Add scientific research workflows plugin

plugins/hetaoBackend/scientific-research-workflows/LICENSE

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+MIT License
+
+Copyright (c) 2025 K-Dense Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

plugins/hetaoBackend/scientific-research-workflows/README.md

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+# Scientific Research Workflows
+
+This MiniMax Code Plugin packages nine complementary, local-first Skills from
+[K-Dense Scientific Agent Skills](https://github.com/K-Dense-AI/scientific-agent-skills).
+It helps researchers move from an early idea to a defensible study plan, analysis, manuscript, and
+review without installing the upstream repository's unrelated, network-dependent, or
+separately-licensed Skills.
+
+## Included Skills
+
+| Skill | Upstream version | Use it for |
+| --- | ---: | --- |
+| `scientific-brainstorming` | 1.1 | Generate and challenge candidate research directions. |
+| `hypothesis-generation` | 2.1 | Turn observations into rival, testable hypotheses and predictions. |
+| `experimental-design` | 1.1 | Plan randomization, blocking, controls, factorial designs, and study layouts. |
+| `statistical-power` | 1.0 | Estimate sample sizes, detectable effects, and simulation-based power. |
+| `statistical-analysis` | 1.1 | Select tests, check assumptions, estimate effects, and report results. |
+| `uncertainty-and-units` | 1.0 | Check dimensions and propagate measurement uncertainty. |
+| `scientific-writing` | 2.0 | Draft and audit evidence-traceable manuscripts and reports. |
+| `peer-review` | 2.1 | Prepare authorized, constructive, evidence-bounded review drafts. |
+| `scholar-evaluation` | 2.1 | Give qualitative developmental feedback on scholarly work. |
+
+The Skills, references, assets, and scripts are sourced from upstream commit
+[`1dd0fccf46fc3c9855c4a0c313a0c57fe4319883`](https://github.com/K-Dense-AI/scientific-agent-skills/commit/1dd0fccf46fc3c9855c4a0c313a0c57fe4319883).
+To satisfy this repository's hosted-package validator, the scientific-writing templates use the
+`[[DRAFT:...]]` label for unfinished fields; its local linter and scaffold helper were updated
+consistently, with no change to the marker's meaning.
+The bundle deliberately contains only Skills that declare the MIT license. The upstream repository
+contains other Skills with different terms; those are not included here.
+
+## Try it
+
+```text
+I am planning a two-site experiment comparing three treatments, but samples arrive in four weekly
+batches. Help me define competing hypotheses, design randomization and blocking, estimate the sample
+size for 80% power, and produce a preregistration-ready analysis outline. State every assumption and
+do not invent pilot data.
+```
+
+Expected result: MiniMax Code separates hypotheses from established evidence, identifies the
+experimental unit and likely batch/site confounders, proposes a reproducible blocked design, asks for
+the effect-size inputs needed for power, and creates an analysis plan with explicit uncertainty and
+reporting boundaries.
+
+## Requirements
+
+- MiniMax Code with Agent Skills support.
+- Core guidance is platform-neutral and works without installing packages.
+- Optional bundled command-line helpers require Python:
+  - Python 3.11+ standard library for the brainstorming, hypothesis, writing, peer-review, and
+    scholar-evaluation helpers.
+  - Python 3.10+ plus `numpy`, `pandas`, and `pyDOE3` for experimental-design helpers.
+  - Python 3.10+ plus `numpy`, `pandas`, `scipy`, `statsmodels`, `pingouin`, and `matplotlib` for
+    statistical power and analysis helpers. `pymc`, `arviz`, and `lifelines` are optional for the
+    corresponding Bayesian and survival workflows.
+  - Python 3.12+ plus `pint`, `uncertainties`, `numpy`, and `scipy` for uncertainty-and-units numeric
+    helpers.
+- Supported on macOS, Windows, and Linux when the selected optional Python dependencies are available.
+- No account or paid service is required.
+
+## Data and network
+
+- The Plugin defines no MCP server, telemetry, lifecycle hook, installer, or background process.
+- Bundled scripts process bounded JSON, CSV, Markdown, or research data locally and do not make
+  network requests.
+- The Skills may read user-selected manuscripts, study plans, or datasets from the active workspace
+  and may write requested local reports or intermediate artifacts.
+- Confidential, unpublished, personal, clinical, or controlled material must remain local unless the
+  user has authorization and explicitly chooses an external destination. The peer-review and writing
+  Skills include stricter venue and confidentiality checks.
+- No credentials required.
+
+Disabling or uninstalling the Plugin removes these Skills from discovery. It does not remove Python
+packages the user installed independently or delete files the user asked MiniMax Code to create.
+
+## Attribution and maintenance
+
+- Upstream project: <https://github.com/K-Dense-AI/scientific-agent-skills>
+- Upstream snapshot: `1dd0fccf46fc3c9855c4a0c313a0c57fe4319883` (2026-08-31)
+- Original author: K-Dense Inc.
+- MiniMax Code package maintainer: [hetaoBackend](https://github.com/hetaoBackend)
+- License: MIT; see [`LICENSE`](LICENSE).
+
+This community package is an independently maintained selection, not an endorsement by K-Dense or
+MiniMax. Future upstream updates require an explicit review of source changes, dependency behavior,
+and each included Skill's license before the pinned snapshot is advanced.

plugins/hetaoBackend/scientific-research-workflows/plugin.json

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+{
+  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
+  "name": "scientific-research-workflows",
+  "version": "1.0.0",
+  "description": "Nine local-first scientific research Skills for ideation, hypotheses, study design, statistics, uncertainty, writing, peer review, and scholarly evaluation.",
+  "author": {
+    "name": "K-Dense Inc.; MiniMax Code packaging by hetaoBackend",
+    "url": "https://k-dense.ai"
+  },
+  "homepage": "https://github.com/K-Dense-AI/scientific-agent-skills",
+  "repository": "https://github.com/K-Dense-AI/scientific-agent-skills",
+  "license": "MIT",
+  "keywords": [
+    "minimax-code",
+    "science",
+    "research",
+    "statistics",
+    "peer-review",
+    "scientific-writing"
+  ]
+}

plugins/hetaoBackend/scientific-research-workflows/skills/experimental-design/SKILL.md

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+---
+name: experimental-design
+description: 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/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.
+allowed-tools: Read Write Edit Bash
+compatibility: Requires Python >=3.10. Scripts use numpy, pandas, and pyDOE3 (DOE matrices). Install with uv as shown below.
+license: MIT license
+metadata:
+  version: "1.1"
+  skill-author: 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
+
+```bash
+uv pip install "numpy>=1.26" "pandas>=2.0" pyDOE3
+```
+
+`pyDOE3` is the maintained successor to pyDOE/pyDOE2 and supplies factorial,
+fractional-factorial, Plackett-Burman, central-composite, Box-Behnken, and
+Latin-hypercube generators. The bundled scripts wrap it to return designs in real
+factor units with named columns and randomized run order.
+
+---
+
+## 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 (more power, watch carry-over).
+│   └─ You can only randomize groups, not individuals (schools, clinics)?
+│         → CLUSTER-randomized design (analyze at the cluster level; 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`. Everything is seeded so the exact
+schedule can be archived and regenerated — a requirement for trial registration
+and good lab practice.
+
+### Randomization / allocation schedules — `scripts/randomization.py`
+
+```python
+from randomization import (
+    simple_randomization, block_randomization,
+    stratified_block_randomization, cluster_randomization,
+    assign_factorial_runs, arm_balance,
+)
+
+# Permuted blocks keep the arms balanced throughout enrollment (use for n < ~100
+# or sequential intake — simple randomization can drift out of balance with small n)
+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** guarantees balance throughout; **stratified block** additionally
+balances a known prognostic factor; **cluster** is mandatory when the intervention
+is delivered at a group level. See `references/randomization_and_blocking.md`.
+
+### DOE matrices — `scripts/doe_designs.py`
+
+```python
+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)}, seed=42)
+
+design.to_csv("experimental_runs.csv", index=False)
+```
+
+Run order is randomized by default so factors aren't confounded with time/drift
+(machine warm-up, reagent aging). See `references/factorial_and_doe.md` for picking
+generators, reading the alias structure, and choosing resolution.
+
+---
+
+## 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 can't detect a curved
+   response; 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.
+7. **Document** the design, seed, and schedule (pre-register if possible) so the
+   analysis is confirmatory and the layout is auditable.
+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*.