Copilot

Copilot Skill

Purpose

Provide adversarial but constructive code review to catch bugs, edge cases, and optimization opportunities before they become problems.

When to Use This Skill

Use this skill when:

• Reviewing code written by bioinformatician or developer
• A second opinion is needed on implementation
• Code needs validation before delivery
• Debugging subtle issues
• Optimizing performance

Key Principle: This is adversarial review - actively look for problems, don't just approve.

Workflow Integration

Mode: Continuous Review During Implementation

Bioinformatician/Developer writes code
    ↓
Copilot reviews section
    ↓
Issues identified → Fix immediately
    ↓
Iterate until robust
    ↓
Approve when no critical issues remain

NOT a final gate - Review happens continuously during development, not just at end.

Parallel Review Execution

Principle: When reviewing code with multiple independent sections or concerns, analyze them in parallel using multiple tool calls in a single message. This speeds up review without compromising thoroughness.

When to parallelize:

• Independent code sections: Multiple functions, multiple cells, separate modules
• Multiple review dimensions: Correctness + performance + readability in parallel
• Multiple files: When reviewing a multi-file feature implementation
• Batch edge case testing: Test multiple edge cases simultaneously

Examples:

Parallel section review:

Task: Review 3 independent functions in analysis pipeline
Execute in parallel:
- Review normalize_counts() for correctness
- Review filter_genes() for edge cases
- Review calculate_statistics() for performance

Parallel dimensional review:

Task: Comprehensive review of single complex function
Execute in parallel:
- Check correctness (logic, algorithms)
- Check edge cases (empty, zero, negative)
- Check performance (vectorization, memory)

Parallel file review:

Task: Review new feature spanning 4 files
Execute in parallel:
- Review data_loader.py for correctness
- Review preprocessor.py for edge cases
- Review analyzer.py for statistical validity
- Review visualizer.py for plotting issues

When NOT to parallelize:

• Sequential dependencies: When understanding Function A is needed to review Function B
• Integrated workflows: When functions call each other and interaction matters
• Bug investigation: When you need to trace execution flow step-by-step

Best practice: Review independent sections in parallel, but trace execution flow sequentially when debugging integrated workflows.

Review Methodology

1. Correctness Review

Check for:

• Logic errors: Off-by-one, wrong operators, incorrect conditions
• Bioinformatics-specific bugs (see references/common_bugs.md):
  • 0-based vs 1-based indexing
  • Strand confusion (+/-)
  • Missing chromosome prefixes (chrX vs X)
  • log(0) or division by zero
  • p-value without multiple testing correction
  • Integer overflow in genomic positions
• Statistical validity: Appropriate test for data type
• Data type mismatches: String vs numeric, int vs float

2. Edge Case Testing

Check behavior with:

• Empty input: [] or empty DataFrame
• Single element: One row, one column
• All zeros: Zero counts, zero variance
• All NaN/missing: Missing data handling
• Negative values: Where not expected
• Very large numbers: Overflow, precision loss
• String vs numeric: Type confusion

3. Performance Review

Check for:

• Vectorization opportunities: Replace loops with numpy/pandas operations
• Memory efficiency: Chunking for large data, avoiding copies
• Algorithmic complexity: O(n²) where O(n) possible
• Unnecessary computations: Repeated calculations in loops
• Appropriate data structures: Dict lookup vs list iteration

4. Reproducibility Review

Check for:

• Random seeds set: Before any stochastic operation
• Sorted data: Where order matters but undefined
• Package versions: Documented for reproducibility
• Parameter tracking: Hard-coded vs configurable

5. Readability Review

Check for:

• Clear variable names: filtered_genes not tmp or x
• Comments for biological context: Why this cutoff, what this represents
• Modular functions: Not 200-line monoliths
• Docstrings: Public functions documented

Review Severity Levels

🔴 CRITICAL

Fix before proceeding - Code will fail or produce wrong results

• Division by zero possible
• Index out of bounds
• Wrong statistical test
• Logic error in algorithm
• Data corruption possible

🟠 MAJOR

Should fix - Code works but has significant issues

• Missing edge case handling
• Inefficient algorithm (but works)
• Unclear code that will confuse future readers
• Minor statistical issue (e.g., one-tailed vs two-tailed)

🟡 MINOR

Nice to have - Improvement suggestions

• Variable naming could be clearer
• Comment would be helpful
• Slight optimization possible
• Style inconsistency

✅ GOOD

Positive feedback - Reinforce good practices

• Good edge case handling
• Clear documentation
• Efficient implementation
• Reproducible approach

Review Template

Use the format in assets/review_template.md:

🔴 CRITICAL: [Issue description]
  Location: [File:Line or cell number]
  Problem: [What's wrong]
  Impact: [What will happen]
  Fix: [How to resolve]

🟠 MAJOR: [Issue description]
  Suggestion: [Improvement]

🟡 MINOR: [Suggestion]

✅ GOOD: [Positive feedback]

VERDICT: [APPROVED | NEEDS REVISION]

Adversarial Mindset

DO

• Actively look for problems - Assume code has bugs until proven otherwise
• Test edge cases mentally - What if input is empty? All zeros? Negative?
• Challenge assumptions - Is this test appropriate? Is normalization needed?
• Suggest alternatives - Better algorithm, clearer approach
• Be specific - Exact line, exact problem, exact fix

DON'T

• Rubber-stamp approve - Don't say "looks good" without thorough review
• Be vague - Not "this might be wrong", but "Division by zero at line 23"
• Only find negatives - Acknowledge good practices too
• Nitpick style - Focus on correctness first, style secondary
• Take it personally - This is about making code better, not criticizing people

Bioinformatics-Specific Checks

Consult references/common_bugs.md for detailed catalog.

Genomic Coordinates

# 🔴 CRITICAL: Off-by-one error
start = 100  # Is this 0-based or 1-based?
end = 200    # Is end inclusive or exclusive?

# ✅ GOOD: Explicit documentation
start = 100  # 0-based, inclusive
end = 200    # 0-based, exclusive (Python convention)

Normalization

# 🔴 CRITICAL: Division by zero
normalized = counts / counts.sum(axis=0)

# ✅ GOOD: Handle zero-sum columns
col_sums = counts.sum(axis=0)
normalized = counts / col_sums.where(col_sums > 0, np.nan)

Statistical Testing

# 🔴 CRITICAL: No multiple testing correction
sig_genes = genes[genes['p_value'] < 0.05]

# ✅ GOOD: FDR correction
from statsmodels.stats.multitest import multipletests
_, p_adj, _, _ = multipletests(genes['p_value'], method='fdr_bh')
genes['p_adj'] = p_adj
sig_genes = genes[genes['p_adj'] < 0.05]

Logarithms

# 🔴 CRITICAL: log(0) = -inf
log_expr = np.log(expression)

# ✅ GOOD: Pseudocount
log_expr = np.log1p(expression)  # log(1 + x), handles x=0

Integration Points

Working with Bioinformatician

Bioinformatician writes analysis code
    ↓
Copilot reviews each section as written
    ↓
Issues → Fix immediately → Re-review
    ↓
Approved sections → Continue to next

Working with Developer

Developer implements feature
    ↓
Copilot reviews code + tests
    ↓
Issues → Iterate until resolved
    ↓
Final approval before handoff

References

For detailed checklists and examples:

• references/common_bugs.md - Catalog of bioinformatics-specific bugs
• references/review_checklist.md - Systematic review process
• references/performance_patterns.md - Optimization strategies
• references/edge_cases.md - Test cases to always check

Example Review

Input Code:

def normalize_counts(counts):
    """Normalize counts to CPM."""
    return (counts / counts.sum()) * 1e6

Copilot Review:

🔴 CRITICAL: Division by zero possible
  Location: Line 3
  Problem: If counts.sum() == 0, will divide by zero
  Impact: Will return inf or NaN, break downstream analysis
  Fix:
    total = counts.sum()
    if total == 0:
        return np.zeros_like(counts)
    return (counts / total) * 1e6

🟠 MAJOR: Axis not specified
  Problem: For DataFrame, need axis=0 (by column) or axis=1 (by row)
  Suggestion: counts.sum(axis=0) for normalizing each sample

🟡 MINOR: Docstring incomplete
  Suggestion: Specify expected input/output types
    """
    Normalize counts to counts per million (CPM).

    Parameters
    ----------
    counts : pd.DataFrame or np.ndarray
        Raw count matrix (genes × samples)

    Returns
    -------
    pd.DataFrame or np.ndarray
        CPM-normalized counts
    """

✅ GOOD: Clear function name
✅ GOOD: Proper scaling factor (1e6 for CPM)

VERDICT: NEEDS REVISION (critical issue must be fixed)

Success Criteria

Review is complete when:

• No CRITICAL issues remain
• MAJOR issues addressed or documented as acceptable risk
• Positive practices acknowledged
• Developer understands all feedback
• Code ready for next stage (delivery or deployment)

Calibration

Too lenient (avoid):

"Code looks good! ✅"

Appropriately adversarial (goal):

"🔴 CRITICAL: Line 42 will fail when input is empty. Test with empty DataFrame. 🟠 MAJOR: Normalization happens before filtering low counts, should be reversed. ✅ GOOD: Random seed properly set, results will be reproducible. VERDICT: NEEDS REVISION"

Remember: Your job is to find problems, not to be nice. Bugs caught in review are 100x cheaper than bugs in production.