[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MA2F6FZP0VA8QB83TGNBFQ":3,"skill-same:01M6MA2F6FZP0VA8QB83TGNBFQ":117},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":53,"linkOnly":54,"markdownContent":55,"markdownBody":56,"frontMatter":57,"refName":65,"commitSha":66,"skillDirectory":67,"forksCount":68,"repositoryHtmlUrl":69,"repositoryHomepageUrl":70,"ownerHtmlUrl":71,"ownerBlogUrl":72,"ownerDescription":73,"updatedAt":74,"install":75,"resources":88},"01M6MA2F6FZP0VA8QB83TGNBFQ","experimental-design",null,"Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects\u002Fsamples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster\u002Fgroup randomization, Latin squares, plate layouts, batch\u002Frun-order effects, replication vs. pseudoreplication, or sequential\u002Fadaptive\u002Fgroup-sequential designs. Trigger even for informal phrasings like \"how should I set up this experiment\", \"how do I avoid confounding\", \"what's the best way to test these 6 factors\", or \"assign these mice to conditions\". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1571,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52],"experimental design","design","power","analyzing data","effects","design of experiments","how to","scripts","statistics","sample","uv","up","write","read","Bash","how","analysis","SET","pandas","batch","run","2026-11-10 10:12:30",false,"---\nname: experimental-design\ndescription: Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects\u002Fsamples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster\u002Fgroup randomization, Latin squares, plate layouts, batch\u002Frun-order effects, replication vs. pseudoreplication, or sequential\u002Fadaptive\u002Fgroup-sequential designs. Trigger even for informal phrasings like \"how should I set up this experiment\", \"how do I avoid confounding\", \"what's the best way to test these 6 factors\", or \"assign these mice to conditions\". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.\nlicense: MIT\ncompatibility: Requires Python >=3.10. Scripts use numpy, pandas, and pyDOE3 (DOE matrices). Install with uv as shown below.\nallowed-tools: Read Write Edit Bash\nmetadata:\n  version: '1.2'\n  category: ideation-and-design\n  maintainer: Kalaris Labs\n  tags: experimental design, randomization, blocking, study design, statistics\n---\n\n# Experimental Design\n\n## Overview\n\nThe design of a study — how units are assigned to conditions, what is held constant, what is varied, and in what structure — determines what questions the data can answer. No analysis can rescue a confounded or pseudoreplicated design after the fact. This skill is about the decisions made *before* data collection: picking a design that isolates the effect of interest, randomizing to license causal claims, blocking to remove known nuisance variation, and structuring multi-factor experiments so effects are estimable rather than tangled together.\n\nThe three ideas behind almost every good design (Fisher's principles):\n- **Randomization** — assign treatments at random so that confounders, known and unknown, are balanced in expectation. This is what turns a comparison into a causal claim.\n- **Replication** — independent repetition at the right level, so you can estimate variability and your effects aren't artifacts of a single unit. The most common fatal error is **pseudoreplication**: counting repeated measurements on the same unit as independent replicates.\n- **Blocking \u002F local control** — group similar units (by batch, day, site, litter) and randomize within blocks, removing that nuisance variation from the error term instead of letting it inflate noise.\n\nThis skill helps you choose among design types, generate the actual randomization or DOE layout (with reproducible scripts), and avoid the structural mistakes that make data uninterpretable.\n\n## When to Use This Skill\n\n- Planning any comparative experiment or trial and deciding how to assign units\n- Randomizing subjects\u002Fsamples to arms (simple, blocked, stratified, or cluster)\n- Removing nuisance variation by blocking or stratification\n- Designing multi-factor experiments: full or fractional factorial, screening designs\n- Optimizing a response over continuous factors (response-surface designs)\n- Within-subject \u002F repeated-measures, crossover, split-plot, or Latin-square designs\n- Cluster- or group-randomized designs (sites, clinics, classrooms, litters)\n- Deciding the number and level of replicates and avoiding pseudoreplication\n- Sequential, group-sequential, or adaptive designs with interim analyses\n- Laying out plates\u002Fbatches and randomizing run order to defeat drift\n\n## Installation\n\n```bash\nuv pip install \"numpy>=1.26\" \"pandas>=2.0\" pyDOE3\n```\n\n`pyDOE3` is the maintained successor to pyDOE\u002FpyDOE2 and supplies factorial,\nfractional-factorial, Plackett-Burman, central-composite, Box-Behnken, and\nLatin-hypercube generators. The bundled scripts wrap it to return designs in real\nfactor units with named columns and randomized run order.\n\n---\n\n## Choosing a design\n\nStart from the question and the structure of your units, not from a favorite design.\n\n```\nWhat are you trying to learn?\n│\n├─ Compare a few predefined conditions (A vs B vs C)?\n│   ├─ Units independent, possibly with a known nuisance factor (day, batch, site)?\n│   │     → Completely randomized (no nuisance) or RANDOMIZED BLOCK design.\n│   ├─ Each unit can receive every condition in sequence (washout possible)?\n│   │     → CROSSOVER \u002F repeated-measures design (more power, watch carry-over).\n│   └─ You can only randomize groups, not individuals (schools, clinics)?\n│         → CLUSTER-randomized design (analyze at the cluster level; see pseudoreplication).\n│\n├─ Screen MANY factors (5+) to find the few that matter?\n│     → FRACTIONAL FACTORIAL or PLACKETT-BURMAN screening design.\n│\n├─ Quantify main effects AND interactions among a handful of factors?\n│     → FULL 2^k FACTORIAL design.\n│\n├─ Find the settings that OPTIMIZE a response (curvature matters)?\n│     → RESPONSE-SURFACE design: central composite or Box-Behnken.\n│\n└─ Explore a simulation\u002Fcomputer model over a continuous space?\n      → SPACE-FILLING design: Latin hypercube.\n```\n\nDetailed guidance per branch:\n- **Randomization, blocking, stratification, controls** → `references\u002Frandomization_and_blocking.md`\n- **Factorial, fractional-factorial, screening, response-surface, DOE concepts (aliasing, resolution)** → `references\u002Ffactorial_and_doe.md`\n- **Crossover, repeated-measures, split-plot, Latin-square, cluster, nested designs** → `references\u002Fdesign_types.md`\n- **Sequential, group-sequential, and adaptive designs (interim analyses)** → `references\u002Fsequential_and_adaptive.md`\n\n---\n\n## Generating the design\n\nTwo scripts produce ready-to-use, reproducible layouts. Run them from the skill's\n`scripts\u002F` directory or add it to `sys.path`. Everything is seeded so the exact\nschedule can be archived and regenerated — a requirement for trial registration\nand good lab practice.\n\n### Randomization \u002F allocation schedules — `scripts\u002Frandomization.py`\n\n```python\nfrom randomization import (\n    simple_randomization, block_randomization,\n    stratified_block_randomization, cluster_randomization,\n    assign_factorial_runs, arm_balance,\n)\n\n# Permuted blocks keep the arms balanced throughout enrollment (use for n \u003C ~100\n# or sequential intake — simple randomization can drift out of balance with small n)\nsched = block_randomization(n=60, arms=[\"treatment\", \"control\"], seed=42)\n\n# Balance a prognostic variable across arms by randomizing within each stratum\nsched = stratified_block_randomization({\"siteA\": 30, \"siteB\": 30},\n                                       arms=[\"drug\", \"placebo\"], ratio=(2, 1), seed=42)\n\n# Randomize whole clusters, not individuals (the cluster is the unit)\nsched = cluster_randomization([\"clinic1\", \"clinic2\", \"clinic3\", \"clinic4\"], seed=42)\n\narm_balance(sched)            # sanity-check the counts per arm\nsched.to_csv(\"allocation_schedule.csv\", index=False)\n```\n\nChoosing among them: **simple** is fine for large n but can produce imbalance with\nsmall n; **block** guarantees balance throughout; **stratified block** additionally\nbalances a known prognostic factor; **cluster** is mandatory when the intervention\nis delivered at a group level. See `references\u002Frandomization_and_blocking.md`.\n\n### DOE matrices — `scripts\u002Fdoe_designs.py`\n\n```python\nfrom doe_designs import (\n    full_factorial, two_level_factorial, fractional_factorial,\n    plackett_burman, central_composite, box_behnken, latin_hypercube,\n)\n\n# Factors as real-world (low, high) ranges -> design comes back in real units\nfactors = {\"temp_C\": (20, 60), \"conc_mM\": (1, 10), \"pH\": (6, 8)}\n\n# Full 2^3: all main effects + all interactions (8 runs), run order randomized\ndesign = two_level_factorial(factors, seed=42)\n\n# Screen 7 factors cheaply (main effects only)\nmany = {f\"factor_{i}\": (0, 1) for i in range(7)}\ndesign = plackett_burman(many, seed=42)\n\n# Optimize over 2 factors with curvature (response-surface)\ndesign = central_composite({\"temp_C\": (20, 60), \"conc_mM\": (1, 10)}, seed=42)\n\ndesign.to_csv(\"experimental_runs.csv\", index=False)\n```\n\nRun order is randomized by default so factors aren't confounded with time\u002Fdrift\n(machine warm-up, reagent aging). See `references\u002Ffactorial_and_doe.md` for picking\ngenerators, reading the alias structure, and choosing resolution.\n\n---\n\n## The mistakes that ruin studies\n\nThese are structural — they can't be fixed in analysis, only in design.\n\n1. **Pseudoreplication.** Treating repeated measurements of one unit as independent\n   replicates: 3 mice with 100 cells each is n = 3 (mice), not n = 300 (cells), for\n   any treatment applied to the mouse. The replicate must be at the level the\n   treatment is randomized. This single error invalidates a large share of published\n   experiments. Randomize and replicate at the right level; analyze with the nesting\n   respected (mixed model). See `references\u002Fdesign_types.md`.\n2. **Confounding by a nuisance variable.** Running all treatment samples on Monday\n   and all controls on Tuesday confounds treatment with day. Randomize across, or\n   block on, every nuisance factor you can name (batch, day, plate, technician,\n   instrument, position).\n3. **No or broken randomization.** Convenience assignment (first-come → treatment)\n   lets confounders sneak in. Use a seeded schedule and follow it.\n4. **No proper control.** Without a concurrent control (and, where relevant, a\n   vehicle\u002Fsham and blinding), you can't separate the treatment effect from time,\n   placebo, or handling effects.\n5. **Batch effects mistaken for biology.** In omics especially, process samples in a\n   randomized\u002Fblocked order across batches; never let batch align with the condition.\n6. **Edge\u002Fposition effects on plates.** Evaporation and thermal gradients make plate\n   edges differ. Randomize or block sample positions; don't put all controls in\n   column 1.\n7. **Aliasing ignored in fractional designs.** A low-resolution fractional factorial\n   confounds main effects with interactions; know your alias structure before\n   concluding a factor \"has no effect.\"\n8. **Optimizing without curvature.** A two-level factorial can't detect a curved\n   response; you'll miss an interior optimum. Use a response-surface design.\n\n---\n\n## Workflow\n\n1. **State the question, the unit, and the response.** What is randomized? What is\n   measured? At what level is a true independent replicate? This determines everything.\n2. **List nuisance factors** (batch, day, site, operator, position) — plan to block,\n   stratify, or randomize across each.\n3. **Pick the design** using the decision tree and reference files.\n4. **Decide replication** at the correct level (and get n from the\n   **statistical-power** skill for the chosen design).\n5. **Generate the layout** with `randomization.py` \u002F `doe_designs.py`, seeded.\n6. **Randomize run\u002Fprocessing order** and plate\u002Fbatch positions.\n7. **Document** the design, seed, and schedule (pre-register if possible) so the\n   analysis is confirmatory and the layout is auditable.\n8. **Match the analysis to the design** — blocks, strata, clusters, and nesting must\n   appear in the model (hand off to **statistical-analysis** \u002F **statsmodels**).\n\n---\n\n## Resources\n\n### Scripts\n- `scripts\u002Frandomization.py` — seeded allocation schedules: `simple_randomization`,\n  `block_randomization`, `stratified_block_randomization`, `cluster_randomization`,\n  `assign_factorial_runs`, `arm_balance`.\n- `scripts\u002Fdoe_designs.py` — DOE matrices in real units: `full_factorial`,\n  `two_level_factorial`, `fractional_factorial`, `plackett_burman`,\n  `central_composite`, `box_behnken`, `latin_hypercube`.\n\n### References\n- `references\u002Frandomization_and_blocking.md` — randomization methods, blocking,\n  stratification, controls, blinding, batch\u002Fplate layout.\n- `references\u002Ffactorial_and_doe.md` — factorial and fractional designs, resolution\n  and aliasing, screening, and response-surface methodology.\n- `references\u002Fdesign_types.md` — completely randomized, randomized block, crossover,\n  repeated-measures, split-plot, Latin-square, cluster, and nested designs; the\n  pseudoreplication problem in depth.\n- `references\u002Fsequential_and_adaptive.md` — group-sequential designs, alpha spending,\n  interim stopping, and adaptive sample-size re-estimation.\n\n### Related skills\n- **statistical-power** — required sample size \u002F power for the design you've chosen.\n- **statistical-analysis** — running and reporting the analysis after collection.\n- **statsmodels** \u002F **pymc** — fitting the models the design implies.\n\n### Key references\n- Fisher, R. A. (1935). *The Design of Experiments*.\n- Montgomery, D. C. (2019). *Design and Analysis of Experiments* (10th ed.).\n- Hurlbert, S. H. (1984). Pseudoreplication and the design of ecological field\n  experiments. *Ecological Monographs*, 54(2), 187–211.\n- Lazic, S. E. (2016). *Experimental Design for Laboratory Biologists*.\n\n## Agent operating procedure\n\n1. **Check the environment.** Clarify the research goal, constraints (budget, time, ethics) and the decision the user needs to make.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Propose a short shortlist of options with explicit assumptions before expanding any of them.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Assumptions are stated, alternatives are compared on explicit criteria, and statistical plans include power and analysis choices.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| A design choice depends on unknown effect sizes or costs | Present scenarios (optimistic, expected, pessimistic) instead of a single guess. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Label speculation as speculation; separate established evidence from new hypotheses.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n","# Experimental Design\n\n## Overview\n\nThe design of a study — how units are assigned to conditions, what is held constant, what is varied, and in what structure — determines what questions the data can answer. No analysis can rescue a confounded or pseudoreplicated design after the fact. This skill is about the decisions made *before* data collection: picking a design that isolates the effect of interest, randomizing to license causal claims, blocking to remove known nuisance variation, and structuring multi-factor experiments so effects are estimable rather than tangled together.\n\nThe three ideas behind almost every good design (Fisher's principles):\n- **Randomization** — assign treatments at random so that confounders, known and unknown, are balanced in expectation. This is what turns a comparison into a causal claim.\n- **Replication** — independent repetition at the right level, so you can estimate variability and your effects aren't artifacts of a single unit. The most common fatal error is **pseudoreplication**: counting repeated measurements on the same unit as independent replicates.\n- **Blocking \u002F local control** — group similar units (by batch, day, site, litter) and randomize within blocks, removing that nuisance variation from the error term instead of letting it inflate noise.\n\nThis skill helps you choose among design types, generate the actual randomization or DOE layout (with reproducible scripts), and avoid the structural mistakes that make data uninterpretable.\n\n## When to Use This Skill\n\n- Planning any comparative experiment or trial and deciding how to assign units\n- Randomizing subjects\u002Fsamples to arms (simple, blocked, stratified, or cluster)\n- Removing nuisance variation by blocking or stratification\n- Designing multi-factor experiments: full or fractional factorial, screening designs\n- Optimizing a response over continuous factors (response-surface designs)\n- Within-subject \u002F repeated-measures, crossover, split-plot, or Latin-square designs\n- Cluster- or group-randomized designs (sites, clinics, classrooms, litters)\n- Deciding the number and level of replicates and avoiding pseudoreplication\n- Sequential, group-sequential, or adaptive designs with interim analyses\n- Laying out plates\u002Fbatches and randomizing run order to defeat drift\n\n## Installation\n\n```bash\nuv pip install \"numpy>=1.26\" \"pandas>=2.0\" pyDOE3\n```\n\n`pyDOE3` is the maintained successor to pyDOE\u002FpyDOE2 and supplies factorial,\nfractional-factorial, Plackett-Burman, central-composite, Box-Behnken, and\nLatin-hypercube generators. The bundled scripts wrap it to return designs in real\nfactor units with named columns and randomized run order.\n\n---\n\n## Choosing a design\n\nStart from the question and the structure of your units, not from a favorite design.\n\n```\nWhat are you trying to learn?\n│\n├─ Compare a few predefined conditions (A vs B vs C)?\n│   ├─ Units independent, possibly with a known nuisance factor (day, batch, site)?\n│   │     → Completely randomized (no nuisance) or RANDOMIZED BLOCK design.\n│   ├─ Each unit can receive every condition in sequence (washout possible)?\n│   │     → CROSSOVER \u002F repeated-measures design (more power, watch carry-over).\n│   └─ You can only randomize groups, not individuals (schools, clinics)?\n│         → CLUSTER-randomized design (analyze at the cluster level; see pseudoreplication).\n│\n├─ Screen MANY factors (5+) to find the few that matter?\n│     → FRACTIONAL FACTORIAL or PLACKETT-BURMAN screening design.\n│\n├─ Quantify main effects AND interactions among a handful of factors?\n│     → FULL 2^k FACTORIAL design.\n│\n├─ Find the settings that OPTIMIZE a response (curvature matters)?\n│     → RESPONSE-SURFACE design: central composite or Box-Behnken.\n│\n└─ Explore a simulation\u002Fcomputer model over a continuous space?\n      → SPACE-FILLING design: Latin hypercube.\n```\n\nDetailed guidance per branch:\n- **Randomization, blocking, stratification, controls** → `references\u002Frandomization_and_blocking.md`\n- **Factorial, fractional-factorial, screening, response-surface, DOE concepts (aliasing, resolution)** → `references\u002Ffactorial_and_doe.md`\n- **Crossover, repeated-measures, split-plot, Latin-square, cluster, nested designs** → `references\u002Fdesign_types.md`\n- **Sequential, group-sequential, and adaptive designs (interim analyses)** → `references\u002Fsequential_and_adaptive.md`\n\n---\n\n## Generating the design\n\nTwo scripts produce ready-to-use, reproducible layouts. Run them from the skill's\n`scripts\u002F` directory or add it to `sys.path`. Everything is seeded so the exact\nschedule can be archived and regenerated — a requirement for trial registration\nand good lab practice.\n\n### Randomization \u002F allocation schedules — `scripts\u002Frandomization.py`\n\n```python\nfrom randomization import (\n    simple_randomization, block_randomization,\n    stratified_block_randomization, cluster_randomization,\n    assign_factorial_runs, arm_balance,\n)\n\n# Permuted blocks keep the arms balanced throughout enrollment (use for n \u003C ~100\n# or sequential intake — simple randomization can drift out of balance with small n)\nsched = block_randomization(n=60, arms=[\"treatment\", \"control\"], seed=42)\n\n# Balance a prognostic variable across arms by randomizing within each stratum\nsched = stratified_block_randomization({\"siteA\": 30, \"siteB\": 30},\n                                       arms=[\"drug\", \"placebo\"], ratio=(2, 1), seed=42)\n\n# Randomize whole clusters, not individuals (the cluster is the unit)\nsched = cluster_randomization([\"clinic1\", \"clinic2\", \"clinic3\", \"clinic4\"], seed=42)\n\narm_balance(sched)            # sanity-check the counts per arm\nsched.to_csv(\"allocation_schedule.csv\", index=False)\n```\n\nChoosing among them: **simple** is fine for large n but can produce imbalance with\nsmall n; **block** guarantees balance throughout; **stratified block** additionally\nbalances a known prognostic factor; **cluster** is mandatory when the intervention\nis delivered at a group level. See `references\u002Frandomization_and_blocking.md`.\n\n### DOE matrices — `scripts\u002Fdoe_designs.py`\n\n```python\nfrom doe_designs import (\n    full_factorial, two_level_factorial, fractional_factorial,\n    plackett_burman, central_composite, box_behnken, latin_hypercube,\n)\n\n# Factors as real-world (low, high) ranges -> design comes back in real units\nfactors = {\"temp_C\": (20, 60), \"conc_mM\": (1, 10), \"pH\": (6, 8)}\n\n# Full 2^3: all main effects + all interactions (8 runs), run order randomized\ndesign = two_level_factorial(factors, seed=42)\n\n# Screen 7 factors cheaply (main effects only)\nmany = {f\"factor_{i}\": (0, 1) for i in range(7)}\ndesign = plackett_burman(many, seed=42)\n\n# Optimize over 2 factors with curvature (response-surface)\ndesign = central_composite({\"temp_C\": (20, 60), \"conc_mM\": (1, 10)}, seed=42)\n\ndesign.to_csv(\"experimental_runs.csv\", index=False)\n```\n\nRun order is randomized by default so factors aren't confounded with time\u002Fdrift\n(machine warm-up, reagent aging). See `references\u002Ffactorial_and_doe.md` for picking\ngenerators, reading the alias structure, and choosing resolution.\n\n---\n\n## The mistakes that ruin studies\n\nThese are structural — they can't be fixed in analysis, only in design.\n\n1. **Pseudoreplication.** Treating repeated measurements of one unit as independent\n   replicates: 3 mice with 100 cells each is n = 3 (mice), not n = 300 (cells), for\n   any treatment applied to the mouse. The replicate must be at the level the\n   treatment is randomized. This single error invalidates a large share of published\n   experiments. Randomize and replicate at the right level; analyze with the nesting\n   respected (mixed model). See `references\u002Fdesign_types.md`.\n2. **Confounding by a nuisance variable.** Running all treatment samples on Monday\n   and all controls on Tuesday confounds treatment with day. Randomize across, or\n   block on, every nuisance factor you can name (batch, day, plate, technician,\n   instrument, position).\n3. **No or broken randomization.** Convenience assignment (first-come → treatment)\n   lets confounders sneak in. Use a seeded schedule and follow it.\n4. **No proper control.** Without a concurrent control (and, where relevant, a\n   vehicle\u002Fsham and blinding), you can't separate the treatment effect from time,\n   placebo, or handling effects.\n5. **Batch effects mistaken for biology.** In omics especially, process samples in a\n   randomized\u002Fblocked order across batches; never let batch align with the condition.\n6. **Edge\u002Fposition effects on plates.** Evaporation and thermal gradients make plate\n   edges differ. Randomize or block sample positions; don't put all controls in\n   column 1.\n7. **Aliasing ignored in fractional designs.** A low-resolution fractional factorial\n   confounds main effects with interactions; know your alias structure before\n   concluding a factor \"has no effect.\"\n8. **Optimizing without curvature.** A two-level factorial can't detect a curved\n   response; you'll miss an interior optimum. Use a response-surface design.\n\n---\n\n## Workflow\n\n1. **State the question, the unit, and the response.** What is randomized? What is\n   measured? At what level is a true independent replicate? This determines everything.\n2. **List nuisance factors** (batch, day, site, operator, position) — plan to block,\n   stratify, or randomize across each.\n3. **Pick the design** using the decision tree and reference files.\n4. **Decide replication** at the correct level (and get n from the\n   **statistical-power** skill for the chosen design).\n5. **Generate the layout** with `randomization.py` \u002F `doe_designs.py`, seeded.\n6. **Randomize run\u002Fprocessing order** and plate\u002Fbatch positions.\n7. **Document** the design, seed, and schedule (pre-register if possible) so the\n   analysis is confirmatory and the layout is auditable.\n8. **Match the analysis to the design** — blocks, strata, clusters, and nesting must\n   appear in the model (hand off to **statistical-analysis** \u002F **statsmodels**).\n\n---\n\n## Resources\n\n### Scripts\n- `scripts\u002Frandomization.py` — seeded allocation schedules: `simple_randomization`,\n  `block_randomization`, `stratified_block_randomization`, `cluster_randomization`,\n  `assign_factorial_runs`, `arm_balance`.\n- `scripts\u002Fdoe_designs.py` — DOE matrices in real units: `full_factorial`,\n  `two_level_factorial`, `fractional_factorial`, `plackett_burman`,\n  `central_composite`, `box_behnken`, `latin_hypercube`.\n\n### References\n- `references\u002Frandomization_and_blocking.md` — randomization methods, blocking,\n  stratification, controls, blinding, batch\u002Fplate layout.\n- `references\u002Ffactorial_and_doe.md` — factorial and fractional designs, resolution\n  and aliasing, screening, and response-surface methodology.\n- `references\u002Fdesign_types.md` — completely randomized, randomized block, crossover,\n  repeated-measures, split-plot, Latin-square, cluster, and nested designs; the\n  pseudoreplication problem in depth.\n- `references\u002Fsequential_and_adaptive.md` — group-sequential designs, alpha spending,\n  interim stopping, and adaptive sample-size re-estimation.\n\n### Related skills\n- **statistical-power** — required sample size \u002F power for the design you've chosen.\n- **statistical-analysis** — running and reporting the analysis after collection.\n- **statsmodels** \u002F **pymc** — fitting the models the design implies.\n\n### Key references\n- Fisher, R. A. (1935). *The Design of Experiments*.\n- Montgomery, D. C. (2019). *Design and Analysis of Experiments* (10th ed.).\n- Hurlbert, S. H. (1984). Pseudoreplication and the design of ecological field\n  experiments. *Ecological Monographs*, 54(2), 187–211.\n- Lazic, S. E. (2016). *Experimental Design for Laboratory Biologists*.\n\n## Agent operating procedure\n\n1. **Check the environment.** Clarify the research goal, constraints (budget, time, ethics) and the decision the user needs to make.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Propose a short shortlist of options with explicit assumptions before expanding any of them.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Assumptions are stated, alternatives are compared on explicit criteria, and statistical plans include power and analysis choices.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| A design choice depends on unknown effect sizes or costs | Present scenarios (optimistic, expected, pessimistic) instead of a single guess. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Label speculation as speculation; separate established evidence from new hypotheses.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n",{"name":5,"license":15,"metadata":58,"description":7,"allowed-tools":63,"compatibility":64},{"tags":59,"version":60,"category":61,"maintainer":62},"experimental design, randomization, blocking, study design, statistics","1.2","ideation-and-design","Kalaris Labs","Read Write Edit Bash","Requires Python >=3.10. Scripts use numpy, pandas, and pyDOE3 (DOE matrices). Install with uv as shown below.","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design",490,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-14 02:12:02",{"source":8,"skillName":5,"commands":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill experimental-design",{"tool":81,"command":82},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill experimental-design",{"tool":84,"command":85},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill experimental-design","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design\u002FSKILL.md",true,[89,95,99,103,107,113],{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fdesign_types.md","REFERENCE","参考文档",7323,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design\u002Freferences\u002Fdesign_types.md",{"path":96,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Ffactorial_and_doe.md",7073,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design\u002Freferences\u002Ffactorial_and_doe.md",{"path":100,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Frandomization_and_blocking.md",6360,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design\u002Freferences\u002Frandomization_and_blocking.md",{"path":104,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fsequential_and_adaptive.md",5488,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design\u002Freferences\u002Fsequential_and_adaptive.md",{"path":108,"resourceType":109,"resourceTypeDesc":110,"sizeBytes":111,"sourceHtmlUrl":112},"scripts\u002Fdoe_designs.py","SCRIPT","脚本",8048,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design\u002Fscripts\u002Fdoe_designs.py",{"path":114,"resourceType":109,"resourceTypeDesc":110,"sizeBytes":115,"sourceHtmlUrl":116},"scripts\u002Frandomization.py",7401,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fexperimental-design\u002Fscripts\u002Frandomization.py",[118,127,134,141,167,192],{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":122,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":126},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":122,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":133,"publishedAt":126},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":122,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":140,"publishedAt":126},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":122,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":147,"publishedAt":53},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[148,149,150,151,152,153,154,155,156,33,157,158,159,160,161,162,163,164,165,166],"search","deja search","never","error","ready","memory","re","decide","fix","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":168,"name":169,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":122,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":53},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[174,175,176,162,177,178,179,180,181,182,183,184,185,186,153,187,188,189,190,191],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":122,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":205,"publishedAt":53},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[198,201,202],{"categoryNo":199,"categoryName":200,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":203,"categoryName":204,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[194,206,207,208,209,210,211,188,212,154,213,214,215,216,158,217,218,219,220,221],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]