القائمة
Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched
Use when facing 2+ independent investigations that can proceed without shared state - parallel literature survey, multi-dataset replication, or pre-specified robustness checks
Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents
Use when a result is surprising, impossible, contradicts a sanity check, a pipeline fails, a model won't converge, or a replication fails - before adjusting anything
Use before running any confirmatory analysis or looking at outcome data, when testing a hypothesis, computing a p-value, or about to claim an effect - locks predictions and decision rules before results are seen
| name | designing-the-analysis |
| description | Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model |
Write a comprehensive analysis plan assuming the analyst has zero context for this project and questionable statistical taste. Document everything they need: which datasets and variables, how each construct is computed, the exact model or test, the sample size / power justification, which confounds are handled and how, the decision rules, and the planned figures. Give them the whole thing as bite-sized steps. DRY. YAGNI. Pre-register. Validate pipelines on known data. Commit frequently.
Assume they are a capable programmer but know almost nothing about this domain, this dataset, or good statistical design.
Announce at start: "I'm using the designing-the-analysis skill to create the analysis plan."
Save plans to: docs/science-superpowers/plans/YYYY-MM-DD-<topic>.md
If the question document still bundles several independent investigations, stop and split it — one plan per question. Each plan should produce an interpretable, self-contained result.
docs/science-superpowers/questions/...)If you don't have a prior effect size, the design must include how you will justify the sample size anyway (smallest effect of interest, precision target, or a sensitivity analysis).
Before defining steps, map the pipeline. Data flows one direction: raw → cleaned → derived → results.
For each known confound from the survey, state how it is handled: measured and adjusted for, stratified, matched, design-excluded, or explicitly acknowledged as a limitation. "We'll see" is not a plan.
State the threats to validity you are accepting and why.
State the target effect size, alpha, desired power, and the resulting required N — or, for a fixed existing sample, the minimum detectable effect at the planned power. If underpowered, say so and decide with your human partner whether to proceed (e.g., reframe as estimation, not a hypothesis test).
Each step is one action (2-5 minutes). Every step that touches data is paired with a validation — the science analog of watching a test fail then pass:
The simulated-data validation step is mandatory for any nontrivial estimator or model: if you never watched your pipeline recover a known signal, you don't know it works.
Every plan MUST start with this header:
# [Question] Analysis Plan
> **For agentic workers:** REQUIRED SUB-SKILL: pre-register this plan with science-superpowers:preregistering-analysis BEFORE execution. Then use science-superpowers:subagent-driven-analysis (recommended) or science-superpowers:executing-analysis to run it step-by-step. Steps use checkbox (`- [ ]`) syntax for tracking.
**Question:** [the falsifiable question, one sentence]
**Design:** [observational/experimental; cross-sectional/longitudinal; the comparison]
**Data:** [datasets, sample, unit of analysis]
**Primary analysis:** [the one model/test that answers the question]
**Decision rule:** [exactly what result confirms vs. disconfirms H1]
---
### Task N: [Analysis component]
**Artifacts:**
- Create: `analysis/exact_script.py`
- Reads: `data/raw/exact_file.csv` (immutable)
- Writes: `data/derived/exact_output.parquet`
- [ ] **Step 1: Write the loading/transform code**
```python
df = pd.read_csv("data/raw/exact_file.csv")
clean = df[df["value"].between(0, 100)]
```
- [ ] **Step 2: Validate the step**
Run: `python analysis/exact_script.py --check`
Expected: `rows in: 10342, rows out: 10298, dropped: 44 (out-of-range)` — dropped count matches the known data-quality issue, not silent loss.
- [ ] **Step 3: Run the primary model exactly as specified**
```python
model = smf.ols("outcome ~ exposure + age + site", data=clean).fit()
```
- [ ] **Step 4: Apply the pre-registered decision rule**
The estimate for `exposure` is interpreted against the rule fixed in the pre-registration — not re-decided here.
- [ ] **Step 5: Commit**
```bash
git add analysis/exact_script.py data/derived/exact_output.parquet
git commit -m "analysis: primary model for exposure effect"
```
Every step must contain the actual content the analyst needs. These are plan failures — never write them:
After writing the plan, re-read the question document with fresh eyes and check the plan against it:
Fix issues inline. If a question requirement has no task, add the task.
The plan is not ready to execute until its predictions and decision rules are locked.
REQUIRED NEXT SKILL: Use science-superpowers:preregistering-analysis to freeze the confirmatory hypotheses, predictions, and decision rules before any outcome is observed. Execution happens only after that.