drift-analysis

name	drift-analysis
description	Use when the user asks about plan drift, reality check, comparing docs to code, project state analysis, roadmap alignment, implementation gaps, or needs guidance on identifying discrepancies between documented plans and actual implementation state.
version	5.1.0

Drift Analysis

Knowledge and patterns for analyzing project state, detecting plan drift, and creating prioritized reconstruction plans.

Architecture Overview

/drift-detect
        │
        ├─→ collectors.js (pure JavaScript)
        │   ├─ scanGitHubState()
        │   ├─ analyzeDocumentation()
        │   └─ scanCodebase()
        │
        └─→ plan-synthesizer (Opus)
            └─ Deep semantic analysis with full context

Data collection: Pure JavaScript (no LLM overhead) Semantic analysis: Single Opus call with complete context

Drift Detection Patterns

Types of Drift

Plan Drift: When documented plans diverge from actual implementation

• PLAN.md items remain unchecked for extended periods
• Roadmap milestones slip without updates
• Sprint/phase goals not reflected in code changes

Documentation Drift: When documentation falls behind implementation

• New features exist without corresponding docs
• README describes features that don't exist
• API docs don't match actual endpoints

Issue Drift: When issue tracking diverges from reality

• Stale issues that no longer apply
• Completed work without closed issues
• High-priority items neglected

Scope Drift: When project scope expands beyond original plans

• More features documented than can be delivered
• Continuous addition without completion
• Ever-growing backlog with no pruning

Detection Signals

HIGH-CONFIDENCE DRIFT INDICATORS:
- Milestone 30+ days overdue with open issues
- PLAN.md < 30% completion after 90 days
- 5+ high-priority issues stale > 60 days
- README features not found in codebase

MEDIUM-CONFIDENCE INDICATORS:
- Documentation files unchanged for 180+ days
- Draft PRs open > 30 days
- Issue themes don't match code activity
- Large gap between documented and implemented features

LOW-CONFIDENCE INDICATORS:
- Many TODOs in codebase
- Stale dependencies
- Old git branches not merged

Prioritization Framework

Priority Calculation

function calculatePriority(item, weights) {
  let score = 0;

  // Severity base score
  const severityScores = {
    critical: 15,
    high: 10,
    medium: 5,
    low: 2
  };
  score += severityScores[item.severity] || 5;

  // Category multiplier
  const categoryWeights = {
    security: 2.0,    // Security issues get 2x
    bugs: 1.5,        // Bugs get 1.5x
    infrastructure: 1.3,
    features: 1.0,
    documentation: 0.8
  };
  score *= categoryWeights[item.category] || 1.0;

  // Recency boost
  if (item.createdRecently) score *= 1.2;

  // Stale penalty (old items slightly deprioritized)
  if (item.daysStale > 180) score *= 0.9;

  return Math.round(score);
}

Time Bucket Thresholds

Bucket	Criteria	Max Items
Immediate	severity=critical OR priority >= 15	5
Short-term	severity=high OR priority >= 10	10
Medium-term	priority >= 5	15
Backlog	everything else	20

Priority Weights (Default)

security: 10     # Security issues always top priority
bugs: 8          # Bugs affect users directly
features: 5      # New functionality
documentation: 3 # Important but not urgent
tech-debt: 4     # Keeps codebase healthy

Cross-Reference Patterns

Document-to-Code Matching

// Fuzzy matching for feature names
function featureMatch(docFeature, codeFeature) {
  const normalize = s => s
    .toLowerCase()
    .replace(/[-_\s]+/g, '')
    .replace(/s$/, ''); // Remove trailing 's'

  const docNorm = normalize(docFeature);
  const codeNorm = normalize(codeFeature);

  return docNorm.includes(codeNorm) ||
         codeNorm.includes(docNorm) ||
         levenshteinDistance(docNorm, codeNorm) < 3;
}

Common Mismatches

Documented As	Implemented As
"user authentication"	auth/, login/, session/
"API endpoints"	routes/, api/, handlers/
"database models"	models/, entities/, schemas/
"caching layer"	cache/, redis/, memcache/
"logging system"	logger/, logs/, telemetry/

Output Templates

Drift Report Section

## Drift Analysis

### {drift_type}
**Severity**: {severity}
**Detected In**: {source}

{description}

**Evidence**:
{evidence_items}

**Recommendation**: {recommendation}

Gap Report Section

## Gap: {gap_title}

**Category**: {category}
**Severity**: {severity}

{description}

**Impact**: {impact_description}

**To Address**:
1. {action_item_1}
2. {action_item_2}

Reconstruction Plan Section

## Reconstruction Plan

### Immediate Actions (This Week)
{immediate_items_numbered}

### Short-Term (This Month)
{short_term_items_numbered}

### Medium-Term (This Quarter)
{medium_term_items_numbered}

### Backlog
{backlog_items_numbered}

Best Practices

When Analyzing Drift

1. Compare timestamps, not just content

   • When was the doc last updated vs. last code change?
   • Are milestones dated realistically?

2. Look for patterns, not individual items

   • One stale issue isn't drift; 10 stale issues is a pattern
   • One undocumented feature isn't drift; 5 undocumented features is

3. Consider context

   • Active development naturally has some drift
   • Mature projects should have minimal drift
   • Post-launch projects often have documentation lag

4. Weight by impact

   • User-facing drift matters more than internal
   • Public API drift matters more than implementation details

When Creating Plans

1. Be actionable, not exhaustive

   • Top 5 immediate items, not top 50
   • Each item should be completable in reasonable time

2. Group related items

   • "Update authentication docs" not "Update login page docs" + "Update signup docs"

3. Include success criteria

   • How do we know this drift item is resolved?

4. Balance categories

   • All security first, but don't ignore everything else
   • Mix quick wins with important work

Data Collection (JavaScript)

The collectors.js module extracts data without LLM overhead:

GitHub Data

• Open issues categorized by labels
• Open PRs with draft status
• Milestones with due dates
• Stale items (> 90 days inactive)
• Theme analysis from titles

Documentation Data

• Parsed README, PLAN.md, CLAUDE.md, CHANGELOG.md
• Checkbox completion counts
• Section analysis
• Feature lists

Code Data

• Directory structure
• Framework detection
• Test framework presence
• Health indicators (CI, linting, tests)

Semantic Analysis (Opus)

The plan-synthesizer receives all collected data and performs:

1. Cross-referencing: Match documented features to implementation
2. Drift identification: Find divergence patterns
3. Gap analysis: Identify what's missing
4. Prioritization: Context-aware ranking
5. Report generation: Actionable recommendations

Example Input/Output

Collected Data (from collectors.js)

{
  "github": {
    "issues": [...],
    "categorized": { "bugs": [...], "features": [...] },
    "stale": [...]
  },
  "docs": {
    "files": { "README.md": {...}, "PLAN.md": {...} },
    "checkboxes": { "total": 15, "checked": 3 }
  },
   
     
          
  

Analysis Output (from plan-synthesizer)

# Reality Check Report

## Executive Summary
Project has moderate drift: 8 stale priority issues and 20% plan completion.
Strong code health (tests + CI) but documentation lags implementation.

## Drift Analysis
### Priority Neglect
**Severity**: high
8 high-priority issues inactive for 60+ days...

## Prioritized Plan
### Immediate
1. Close #45 (already implemented)
2. Update README API section...