RNZYSF5DQ8HZ86MCZ20","fmri","Coordinate end-to-end task-fMRI analyses across BIDS discovery, fmrireg models, fmrigds group inference, and neuromosaic reports. Use for multi-stage analysis plans or execution, not a single-package question.","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Ffmri\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Ffmri\u002FSKILL.md",[],[],{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":151,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":19},"01M7DF0N2CZ4SNNMZDSPK5X3JH","fmri-bids","Inspect and audit BIDS fMRI datasets with bidser: inherited metadata, derivatives, events, confounds, and acquisition matching. Use for dataset discovery or analysis readiness; do not fit models or preprocess images.","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Ffmri-bids\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcollections\u002Ffmri-workbench\u002Fskills\u002Ffmri-bids\u002FSKILL.md",[],[],{"skillNo":156,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":157,"sourceHtmlUrl":158,"categories":159,"keywords":160,"publishedAt":19},"01M7DEZD9H20TPS57Z509SWSRN","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcodex\u002Fneuromosaic\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcodex\u002Fneuromosaic\u002FSKILL.md",[],[],{"skillNo":162,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":163,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":19},"01M7DEYXMWRWFGPZNFYR9MMB6C","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcodex\u002Ffmrireg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fcodex\u002Ffmrireg\u002FSKILL.md",[],[]]