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Experimental Design

K-DenseCommunityCreated by K-Dense Inc.

  • Skill
  • Workflow
  • Science and research
  • MIT
  • Low risk
  • Has scripts
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From the author

Designs 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/samples 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/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-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.
— the description by K-Dense Inc.

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Experimental design is the critical phase of research that occurs before data collection. This skill provides a framework for structuring experiments to ensure results are valid and interpretable. It emphasizes Fisher's principles—randomization, replication, and blocking—to isolate effects of interest from nuisance variation. By using this skill, researchers can avoid common structural pitfalls like pseudoreplication and confounding, ensuring that the chosen design matches the research question and the unit of analysis.

What it does

  • Provides a decision tree to select the appropriate experimental design based on research goals.
  • Generates reproducible randomization and allocation schedules for subjects or samples.
  • Creates Design of Experiments (DOE) matrices for factorial, fractional-factorial, and response-surface studies.
  • Offers guidance on blocking and stratification to remove nuisance variation.
  • Helps identify and prevent structural errors like pseudoreplication and aliasing.
  • Supports complex designs including crossover, split-plot, and cluster-randomized studies.

When to use it

  • When planning any comparative experiment or clinical trial.
  • When you need to assign subjects to groups while balancing nuisance factors.
  • When designing multi-factor experiments to screen for significant effects or optimize responses.
  • When you need to determine the correct level of replication to avoid pseudoreplication.
  • When setting up plate layouts or run orders to minimize drift and batch effects.

Advantages

  • Prevents unfixable structural errors that invalidate data analysis.
  • Increases statistical precision by effectively managing nuisance variables.
  • Ensures causal claims are supported by proper randomization.
  • Provides reproducible, seeded layouts for consistent study execution.
  • Optimizes experimental efficiency by reducing the number of runs required.

Usage examples

“How should I assign 60 mice to two treatment groups while accounting for two different study sites?”
The agent will recommend a stratified block randomization approach, providing a script to generate the schedule that balances the treatment arms within each site.
“I need to test 5 factors to see which ones affect my yield. What is the best way to set this up?”
The agent will suggest a fractional factorial or Plackett-Burman screening design to efficiently identify significant factors while minimizing the total number of experimental runs.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/k-dense-ai/experimental-design/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip experimental-design.zip -d .agents/skills

Documentation by K-Dense Inc.

Experimental Design

Overview

The 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.

The three ideas behind almost every good design (Fisher's principles):

  • 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.

  • 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.

  • Blocking / 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.

This 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.

When to Use This Skill

  • Planning any comparative experiment or trial and deciding how to assign units

  • Randomizing subjects/samples to arms (simple, blocked, stratified, or cluster)

  • Removing nuisance variation by blocking or stratification

  • Designing multi-factor experiments: full or fractional factorial, screening designs

  • Optimizing a response over continuous factors (response-surface designs)

  • Within-subject / repeated-measures, crossover, split-plot, or Latin-square designs

  • Cluster- or group-randomized designs (sites, clinics, classrooms, litters)

  • Deciding the number and level of replicates and avoiding pseudoreplication

  • Sequential, group-sequential, or adaptive designs with interim analyses

  • Laying out plates/batches and randomizing run order to defeat drift

Installation

uv venv --python 3.13 .venv-design
uv pip install --python .venv-design/bin/python "numpy==2.5.3" "pandas==3.0.6" "pydoe==1.5.0"

pyDOE3 was archived in May 2026; active development returned to pydoe. The bundled wrappers target pydoe==1.5.0, imported as lowercase pydoe, and return designs in real factor units. All seven DOE wrappers and both script demos were executed with Python 3.13, NumPy 2.5.3, pandas 3.0.6, and SciPy 1.18.1. On Windows, the environment interpreter is .venv-design/Scripts/python.exe. Archive the environment versions and exported schedules: a seed alone does not promise identical output across package or script upgrades.

Choosing a design

Start from the question and the structure of your units, not from a favorite design.

What are you trying to learn?
│
├─ Compare a few predefined conditions (A vs B vs C)?
│   ├─ Units independent, possibly with a known nuisance factor (day, batch, site)?
│   │     → Completely randomized (no nuisance) or RANDOMIZED BLOCK design.
│   ├─ Each unit can receive every condition in sequence (washout possible)?
│   │     → CROSSOVER / repeated-measures design (watch carry-over and correlation).
│   └─ You can only randomize groups, not individuals (schools, clinics)?
│         → CLUSTER-randomized design (account for clustering; see pseudoreplication).
│
├─ Screen MANY factors (5+) to find the few that matter?
│     → FRACTIONAL FACTORIAL or PLACKETT-BURMAN screening design.
│
├─ Quantify main effects AND interactions among a handful of factors?
│     → FULL 2^k FACTORIAL design.
│
├─ Find the settings that OPTIMIZE a response (curvature matters)?
│     → RESPONSE-SURFACE design: central composite or Box-Behnken.
│
└─ Explore a simulation/computer model over a continuous space?
      → SPACE-FILLING design: Latin hypercube.

Detailed guidance per branch:

  • Randomization, blocking, stratification, controls → references/randomization_and_blocking.md

  • Factorial, fractional-factorial, screening, response-surface, DOE concepts (aliasing, resolution) → references/factorial_and_doe.md

  • Crossover, repeated-measures, split-plot, Latin-square, cluster, nested designs → references/design_types.md

  • Sequential, group-sequential, and adaptive designs (interim analyses) → references/sequential_and_adaptive.md

Generating the design

Two scripts produce ready-to-use, reproducible layouts. Run them from the skill's scripts/ directory or add it to sys.path. Seeds support reproducible layouts within the recorded software environment. The default seed is for demonstrations; choose and securely record a study-specific seed for a real allocation. These helpers do not implement an enrollment system or conceal future assignments from recruiters.

Randomization / allocation schedules — scripts/randomization.py
from randomization import (
    simple_randomization, block_randomization,
    stratified_block_randomization, cluster_randomization,
    assign_factorial_runs, arm_balance,
)

# Permuted blocks balance at completed-block boundaries.
# A partial final block or interim prefix need not have the requested ratio.
sched = block_randomization(n=60, arms=["treatment", "control"], seed=42)

# Balance a prognostic variable across arms by randomizing within each stratum
sched = stratified_block_randomization({"siteA": 30, "siteB": 30},
                                       arms=["drug", "placebo"], ratio=(2, 1), seed=42)

# Randomize whole clusters, not individuals (the cluster is the unit)
sched = cluster_randomization(["clinic1", "clinic2", "clinic3", "clinic4"], seed=42)

arm_balance(sched)            # sanity-check the counts per arm
sched.to_csv("allocation_schedule.csv", index=False)

Choosing among them: simple is fine for large n but can produce imbalance with small n; block enforces the ratio within each complete block; stratified block does this independently within each stratum; cluster is mandatory when the intervention is delivered at a group level. See references/randomization_and_blocking.md.

For a sequence of stratum labels, unit_id is the original one-based input position, including when strata are interleaved. Join the exported schedule to your subject IDs using that position and verify every ID exactly once. A dict input creates grouped planning slots rather than assigning an existing roster. Missing stratum labels and duplicate cluster IDs are rejected.

DOE matrices — scripts/doe_designs.py
from doe_designs import (
    full_factorial, two_level_factorial, fractional_factorial,
    plackett_burman, central_composite, box_behnken, latin_hypercube,
)

# Factors as real-world (low, high) ranges -> design comes back in real units
factors = {"temp_C": (20, 60), "conc_mM": (1, 10), "pH": (6, 8)}

# Full 2^3: all main effects + all interactions (8 runs), run order randomized
design = two_level_factorial(factors, seed=42)

# Screen 7 factors cheaply (main effects only)
many = {f"factor_{i}": (0, 1) for i in range(7)}
design = plackett_burman(many, seed=42)

# Optimize over 2 factors with curvature (response-surface)
design = central_composite(
    {"temp_C": (20, 60), "conc_mM": (1, 10)},
    center=(2, 2), face="inscribed", seed=42,
)

design.to_csv("experimental_runs.csv", index=False)

Before running a central composite design, inspect each factor's actual minimum and maximum. The default face="circumscribed" places axial points beyond the supplied low/high factorial settings; those arguments are not hard operating limits. If the stated ranges are physical limits, choose face="inscribed" or face="faced", then recheck all combinations. Do not clip out-of-range rows: clipping changes the design geometry and its statistical properties. See the NIST CCD comparison.

The CCD example reserves four center runs; execute them independently to estimate pure error. The wrapper default has only one. Confirm all settings are feasible, the intended model matrix has full rank, and independent replication supplies residual degrees of freedom. Center points check aggregate curvature; they do not identify each quadratic term without axial or other suitable runs.

DOE run order is randomized by default (Latin hypercube defaults to no added run_order) so factors are not systematically aligned with time/drift (machine warm-up, reagent aging). See references/factorial_and_doe.md for picking generators, reading the alias structure, and choosing resolution. These wrappers shuffle globally: for split plots, plates, or a CCD run in separate batches, retain block IDs and randomize only within the permitted structure.

The mistakes that ruin studies

These are structural — they can't be fixed in analysis, only in design.

  1. Pseudoreplication. Treating repeated measurements of one unit as independent replicates: 3 mice with 100 cells each is n = 3 (mice), not n = 300 (cells), for any treatment applied to the mouse. The replicate must be at the level the treatment is randomized. This single error invalidates a large share of published experiments. Randomize and replicate at the right level; analyze with the nesting respected (mixed model). See references/design_types.md.

  2. Confounding by a nuisance variable. Running all treatment samples on Monday and all controls on Tuesday confounds treatment with day. Randomize across, or block on, every nuisance factor you can name (batch, day, plate, technician, instrument, position).

  3. No or broken randomization. Convenience assignment (first-come → treatment) lets confounders sneak in. Use a seeded schedule and follow it.

  4. No proper control. Without a concurrent control (and, where relevant, a vehicle/sham and blinding), you can't separate the treatment effect from time, placebo, or handling effects.

  5. Batch effects mistaken for biology. In omics especially, process samples in a randomized/blocked order across batches; never let batch align with the condition.

  6. Edge/position effects on plates. Evaporation and thermal gradients make plate edges differ. Randomize or block sample positions; don't put all controls in column 1.

  7. Aliasing ignored in fractional designs. A low-resolution fractional factorial confounds main effects with interactions; know your alias structure before concluding a factor "has no effect."

  8. Optimizing without curvature. A two-level factorial alone cannot estimate pure quadratic terms; you'll miss an interior optimum. Use a response-surface design.

Workflow

  1. State the question, the unit, and the response. What is randomized? What is measured? At what level is a true independent replicate? This determines everything.

  2. List nuisance factors (batch, day, site, operator, position) — plan to block, stratify, or randomize across each.

  3. Pick the design using the decision tree and reference files.

  4. Decide replication at the correct level (and get n from the statistical-power skill for the chosen design).

  5. Generate the layout with randomization.py / doe_designs.py, seeded.

  6. Randomize run/processing order and plate/batch positions within the design restrictions; verify roster IDs, completed-block balance, range limits, and rank.

  7. Document the design, software versions, seed, and schedule. Pre-specify the analysis; restrict access to the seed and future assignments during enrollment.

  8. Match the analysis to the design — blocks, strata, clusters, and nesting must appear in the model (hand off to statistical-analysis / statsmodels).

Resources

Scripts

  • scripts/randomization.py — seeded allocation schedules: simple_randomization, block_randomization, stratified_block_randomization, cluster_randomization, assign_factorial_runs, arm_balance.

  • scripts/doe_designs.py — DOE matrices in real units: full_factorial, two_level_factorial, fractional_factorial, plackett_burman, central_composite, box_behnken, latin_hypercube.

References

  • references/randomization_and_blocking.md — randomization methods, blocking, stratification, controls, blinding, batch/plate layout.

  • references/factorial_and_doe.md — factorial and fractional designs, resolution and aliasing, screening, and response-surface methodology.

  • references/design_types.md — completely randomized, randomized block, crossover, repeated-measures, split-plot, Latin-square, cluster, and nested designs; the pseudoreplication problem in depth.

  • references/sequential_and_adaptive.md — group-sequential designs, alpha spending, interim stopping, and adaptive sample-size re-estimation.

Related skills

  • statistical-power — required sample size / power for the design you've chosen.

  • statistical-analysis — running and reporting the analysis after collection.

  • statsmodels / pymc — fitting the models the design implies.

Key references

  • Fisher, R. A. (1935). The Design of Experiments.

  • Montgomery, D. C. (2019). Design and Analysis of Experiments (10th ed.).

  • Hurlbert, S. H. (1984). Pseudoreplication and the design of ecological field experiments. Ecological Monographs, 54(2), 187–211.

  • Lazic, S. E. (2016). Experimental Design for Laboratory Biologists.

Citing Scientific Agent Skills

This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1. When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.

Files

scripts/randomization.py

"""Reproducible randomization / allocation schedules for experiments and trials.

Randomization is what licenses causal inference: it breaks the link between
treatment assignment and any confounder, measured or not. But "I shuffled it"
is not enough — the *method* matters (simple vs. blocked vs. stratified) and the
schedule must be reproducible (seeded) and auditable. This module produces
allocation tables as pandas DataFrames, with a fixed seed so the exact schedule
can be regenerated and archived.

Functions:
  simple_randomization        independent coin-flip per unit (can yield imbalance)
  block_randomization         permuted blocks -> balance at complete-block boundaries
  stratified_block_randomization   blocks within strata -> balance per subgroup
  cluster_randomization       randomize whole clusters (sites/classes), not units
  assign_factorial_runs       randomize the RUN ORDER of a list of design rows

Requires: numpy, pandas.
"""

from __future__ import annotations

import operator

import numpy as np
import pandas as pd


def _count(value, name, minimum=0):
    """Validate counts before allocation; zero-sized blocks would never advance."""
    try:
        count = operator.index(value)
    except TypeError:
        raise ValueError(f"{name} must be an integer >= {minimum}") from None
    if isinstance(value, (bool, np.bool_)) or count < minimum:
        raise ValueError(f"{name} must be an integer >= {minimum}")
    return count


def _labels(values, name, unique=False, minimum=0):
    values = list(values)
    try:
        valid = (len(values) >= minimum
                 and not pd.Series(values, dtype=object).isna().any()
                 and all(isinstance(hash(value), int) for value in values))
        if unique:
            valid = valid and len(set(values)) == len(values)
    except (TypeError, ValueError):
        valid = False
    if not valid:
        raise ValueError(f"{name} must contain non-missing, hashable labels"
                         + (" that are unique" if unique else "")
                         + f" (at least {minimum})")
    return values


def _normalize_ratio(arms, ratio):
    """Turn arms + integer ratio into a block template list, e.g.
    arms=['A','B'], ratio=(2,1) -> ['A','A','B']."""
    arms = _labels(arms, "arms", unique=True, minimum=2)
    if ratio is None:
        ratio = [1] * len(arms)
    if len(ratio) != len(arms):
        raise ValueError("ratio must have one entry per arm")
    try:
        invalid = any(isinstance(r, (bool, np.bool_)) or r <= 0 or int(r) != r
                      for r in ratio)
    except (TypeError, ValueError, OverflowError):
        invalid = True
    if invalid:
        raise ValueError("ratio entries must be positive integers")
    template = []
    for arm, r in zip(arms, ratio):
        template += [arm] * int(r)
    return template


def simple_randomization(n, arms=("treatment", "control"), ratio=None, seed=0):
    """Independent random assignment per unit.

    Simplest method; with small n it can produce noticeable arm-size imbalance
    (like flipping few coins). Fine for large n. Use block_randomization when you
    need tighter control of arm counts or have sequential enrollment.
    """
    n = _count(n, "n")
    rng = np.random.default_rng(seed)
    arms = list(arms)
    template = _normalize_ratio(arms, ratio)
    probs = np.array([template.count(a) for a in arms], dtype=float)
    probs /= probs.sum()
    # Preserve labels when arms mix numeric and string values.
    labels = np.empty(len(arms), dtype=object)
    labels[:] = arms
    assign = rng.choice(labels, size=n, p=probs)
    return pd.DataFrame({"unit_id": np.arange(1, n + 1), "arm": assign})


def block_randomization(n, arms=("treatment", "control"), block_size=None,
                        ratio=None, seed=0):
    """Permuted-block randomization: exact ratio at complete-block boundaries.

    Within each block every arm appears in the specified ratio; the order inside
    a block is shuffled. block_size must be a multiple of sum(ratio). Leaving it
    None picks a small valid size. Partial blocks need not balance. This helper
    uses a fixed block size; it does not implement variable block sizes or
    allocation concealment. Keep the seed/schedule away from enrolling staff.
    """
    n = _count(n, "n")
    rng = np.random.default_rng(seed)
    arms = list(arms)
    template = _normalize_ratio(arms, ratio)
    unit = len(template)
    if block_size is None:
        block_size = unit * 2  # two of each ratio-unit per block
    block_size = _count(block_size, "block_size", minimum=1)
    if block_size % unit != 0:
        raise ValueError(f"block_size ({block_size}) must be a multiple of "
                         f"sum(ratio)={unit}")
    reps = block_size // unit

    out = []
    block_id = 0
    while len(out) < n:
        block = template * reps
        rng.shuffle(block)
        for a in block:
            out.append((len(out) + 1, block_id, a))
        block_id += 1
    df = pd.DataFrame(out[:n], columns=["unit_id", "block", "arm"])
    return df


def stratified_block_randomization(strata, arms=("treatment", "control"),
                                   block_size=None, ratio=None, seed=0):
    """Block-randomize independently within each stratum.

    Use when a prognostic variable (site, sex, disease stage) must be balanced
    across arms. `strata` is a dict {stratum_label: n_in_that_stratum} or a
    sequence of stratum labels (one per unit). Sequence inputs retain their input
    order: unit_id is the original one-based position. Map real subject IDs to
    these positions before use. Dict inputs produce grouped planning schedules.
    Balance is exact only for complete blocks within each stratum.
    """
    arms = list(arms)
    _normalize_ratio(arms, ratio)
    seed = _count(seed, "seed")
    if isinstance(strata, dict):
        _labels(strata, "strata")
        items = [(label, _count(count, "stratum count"), None)
                 for label, count in strata.items()]
    else:  # sequence of labels, with original unit identities retained
        labels = _labels(strata, "strata")
        positions = {}
        for position, label in enumerate(labels, start=1):
            positions.setdefault(label, []).append(position)
        items = [(label, len(ids), ids) for label, ids in positions.items()]

    frames = []
    next_id = 1
    for i, (label, count, ids) in enumerate(items):
        df = block_randomization(count, arms=arms, block_size=block_size,
                                 ratio=ratio, seed=seed + 1 + i)
        df["unit_id"] = ids if ids is not None else np.arange(next_id, next_id + count)
        next_id += count
        df.insert(1, "stratum", pd.Series([label] * count, dtype=object))
        frames.append(df)
    if not frames:
        return pd.DataFrame(columns=["unit_id", "stratum", "block", "arm"])
    out = pd.concat(frames, ignore_index=True)
    return out.sort_values("unit_id").reset_index(drop=True)


def cluster_randomization(clusters, arms=("treatment", "control"), ratio=None,
                          block_size=None, seed=0):
    """Randomize whole clusters (clinics, schools, litters) to arms.

    The cluster — not the individual — is the unit of randomization AND the unit
    of analysis-level independence. `clusters` is a list of cluster IDs (or an int
    count). Returns one row per cluster. Analyze with a method that accounts for
    clustering (mixed model / GEE); treating members as independent is
    pseudoreplication. Uses blocking across clusters for arm balance.
    """
    if isinstance(clusters, (int, np.integer)):
        clusters = [f"cluster_{i+1}" for i in range(_count(clusters, "clusters"))]
    clusters = _labels(clusters, "clusters", unique=True)
    df = block_randomization(len(clusters), arms=arms, ratio=ratio,
                             block_size=block_size, seed=seed)
    df = df.drop(columns=["unit_id"])
    df.insert(0, "cluster_id", clusters)
    return df


def assign_factorial_runs(design_df, seed=0):
    """Randomize the execution order of a set of design runs (e.g. a DOE matrix).

    Run order matters: executing a factorial design in a systematic order
    confounds the factors with time/drift (the machine warms up, the reagent
    degrades). Randomizing run order protects against that. Returns the design
    with a 'run_order' column and rows sorted by it.
    """
    rng = np.random.default_rng(seed)
    df = design_df.copy().reset_index(drop=True)
    order = rng.permutation(len(df)) + 1
    df["run_order"] = order
    return df.sort_values("run_order").reset_index(drop=True)


def arm_balance(df, arm_col="arm", by=None):
    """Quick check: counts per arm (optionally within each stratum/block)."""
    if by:
        return df.groupby([by, arm_col]).size().unstack(fill_value=0)
    return df[arm_col].value_counts()


if __name__ == "__main__":
    print("== simple (n=10) ==")
    print(arm_balance(simple_randomization(10, seed=1)).to_dict())

    print("\n== permuted blocks, 2:1 treatment:control, n=12 ==")
    d = block_randomization(12, arms=["treatment", "control"], ratio=(2, 1), seed=1)
    print(d.to_string(index=False))
    print("balance:", arm_balance(d).to_dict())

    print("\n== stratified by site (A=8, B=6) ==")
    d = stratified_block_randomization({"siteA": 8, "siteB": 6}, seed=1)
    print(arm_balance(d, by="stratum").to_string())

    print("\n== cluster randomization (6 clinics) ==")
    print(cluster_randomization(6, seed=1).to_string(index=False))

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs network access, Needs Python

The author says: Requires Python >=3.12 with numpy, pandas, and pydoe 1.5.0 (DOE matrices). Network access is needed only to install packages; no credentials are required.

Security

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  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

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Attribution and licence

Created by
K-Dense Inc.
Publisher
K-Dense (Community)
Source
K-Dense-AI/scientific-agent-skills · skills/experimental-design · commit a25cf9e
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE.md)
  • ATTRIBUTION.md (written by AutoGineer)
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