
Drift Analysis
- 62 installs
- 931 repo stars
- Updated July 26, 2026
- avifenesh/agentsys
drift-analysis is a Claude skill that detects drift between documented plans and actual code and produces a prioritized reconstruction plan.
About
This skill analyzes project state to detect drift between documented plans and the actual implementation. It classifies plan drift, documentation drift, issue drift, and scope drift using confidence-graded signals, then scores and buckets items into a prioritized reconstruction plan. Developers use it for a reality check comparing docs, roadmaps, and issues against the code.
- Classifies four drift types: plan, documentation, issue, and scope
- Confidence-graded drift indicators (HIGH/MEDIUM/LOW) with a priority-scoring formula
- Buckets items into Immediate, Short-term, Medium-term, and Backlog
Drift Analysis by the numbers
- 62 all-time installs (skills.sh)
- Ranked #1,543 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
drift-analysis capabilities & compatibility
- Capabilities
- drift detection · docs vs code audit · roadmap alignment
- Works with
- github
- Use cases
- planning · project management · documentation
What drift-analysis says it does
Knowledge and patterns for analyzing project state, detecting plan drift, and creating prioritized reconstruction plans.
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| Installs | 62 |
|---|---|
| repo stars | ★ 931 |
| Last updated | July 26, 2026 |
| Repository | avifenesh/agentsys ↗ |
What it does
Detect drift between documented plans and actual code, then produce a prioritized reconstruction plan.
Who is it for?
Reality-checking whether docs, roadmaps, and issues still match the actual codebase
Skip if: Implementing the fixes it identifies
When should I use this skill?
The user asks about plan drift, a reality check, comparing docs to code, or roadmap alignment
What you get
A confidence-graded drift report and a prioritized reconstruction plan bucketed by urgency.
- drift report
- prioritized reconstruction plan
By the numbers
- 4 drift types
- 4 priority buckets
Files
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 contextData 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 mergedPrioritization 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 healthyCross-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 }
},
"code": {
"frameworks": ["Express"],
"health": { "hasTests": true, "hasCi": true }
}
}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...Related skills
FAQ
What kinds of drift does it detect?
Plan drift, documentation drift, issue drift, and scope drift, each with confidence-graded indicators.
How are findings prioritized?
A priority formula scores items by severity and category, then buckets them into Immediate, Short-term, Medium-term, and Backlog.