[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7DEVCE44JSR5MD257NN1X22":3,"skill-same:01M7DEVCE44JSR5MD257NN1X22":132},{"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},"01M7DEVCE44JSR5MD257NN1X22","fmrigds",null,"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.","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\u002Ffmrigds\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fbbuchsbaum\u002Fbrbskills\u002Fblob\u002Fmain\u002Fclaude\u002Ffmrigds\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: fmrigds\ndescription: \"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.\"\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# Group fMRI with fmrigds\n\nAccept existing subject estimates\u002Fmaps\u002Ftables or a first-level handoff; do not\nrequire BIDS or fmrireg. For an API question, read only [API](references\u002Fapi.md).\nFor a new analysis also read the [operating contract](references\u002Foperating-contract.md)\nand [inference decisions](references\u002Finference.md). Check [installed capabilities](references\u002Fcapabilities.md).\n\n## Establish the observation and estimand\n\nVerify subject identity\u002Forder, independent units, repeated sessions\u002Fconditions,\nspatial alignment, contrast semantics and units, masks, missingness, covariates,\nand the meaning of available assays. Follow the [handoff bridge](references\u002Fhandoff.md).\nFor subject NIfTI beta\u002FSE maps use the [NIfTI recipe](references\u002Fnifti-handoff.md),\nincluding explicit contrast order and coverage masks; import success does not\nverify every image's affine.\nAn SE is not a variance; a z\u002Fp map cannot uniquely supply an effect and its\nsampling variance. Effects without uncertainty can support a suitable unweighted\ngroup model; do not invent unit variance for precision weighting. Repeated runs\nare not independent subjects. A fixed-effects teaching example is not a default\npopulation model.\n\nAsk only material unresolved group decisions: estimand\u002Fmodel\u002Fcovariates,\nrepeated-measures structure, uncertainty assumptions, inclusion\u002Fmissingness,\nand correction family. Freeze the proposed model and contrast before inspecting\nits interesting outcomes. Fit neither an unsupported general mixed model nor\na whole-brain correction independently by chunk.\n\n## Build a lazy plan\n\nUse `gds()` or documented `as_gds()` inputs, keyed covariate attachment, explicit\n`reduce()` or appropriate public model helper, and `posthoc()` where justified.\nInspect `explain()` \u002F `preview()` \u002F `validate()` before approved `compute()`.\n`assets\u002Fgroup_template.R` constructs a lazy plan from explicit inputs; it does\nnot execute. `scripts\u002Fsmoke_group.R` checks a known fixed-effects arithmetic\nidentity on tiny synthetic data, not a production model choice.\n\nChoose public registered methods supported by the installed version. The reviewed\nLMM family is intentionally restricted: do not assume arbitrary lmer syntax,\nrandom effects, missing layouts, or covariance structures. Unsupported science\nrequires an explicit alternative adapter\u002Fmodel, not silent simplification.\n\n## Examine, correct, export\n\nUse `examine_group()` where supported to separate validity concerns, model\nsurprise, and influence. A review priority is not an exclusion rule. Record\nsensitivity analyses without redefining the primary cohort opportunistically.\nPreserve actual per-sample N, model\u002Fvariance diagnostics, convergence flags,\nheterogeneity where relevant, and missingness. Correct over the declared full\nfamily, accounting explicitly for mask, contrasts, sidedness, and post-selection.\nRetain unthresholded effects\u002Fstatistics, uncertainty, p\u002Fq and rejection masks.\n\nComplete the [artifact contract](references\u002Fartifact-contracts.md) with a frozen\ninference manifest. A reporting stage is optional; do not rerun a model just to\nmake maps look more impressive. A report renderer's cluster threshold is not a\nstatistical correction. Deliver outputs and limitations, including any failed\nsubjects\u002Fsamples, numerical approximations, or unsupported assumptions.\n","# Group fMRI with fmrigds\n\nAccept existing subject estimates\u002Fmaps\u002Ftables or a first-level handoff; do not\nrequire BIDS or fmrireg. For an API question, read only [API](references\u002Fapi.md).\nFor a new analysis also read the [operating contract](references\u002Foperating-contract.md)\nand [inference decisions](references\u002Finference.md). Check [installed capabilities](references\u002Fcapabilities.md).\n\n## Establish the observation and estimand\n\nVerify subject identity\u002Forder, independent units, repeated sessions\u002Fconditions,\nspatial alignment, contrast semantics and units, masks, missingness, covariates,\nand the meaning of available assays. Follow the [handoff bridge](references\u002Fhandoff.md).\nFor subject NIfTI beta\u002FSE maps use the [NIfTI recipe](references\u002Fnifti-handoff.md),\nincluding explicit contrast order and coverage masks; import success does not\nverify every image's affine.\nAn SE is not a variance; a z\u002Fp map cannot uniquely supply an effect and its\nsampling variance. Effects without uncertainty can support a suitable unweighted\ngroup model; do not invent unit variance for precision weighting. Repeated runs\nare not independent subjects. A fixed-effects teaching example is not a default\npopulation model.\n\nAsk only material unresolved group decisions: estimand\u002Fmodel\u002Fcovariates,\nrepeated-measures structure, uncertainty assumptions, inclusion\u002Fmissingness,\nand correction family. Freeze the proposed model and contrast before inspecting\nits interesting outcomes. Fit neither an unsupported general mixed model nor\na whole-brain correction independently by chunk.\n\n## Build a lazy plan\n\nUse `gds()` or documented `as_gds()` inputs, keyed covariate attachment, explicit\n`reduce()` or appropriate public model helper, and `posthoc()` where justified.\nInspect `explain()` \u002F `preview()` \u002F `validate()` before approved `compute()`.\n`assets\u002Fgroup_template.R` constructs a lazy plan from explicit inputs; it does\nnot execute. `scripts\u002Fsmoke_group.R` checks a known fixed-effects arithmetic\nidentity on tiny synthetic data, not a production model choice.\n\nChoose public registered methods supported by the installed version. The reviewed\nLMM family is intentionally restricted: do not assume arbitrary lmer syntax,\nrandom effects, missing layouts, or covariance structures. Unsupported science\nrequires an explicit alternative adapter\u002Fmodel, not silent simplification.\n\n## Examine, correct, export\n\nUse `examine_group()` where supported to separate validity concerns, model\nsurprise, and influence. A review priority is not an exclusion rule. Record\nsensitivity analyses without redefining the primary cohort opportunistically.\nPreserve actual per-sample N, model\u002Fvariance diagnostics, convergence flags,\nheterogeneity where relevant, and missingness. Correct over the declared full\nfamily, accounting explicitly for mask, contrasts, sidedness, and post-selection.\nRetain unthresholded effects\u002Fstatistics, uncertainty, p\u002Fq and rejection masks.\n\nComplete the [artifact contract](references\u002Fartifact-contracts.md) with a frozen\ninference manifest. A reporting stage is optional; do not rerun a model just to\nmake maps look more impressive. A report renderer's cluster threshold is not a\nstatistical correction. Deliver outputs and limitations, including any failed\nsubjects\u002Fsamples, numerical approximations, or unsupported assumptions.\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","claude\u002Ffmrigds","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 fmrigds",{"tool":40,"command":41},"bunx","bunx skills add bbuchsbaum\u002Fbrbskills --skill fmrigds",{"tool":43,"command":44},"pnpm","pnpm dlx skills add bbuchsbaum\u002Fbrbskills --skill 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