Ds Review

Announce: "Using ds-review (Phase 4) to check methodology and quality."

Contents

• The Iron Law of DS Review
• Red Flags - STOP Immediately If You Think
• Review Focus Areas
• Confidence Scoring
• Common DS Issues to Check
• Required Output Structure
• Agent Invocation
• Quality Standards

Analysis Review

Single-pass review combining methodology correctness, data quality handling, and reproducibility checks. Uses confidence-based filtering.

You MUST only report issues with >= 80% confidence. This is not negotiable.

Before reporting ANY issue, you MUST:

1. Verify it's not a false positive
2. Verify it impacts results or reproducibility
3. Assign a confidence score
4. Only report if score >= 80

This applies even when:

• "This methodology looks suspicious"
• "I think this might introduce bias"
• "The approach seems unusual"
• "I would have done it differently"

STOP - If you catch yourself about to report a low-confidence issue, DISCARD IT. You're about to compromise the review's integrity.

Red Flags - STOP Immediately If You Think:

Review Focus Areas

Spec Compliance

• Verify all objectives from .claude/SPEC.md are addressed
• Confirm success criteria can be verified
• Check constraints were respected (especially replication requirements)
• Verify analysis answers the original question

Data Quality Handling

• Confirm missing values handled appropriately (not ignored)
• Verify duplicates addressed (documented if kept)
• Check outliers considered (handled or justified)
• Verify data types correct (dates parsed, numerics not strings)
• Confirm filtering logic documented with counts

Methodology Appropriateness

• Verify statistical methods appropriate for data type
• Check assumptions documented and verified (normality, independence, etc.)
• Confirm sample sizes adequate for conclusions
• Check multiple comparisons addressed if applicable
• Verify causality claims justified (or appropriately limited)

Reproducibility

• Verify random seeds set where needed
• Check package versions documented
• Verify data source/version documented
• Confirm all transformations traceable
• Verify results can be regenerated

Output Quality

• Verify visualizations labeled (title, axes, legend)
• Check numbers formatted appropriately (sig figs, units)
• Verify conclusions supported by evidence shown
• Confirm limitations acknowledged

Confidence Scoring

Rate each potential issue from 0-100:

CRITICAL: You MUST only report issues with confidence >= 80. If you report below this threshold, you're misrepresenting your certainty.

Common DS Issues to Check

Data Leakage

• Training data contains information from future
• Test data used in feature engineering
• Target variable used directly or indirectly in features

Selection Bias

• Filtering introduced systematic bias
• Survivorship bias in longitudinal data
• Non-random sampling not addressed

Statistical Errors

• Multiple testing without correction
• p-hacking or selective reporting
• Correlation interpreted as causation
• Inadequate sample size for claimed precision

Reproducibility Failures

• Random operations without seeds
• Undocumented data preprocessing
• Hard-coded paths or environment dependencies
• Missing package versions

Required Output Structure

markdown-output-structure: Template for review results with confidence-scored issues

## Analysis Review: [Analysis Name]
Reviewing: [files/notebooks being reviewed]

### Critical Issues (Confidence >= 90)

#### [Issue Title] (Confidence: XX)

**Location:** `file/path.py:line` or `notebook.ipynb cell N`

**Problem:** Clear description of the issue

**Impact:** How this affects results/conclusions

**Fix:**
```python
# Specific fix

Important Issues (Confidence 80-89)

[Same format as Critical Issues]

Data Quality Checklist

Methodology Checklist

Reproducibility Checklist

Summary

Verdict: APPROVED | CHANGES REQUIRED

[If APPROVED] The analysis meets quality standards. No methodology issues with confidence >= 80 detected.

[If CHANGES REQUIRED] X critical issues and Y important issues must be addressed before proceeding.

## Agent Invocation

task-agent-spawn: Spawn Task agent for structured analysis review

Spawn a Task agent to review the analysis:

Task(subagent_type="general-purpose"): "Review analysis against .claude/SPEC.md.

Execute single-pass review covering:

1. Spec compliance - verify objectives met
2. Data quality - confirm nulls, dupes, outliers handled
3. Methodology - verify appropriate, assumptions checked
4. Reproducibility - confirm seeds, versions, documentation

Confidence score each issue (0-100). Report only issues with >= 80 confidence. Return structured output per /ds-review format."

## Quality Standards

- **You must NOT report methodology preferences not backed by statistical principles.** Your opinion about how code should be written is not a review issue.
- **You must treat alternative valid approaches as non-issues (confidence = 0).** If the approach works correctly, don't report it.
- Ensure each reported issue is immediately actionable
- **If you're unsure, rate it below 80 confidence.** Uncertainty is not a reason to report—it's a reason to investigate more.
- Focus on what affects conclusions, not style. **STOP if you catch yourself criticizing coding style—that's not your role here.**

## Phase Complete

phase-transition: Invoke ds-verify after APPROVED review

After review is APPROVED, immediately invoke:

ds-verify: Verify analysis reproducibility and user acceptance

Skill(skill="workflows:ds-verify")

If CHANGES REQUIRED, return to `/ds-implement` to fix issues first.