# Dr Cook:method Designer

> Guide researchers through experimental design decisions. Use when planning experimental design, study design, method design, CRISPR design, sgRNA design, network pharmacology design, multi-omics design, sample size calculation, power calculation, statistical power, cohort design, knockout design, knockin design, 实验设计, 样本量计算, 统计功效, CRISPR设计, 多组学, 网络药理学设计. Do NOT trigger for: bioinformatics-assistant (data analysis on existing data), data-visualizer (plotting only), paper-writer (Methods section writing only).

- Skill: `wen-chen/dr-cook-method-designer` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add wen-chen/dr-cook-method-designer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/wen-chen/dr-cook-method-designer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: wen-chen (https://skillmd.com/u/wen-chen)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/wen-chen/dr-cook-method-designer

---


# method-designer

## 1. Overview

method-designer guides researchers through experimental design decisions before data collection begins. Four tracks: CRISPR (sgRNA selection, delivery, validation, controls); network pharmacology (TCM compound-target-pathway design, ADME filtering, validation plan); multi-omics (platform selection, matched-sample design, integration strategy); sample size (power analysis for t-test, ANOVA, survival with dropout adjustments).

## 2. Parameters

### Required

| Parameter | Values | Description |
|---|---|---|
| `design_type` | `crispr` \| `network_pharmacology` \| `multi_omics` \| `sample_size` | Which design track to run |
| `research_question` | string | 1–3 sentence description of what the experiment aims to answer |

### Optional

| Parameter | Values | Description |
|---|---|---|
| `organism` | `human` \| `mouse` \| `rat` \| `cell_line` (default: `human`) | Species or model system for the experiment |
| `domain` | `tcm` \| `bioinformatics` \| `clinical` \| `pharmacology` | Research domain; loaded from context_output if available |
| `budget_constraint` | `low` \| `medium` \| `high` | Affects method and reagent recommendations |
| `timeline` | string | Expected project duration (e.g., `"6 months"`, `"1 year"`) |
| `existing_data` | string | Description of data or resources already available (e.g., "mouse colony established", "RNA-seq data from prior study") |

## 3. Workflow

**Step 1 — Check upstream context_output.**
Inspect `context_output.parameters` for existing values. Inherit `domain`, `parameters.organism`, and `parameters.analysis_type` (from bioinformatics-assistant; maps to `design_type` context) if present. Also check `context_output.summary` (from literature-reviewer) for an implicit `research_question`. If inherited parameters cover required fields, skip re-asking for them. Do not ask the user for information already known from upstream modules.

**Step 2 — Collect design_type and research_question.**
Ask for one parameter at a time. If `design_type` is not specified, present the menu:

> "Which design track?
> (1) CRISPR / gene editing
> (2) Network pharmacology
> (3) Multi-omics integration
> (4) Sample size calculation"

Then collect `research_question` if not already stated or inferable from upstream `context_output.summary`.

**Step 3 — Collect optional context.**
Ask in a single prompt for budget, timeline, and existing data. If the user declines or does not respond, proceed with standard-tier recommendations.

**Step 4 — Load the relevant reference file.**
Select based on `design_type`:
- `crispr` → load `references/crispr-design.md`
- `network_pharmacology` → load `references/network-pharmacology-workflow.md`
- `multi_omics` → load `references/multi-omics-integration.md`
- `sample_size` → load `references/sample-size-calculation.md`

Apply all design rules, tables, and decision logic from the loaded reference throughout Steps 5–6.

**Step 5 — Generate design plan.**
Produce a structured plan tailored to the track:

*CRISPR track:* Apply sgRNA selection criteria (on-target score via CRISPOR, off-target check, design 3–5 guides per target). Recommend delivery method from the table in crispr-design.md based on `organism`. Outline the validation workflow: Surveyor/T7E1 → Sanger → Western blot. List required controls: scramble sgRNA, positive control, parental baseline. If `budget_constraint = low`: prefer RNP transfection (no plasmid, no viral). If `high`: consider conditional knockin via Cas9-nickase or base editing.

*Network pharmacology track:* Map the research question to TCM formula → disease, single herb → disease, or compound class → phenotype. Recommend databases from the selection table. State ADME filters (OB ≥ 30%, DL ≥ 0.18). Outline the validation plan: docking → binding assay → cellular assay. List reporting requirements. If `budget_constraint = low`: limit wet-lab validation to cell-based assays; defer animal experiments.

*Multi-omics track:* Select platforms from the table in multi-omics-integration.md. State matched-sample requirements and minimum n ≥ 6. Recommend integration strategy (MOFA2 for early integration, DIABLO for supervised). Flag batch effect and missing data pitfalls. If `budget_constraint = low`: prioritize transcriptomics only; add proteomics in phase 2.

*Sample size track:* Identify study type. State α = 0.05 and β = 0.20 (80% power) defaults. Apply formula and R code from sample-size-calculation.md. Present n across at least three effect sizes. Apply dropout and multiple testing adjustments.

**Step 6 — Present structured design plan.**
Format the output as follows:
- Header line: `[Design: <TRACK> | Organism: <ORGANISM> | Budget: <TIER>]`
- Numbered workflow steps with phase headings (Phase 1: ..., Phase 2: ...)
- Decision points marked with ⚠️ and a brief explanation of what depends on the outcome
- Estimated timeline per phase if `timeline` was provided
- Budget-tier recommendations in a separate callout if `budget_constraint` was provided
- Materials or reagents list where applicable (CRISPR, multi-omics)

**Step 7 — Offer iteration and downstream modules.**
End with: "Would you like me to expand any section, adjust for a different budget or timeline, or help write the Methods section for this design?" Then add a `**Next steps**` block with context-sensitive routing: CRISPR → bioinformatics-assistant (DEG on knockout vs. wildtype RNA-seq); network pharmacology → bioinformatics-assistant (for computational pipeline) OR paper-writer (if the user's focus is writing the Methods section); multi-omics → bioinformatics-assistant; sample size → paper-writer (to draft the Statistical Analysis subsection). If user mentions "write methods" or "Methods section," always suggest paper-writer.

## 4. Output Format

Begin every response with the design header:

```
[Design: CRISPR | Organism: Mouse | Budget: Medium]
```

The body is a numbered, phase-structured plan with bold phase headings (`**Phase 1: ...**`). Decision points use the ⚠️ marker with a brief conditional (e.g., "⚠️ If editing efficiency < 20%, switch to RNP electroporation"). Code blocks use language-tagged fences (` ```r `). Materials and reagents appear as a bulleted list under `**Materials**`. A `**Next steps**` block at the end names specific Dr. Cook modules.

## 5. context_output

### Reads from upstream

| Field | Source | Usage |
|---|---|---|
| `parameters.domain` | any upstream module | Avoids re-asking for domain |
| `parameters.organism` | any upstream module | Avoids re-asking for organism |
| `parameters.analysis_type` | bioinformatics-assistant | Maps to `design_type` if present |
| `summary` | literature-reviewer, research-gap-finder | May serve as `research_question` source |

### Writes to output

```json
{
  "module": "method-designer",
  "summary": "<e.g., 'CRISPR knockout design for mouse Tp53, medium budget, 6-month timeline'>",
  "raw_text": "<the full design plan as produced in Step 6>",
  "parameters": {
    "design_type": "<crispr | network_pharmacology | multi_omics | sample_size>",
    "organism": "<human | mouse | rat | cell_line>",
    "budget_constraint": "<low | medium | high | null>",
    "timeline": "<string | null>",
    "existing_data": "<string | null>"
  },
  "status": "success | partial | failed",
  "error_message": "<string | null>"
}
```

`status = partial` when `research_question` is vague or `design_type` could not be determined after one clarification attempt. `status = failed` if a required parameter could not be collected after two attempts. `raw_text` is populated with the full structured design plan once Step 6 is complete.

## 6. References

`references/`: `crispr-design.md`, `network-pharmacology-workflow.md`, `multi-omics-integration.md`, `sample-size-calculation.md`.