[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M75N188QJSAS3PB803ETF6M1":3,"skill-same:01M75N188QJSAS3PB803ETF6M1":458},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"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":38,"commitSha":39,"skillDirectory":40,"forksCount":13,"repositoryHtmlUrl":41,"repositoryHomepageUrl":6,"ownerHtmlUrl":42,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":43,"install":44,"resources":57},"01M75N188QJSAS3PB803ETF6M1","Experimental Design (DOE & randomization)",null,"rhCat\u002FskillChip","rhCat","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F90529833?v=4","01M75MPVRCVW3V3RQ924XGPE0G","Rui He","01M75MPVSDT47E5PYXE0XFKFZB",0,"MIT","https:\u002F\u002Fgithub.com\u002FrhCat\u002FskillChip\u002Fblob\u002Fmain\u002Fkdense\u002Fexperimental-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FrhCat\u002FskillChip\u002Fblob\u002Fmain\u002Fkdense\u002Fexperimental-design\u002FSKILL.md",[],[],"2026-11-12 13:55:43",false,"---\nskill: experimental-design\nname: Experimental Design (DOE & randomization)\nperks: [simple-randomization, block-randomization, stratified-block-randomization, cluster-randomization, assign-factorial-runs, full-factorial, two-level-factorial, fractional-factorial, plackett-burman, central-composite, box-behnken, latin-hypercube]\n---\n\n# experimental-design — Experimental Design (DOE & randomization)\n\nGenerate seeded randomization\u002Fallocation schedules and design-of-experiments matrices (read-only, local). Each perk reads a small JSON spec and writes a reproducible CSV layout under `record_store`.\n\n## What to look out for\nEach tool emits one line of structured JSON (the audit + debug log) and writes its artifacts under `record_store`. LOGS TO CHECK: that line + the named CSV + the executor run-ledger. Every layout is seeded, so the exact schedule\u002Fmatrix can be archived and regenerated (a requirement for trial registration and good lab practice).\n\nThe randomization perks (`simple-`, `block-`, `stratified-block-`, `cluster-randomization`, `assign-factorial-runs`) need only numpy + pandas. The DOE-matrix perks (`full-`, `two-level-`, `fractional-factorial`, `plackett-burman`, `central-composite`, `box-behnken`, `latin-hypercube`) additionally require `pyDOE3`; when it is absent the porter writes a graceful empty `{}` artifact rather than failing.\n\n## Perks\n| perk | tool | nature |\n|---|---|---|\n| `simple-randomization` | `simple_randomization` | read-only — independent per-unit assignment (numpy\u002Fpandas) |\n| `block-randomization` | `block_randomization` | read-only — permuted blocks, balance throughout (numpy\u002Fpandas) |\n| `stratified-block-randomization` | `stratified_block_randomization` | read-only — blocks within strata (numpy\u002Fpandas) |\n| `cluster-randomization` | `cluster_randomization` | read-only — randomize whole clusters (numpy\u002Fpandas) |\n| `assign-factorial-runs` | `assign_factorial_runs` | read-only — randomize run order of design rows (numpy\u002Fpandas) |\n| `full-factorial` | `full_factorial` | read-only — full factorial over explicit levels (pyDOE3) |\n| `two-level-factorial` | `two_level_factorial` | read-only — full 2^k factorial (pyDOE3) |\n| `fractional-factorial` | `fractional_factorial` | read-only — 2^(k-p) from a generator (pyDOE3) |\n| `plackett-burman` | `plackett_burman` | read-only — main-effects screening (pyDOE3) |\n| `central-composite` | `central_composite` | read-only — response-surface CCD (pyDOE3) |\n| `box-behnken` | `box_behnken` | read-only — response-surface BBD, >=3 factors (pyDOE3) |\n| `latin-hypercube` | `latin_hypercube` | read-only — space-filling sample (pyDOE3) |\n\nAll perks are `destructive: false`: they read a JSON spec and write a CSV; nothing mutates a remote or live service.\n\n## How to use it\nPick a perk, copy `ledger.json` → `task-ledger.json`, fill its vars + `record_store`, then validate → compose → compile → oversight → executor.\n\n> Localized from [K-Dense-AI\u002Fscientific-agent-skills](https:\u002F\u002Fgithub.com\u002FK-Dense-AI\u002Fscientific-agent-skills) `experimental-design` — MIT (see LICENSE.txt).\n","# experimental-design — Experimental Design (DOE & randomization)\n\nGenerate seeded randomization\u002Fallocation schedules and design-of-experiments matrices (read-only, local). Each perk reads a small JSON spec and writes a reproducible CSV layout under `record_store`.\n\n## What to look out for\nEach tool emits one line of structured JSON (the audit + debug log) and writes its artifacts under `record_store`. LOGS TO CHECK: that line + the named CSV + the executor run-ledger. Every layout is seeded, so the exact schedule\u002Fmatrix can be archived and regenerated (a requirement for trial registration and good lab practice).\n\nThe randomization perks (`simple-`, `block-`, `stratified-block-`, `cluster-randomization`, `assign-factorial-runs`) need only numpy + pandas. The DOE-matrix perks (`full-`, `two-level-`, `fractional-factorial`, `plackett-burman`, `central-composite`, `box-behnken`, `latin-hypercube`) additionally require `pyDOE3`; when it is absent the porter writes a graceful empty `{}` artifact rather than failing.\n\n## Perks\n| perk | tool | nature |\n|---|---|---|\n| `simple-randomization` | `simple_randomization` | read-only — independent per-unit assignment (numpy\u002Fpandas) |\n| `block-randomization` | `block_randomization` | read-only — permuted blocks, balance throughout (numpy\u002Fpandas) |\n| `stratified-block-randomization` | `stratified_block_randomization` | read-only — blocks within strata (numpy\u002Fpandas) |\n| `cluster-randomization` | `cluster_randomization` | read-only — randomize whole clusters (numpy\u002Fpandas) |\n| `assign-factorial-runs` | `assign_factorial_runs` | read-only — randomize run order of design rows (numpy\u002Fpandas) |\n| `full-factorial` | `full_factorial` | read-only — full factorial over explicit levels (pyDOE3) |\n| `two-level-factorial` | `two_level_factorial` | read-only — full 2^k factorial (pyDOE3) |\n| `fractional-factorial` | `fractional_factorial` | read-only — 2^(k-p) from a generator (pyDOE3) |\n| `plackett-burman` | `plackett_burman` | read-only — main-effects screening (pyDOE3) |\n| `central-composite` | `central_composite` | read-only — response-surface CCD (pyDOE3) |\n| `box-behnken` | `box_behnken` | read-only — response-surface BBD, >=3 factors (pyDOE3) |\n| `latin-hypercube` | `latin_hypercube` | read-only — space-filling sample (pyDOE3) |\n\nAll perks are `destructive: false`: they read a JSON spec and write a CSV; nothing mutates a remote or live service.\n\n## How to use it\nPick a perk, copy `ledger.json` → `task-ledger.json`, fill its vars + `record_store`, then validate → compose → compile → oversight → executor.\n\n> Localized from [K-Dense-AI\u002Fscientific-agent-skills](https:\u002F\u002Fgithub.com\u002FK-Dense-AI\u002Fscientific-agent-skills) `experimental-design` — MIT (see LICENSE.txt).\n",{"name":5,"perks":24,"skill":37},[25,26,27,28,29,30,31,32,33,34,35,36],"simple-randomization","block-randomization","stratified-block-randomization","cluster-randomization","assign-factorial-runs","full-factorial","two-level-factorial","fractional-factorial","plackett-burman","central-composite","box-behnken","latin-hypercube","experimental-design","main","809229203e31273bda79d47a69dfeecc282f0c9f","kdense\u002Fexperimental-design","https:\u002F\u002Fgithub.com\u002FrhCat\u002FskillChip","https:\u002F\u002Fgithub.com\u002FrhCat","2026-07-31 22:22:14",{"source":7,"skillName":5,"commands":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add rhCat\u002FskillChip --skill \"Experimental Design (DOE & randomization)\"",{"tool":50,"command":51},"bunx","bunx skills add rhCat\u002FskillChip 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