[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7DEVXVCB7SGWHF36S9G4XHT":3,"skill-same:01M7DEVXVCB7SGWHF36S9G4XHT":152},{"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},"01M7DEVXVCB7SGWHF36S9G4XHT","fmrireg",null,"Specify, 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.","bbuchsbaum\u002Fbrbskills","bbuchsbaum","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F53819?v=4","01M7DEQDWCB5Q14MV6V9QK719R","01M7DEQDX2QYX57H9RFJGRH22C",0,"MIT","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fclaude\u002Ffmrireg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fclaude\u002Ffmrireg\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: fmrireg\ndescription: \"Specify, 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.\"\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: \"2026-09-28\"\n---\n\n# First-level fMRI with fmrireg\n\nStart from preprocessed BOLD with aligned events\u002Fconfounds, an fmri_frame,\nmatrices, or validated bindings. This skill works without bidser, fmrigds, or\nneuromosaic. Do not import a whole cohort workflow for a formula\u002FAPI question.\n\nFor an analysis, read the [operating contract](references\u002Foperating-contract.md)\nand reuse prior decisions. Verify the installed [API](references\u002Fapi.md) and\n[capabilities](references\u002Fcapabilities.md). Ask only unresolved first-level\nscientific questions; do not interview about group covariates or report atlases.\n\nWhen adapting an existing script or joining bidser inputs to a manual model, use\n[existing analysis patterns](references\u002Fexisting-analyses.md). Preserve the\nestimand and downstream outputs; verify historical workarounds against the\nselected package versions before retaining them.\n\n## Specify an estimable model\n\nWhen the intended model is still open, inspect the event tables and use\n[events to candidate designs](references\u002Fevents-to-design.md) to propose supported\nquestions and contrasts before choosing a formula.\nFor ambiguous timing, missing modulators or interactions, use the\n[synthetic design audits](references\u002Fdesign-audits.md). For trialwise\u002FRSA work,\nread the [specialist decision guide](references\u002Ftrial-patterns-rsa.md).\n\nRecord the event meaning and timing origin, duration\u002Famplitude handling, HRF and\nbasis interpretation, contrast sign\u002Fweights, run structure, scaling, nuisance,\nand temporal-noise model. Use [design decisions](references\u002Fdesign-decisions.md)\nand [confounds](references\u002Fconfounds.md) only as needed. Inspect the realized\nmatrix: matching rows, finite values, columns\u002Fcondition counts per run, rank,\nconditioning, residual degrees of freedom, and contrast estimability. Plot a\nsmall design\u002Ftiming sample. Treat a missing condition as unavailable, not zero.\nDo not silently delete nuisance columns, change contrasts, or simplify a\npreregistered model to make a fit run.\n\nUse the public frame\u002Fdesign interfaces and `fmri_lm()`. For new repeated-fit workflows prefer\n`fmri_template()` + explicit bindings + `instantiate()` + `preflight()`; read\n[batch and export](references\u002Fbatch-and-export.md). The `from_bids()` shortcut\nrequires a separately certified unambiguous dataset; its reviewed implementation\nis not safe to assume for mixed TR, repeated run labels across sessions, or\nmultiple echoes\u002Frepresentations. Use `as_manifest()` for explicit bindings.\n\n## Test and fit\n\n`scripts\u002Fsmoke_first_level.R` is a small synthetic compatibility test, not a\nproduction default. `assets\u002Ffirst_level_template.R` builds jobs from reviewed\nbindings and explicit choices without launching them. Verify a representative\npilot (including known irregular runs and distinct acquisition strata), coefficient\u002Fcontrast signs, residuals,\nuncertainty, and execution resources before approved fan-out. Use native job\nfailure records and file-backed reducers; keep whole fits out of agent context.\nThe reviewed `noise_spec(censor=...)` is not regression-volume removal; it only\naffects AR estimation\u002Fwhitening and is inert for iid noise. Do not treat OLS, AR prewhitening, robust weighting, and accelerated engines as\nstatistically interchangeable. A fast engine needs its own validity check.\n\n## Export and stop at scope\n\nExport unthresholded contrast estimates plus SE\u002Fvariance and needed statistics\u002F\ndf; carry coefficient order, contrast weights, masks\u002Fspace, units, temporal-noise\nsettings, and complete provenance. See [artifact contracts](references\u002Fartifact-contracts.md).\nFor group-ready results retain within-person covariance when required. Do not\naverage t\u002Fz maps as if they were effect estimates. For NIfTI inputs to fmrigds,\nfollow the [subject-to-group recipe](references\u002Fnifti-handoff.md): separate contrast\nbeta\u002FSE files, explicit contrast order, verified grids and coverage masks.\n`scripts\u002Fsmoke_nifti_handoff.R` checks this route on synthetic data when both\npackages are available. Group inference is optional;\na first-level request ends with first-level outputs and QC.\n\nApply explicitly confirmed, applicable preferences, but never infer that examples\nsuch as motion24 or B-spline drift are a user's defaults. Freeze realized nuisance\ncolumns and drift basis details in the analysis record. Return exact outputs,\nfailed units, and limitations; never claim a test ran when only source was read.\n","# First-level fMRI with fmrireg\n\nStart from preprocessed BOLD with aligned events\u002Fconfounds, an fmri_frame,\nmatrices, or validated bindings. This skill works without bidser, fmrigds, or\nneuromosaic. Do not import a whole cohort workflow for a formula\u002FAPI question.\n\nFor an analysis, read the [operating contract](references\u002Foperating-contract.md)\nand reuse prior decisions. Verify the installed [API](references\u002Fapi.md) and\n[capabilities](references\u002Fcapabilities.md). Ask only unresolved first-level\nscientific questions; do not interview about group covariates or report atlases.\n\nWhen adapting an existing script or joining bidser inputs to a manual model, use\n[existing analysis patterns](references\u002Fexisting-analyses.md). Preserve the\nestimand and downstream outputs; verify historical workarounds against the\nselected package versions before retaining them.\n\n## Specify an estimable model\n\nWhen the intended model is still open, inspect the event tables and use\n[events to candidate designs](references\u002Fevents-to-design.md) to propose supported\nquestions and contrasts before choosing a formula.\nFor ambiguous timing, missing modulators or interactions, use the\n[synthetic design audits](references\u002Fdesign-audits.md). For trialwise\u002FRSA work,\nread the [specialist decision guide](references\u002Ftrial-patterns-rsa.md).\n\nRecord the event meaning and timing origin, duration\u002Famplitude handling, HRF and\nbasis interpretation, contrast sign\u002Fweights, run structure, scaling, nuisance,\nand temporal-noise model. Use [design decisions](references\u002Fdesign-decisions.md)\nand [confounds](references\u002Fconfounds.md) only as needed. Inspect the realized\nmatrix: matching rows, finite values, columns\u002Fcondition counts per run, rank,\nconditioning, residual degrees of freedom, and contrast estimability. Plot a\nsmall design\u002Ftiming sample. Treat a missing condition as unavailable, not zero.\nDo not silently delete nuisance columns, change contrasts, or simplify a\npreregistered model to make a fit run.\n\nUse the public frame\u002Fdesign interfaces and `fmri_lm()`. For new repeated-fit workflows prefer\n`fmri_template()` + explicit bindings + `instantiate()` + `preflight()`; read\n[batch and export](references\u002Fbatch-and-export.md). The `from_bids()` shortcut\nrequires a separately certified unambiguous dataset; its reviewed implementation\nis not safe to assume for mixed TR, repeated run labels across sessions, or\nmultiple echoes\u002Frepresentations. Use `as_manifest()` for explicit bindings.\n\n## Test and fit\n\n`scripts\u002Fsmoke_first_level.R` is a small synthetic compatibility test, not a\nproduction default. `assets\u002Ffirst_level_template.R` builds jobs from reviewed\nbindings and explicit choices without launching them. Verify a representative\npilot (including known irregular runs and distinct acquisition strata), coefficient\u002Fcontrast signs, residuals,\nuncertainty, and execution resources before approved fan-out. Use native job\nfailure records and file-backed reducers; keep whole fits out of agent context.\nThe reviewed `noise_spec(censor=...)` is not regression-volume removal; it only\naffects AR estimation\u002Fwhitening and is inert for iid noise. Do not treat OLS, AR prewhitening, robust weighting, and accelerated engines as\nstatistically interchangeable. A fast engine needs its own validity check.\n\n## Export and stop at scope\n\nExport unthresholded contrast estimates plus SE\u002Fvariance and needed statistics\u002F\ndf; carry coefficient order, contrast weights, masks\u002Fspace, units, temporal-noise\nsettings, and complete provenance. See [artifact contracts](references\u002Fartifact-contracts.md).\nFor group-ready results retain within-person covariance when required. Do not\naverage t\u002Fz maps as if they were effect estimates. For NIfTI inputs to fmrigds,\nfollow the [subject-to-group recipe](references\u002Fnifti-handoff.md): separate contrast\nbeta\u002FSE files, explicit contrast order, verified grids and coverage masks.\n`scripts\u002Fsmoke_nifti_handoff.R` checks this route on synthetic data when both\npackages are available. Group inference is optional;\na first-level request ends with first-level outputs and QC.\n\nApply explicitly confirmed, applicable preferences, but never infer that examples\nsuch as motion24 or B-spline drift are a user's defaults. Freeze realized nuisance\ncolumns and drift basis details in the analysis record. 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