Last Updated: January 2026
Maintainer: Update this document when analysis logic, personas, or metrics change.
Vibe Coding Profile analyzes your git history to reveal your vibe coding/ AI Assisted Engineering style and patterns. Think of it as "Spotify Wrapped for vibe coders." We look at how you build, not what you build.
Note: The term "vibe coding" can be polarizing, but it captures the cultural moment around AI-shaped development. We personally prefer "AI Assisted Engineering" because it’s more explicit about the role of AI. This is a playful side project, so we lean into the pop framing and keep it approachable for non-technical folks.
Your Git History → Analysis → Vibe Coding Profile (VCP)
↓ ↓ ↓
Commits Metrics Persona + Insights
PRs Axes Narrative
Timing Scores Share Cards
Key Principle: Vibe Coding Profile is observational, not judgmental. We detect patterns, not quality. Every coding style has strengths.
| Source | What We Extract | Privacy Note |
|---|---|---|
| Commits | Timestamps, file counts, additions/deletions, message structure | We never store or analyze code content |
| Pull Requests | Merge methods, checklists, issue links, templates | Body text parsed for structure only |
| File Paths | Subsystem classification (ui, api, tests, etc.) | Paths only, no file contents |
- Source code contents
- Private comments or discussions
- IDE activity or local history
- Unpushed commits
We retrieve up to 300 commits from your repository via GitHub API, sampling evenly across the repo's lifetime to capture patterns from start to present.
Bot commits (dependabot, renovate, release-please, etc.) are automatically filtered out so your analysis reflects your work, not automation.
We calculate 25+ metrics across several dimensions:
Volume & Timing
- Total commits, additions, deletions
- Active days vs span days
- Commit frequency patterns
Rhythm & Burstiness
- Hours between commits (median, p90)
- Burstiness score (-1 = steady, +1 = bursty)
- Peak coding hours and days
Commit Patterns
- Size distribution (p50, p90)
- Conventional commit ratio
- Fix-after-feature sequences
Build Categories
- Classification: feature, fix, test, docs, infra, refactor, etc.
- Category distribution and first-occurrence order
Six deterministic axes (0-100 scores) capture your workflow style:
| Axis | What It Measures |
|---|---|
| Automation Heaviness | How much you rely on AI agents and tools for generating code (initial/bulk commits dampened) |
| Guardrail Strength | How closely tests, CI, and docs follow your AI-generated changes |
| Iteration Loop Intensity | How often you run prompt-fix-run loops to refine AI output |
| Planning Signal | How much structure you define before prompting AI to generate code |
| Surface Area per Change | How many parts of the codebase your typical prompt or change touches |
| Shipping Rhythm | Your coding session pattern — steady output vs intense vibe sessions |
The Automation Heaviness axis measures how "agentic" your workflow looks based on commit size and breadth. However, initial project commits (scaffolding an entire codebase at once) and bulk operations can skew this metric, especially for small repos.
To address this, we apply commit weight dampening:
| Commit Type | Weight | Rationale |
|---|---|---|
| First commit | 0.25 | Often includes project scaffolding, framework setup |
| Bulk commits (>50% of max files, when max > 20) | 0.5 | Large refactors, dependency updates |
| Normal commits | 1.0 | Typical development work |
This means a repo with 7 commits where the first commit touched 800 files won't be marked as "AI-heavy" just because of the initial setup. The weighted average better reflects actual day-to-day patterns.
Based on your axes, we match you to one of 7 Vibe Personas:
| Persona | Signature Pattern | Tagline |
|---|---|---|
| Vibe Prototyper | High automation, rapid iteration, minimal guardrails | "You prompt fast, ship fast, and let the code evolve" |
| Test-First Validator | Strong guardrails with automation | "You give AI the wheel but keep tests and CI in the passenger seat" |
| Spec-Driven Architect | High planning signal, early guardrails | "You write the spec before the prompt — AI follows your blueprint" |
| Agent Orchestrator | Wide surface area, high automation | "You orchestrate agents across the stack" |
| Hands-On Debugger | Intense fix loops, fast shipping | "You prompt, run, fix, repeat — tight feedback loops" |
| Rapid Risk-Taker | High automation, low guardrails, fast shipping | "You trust the AI output and ship" |
| Reflective Balancer | Balanced across all axes (fallback) | "You blend AI-assisted speed with manual craft" |
Deterministic insights are computed server-side:
- Longest Streak: Consecutive days with commits
- Peak Window: When you code most (mornings, afternoons, evenings, late nights)
- Chunkiness: Slicer (focused), Mixer (balanced), or Chunker (wide scope)
- Tech Signals: Keywords detected in commit messages
- Multi-Agent Signals: Co-author trailers, AI attribution patterns
- AI Tool Metrics: Per-tool usage breakdown from Co-Authored-By trailers (see below)
If LLM is configured, we generate a human-readable narrative about your AI-assisted coding patterns, never about what you built, only how you built it and how AI tools shaped your workflow.
Privacy: The LLM only sees metadata (timestamps, categories, metrics), never commit message content or code.
Vibe Coding Profile detects which AI coding tools you use by parsing Co-Authored-By trailers in your commit messages. Many AI tools automatically add these trailers when they help write code.
- Extract — We parse
Co-Authored-Bytrailers from every commit message. - Identify — Each trailer value is matched against a registry of 11 known AI tools (Claude, GitHub Copilot, Cursor, Aider, Cline, Roo Code, Windsurf, Devin, Codegen, SWE-Agent, Gemini).
- Quantify — We count how many commits each tool co-authored, compute an overall AI collaboration rate, and identify the primary tool.
| Metric | Description |
|---|---|
| AI Collaboration Rate | Fraction of commits with AI co-authorship (0–100%) |
| Primary Tool | The AI tool that appears most frequently |
| Tool Breakdown | Per-tool usage percentages |
| Tool Diversity | Number of different AI tools detected |
| Confidence | high/medium/low based on number of AI-assisted commits |
- Trailer-dependent: If an AI tool doesn't add
Co-Authored-Bytrailers, we can't detect it. IDE autocomplete, copy-paste from ChatGPT, etc. are invisible. - Pattern matching: A human co-author whose name matches a tool pattern (e.g., someone named "Claude") could be counted. This is rare in practice.
- Not usage tracking: We detect presence in commits, not how much of the code the tool wrote.
- Repo VCP: AI tools section on individual repo analysis pages
- Unified VCP: Aggregated across all your repos on the dashboard
- Public Profile: Visible when the "AI Tools" toggle is enabled (default: on)
See AI Tool Metrics Architecture for technical details.
One repo gives you a snapshot of your coding style for that project.
When you analyze multiple repos, Vibe Coding Profile aggregates them into a single Unified VCP:
Repo A Analysis ─┐
Repo B Analysis ─┼─→ Unified VCP
Repo C Analysis ─┘
Aggregation Logic:
- Metrics are weighted by commit count (larger repos contribute more)
- Axes are averaged across repos
- Persona is re-detected from aggregated axes
- Confidence increases with more repos (3+ repos = stronger signal)
Every insight includes a confidence level:
| Level | Meaning |
|---|---|
| High | 200+ commits or 15+ PRs, good data quality |
| Medium | 80+ commits or 6+ PRs |
| Low | Limited data, take insights with a grain of salt |
- Not a productivity tracker: We don't measure "good" vs "bad"
- Not a code quality tool: We don't analyze code, just patterns
- Not an AI policing tool: We show which AI tools you use as a feature, not a judgment. Every coding style has strengths.
- Not surveillance: You control what repos to analyze, data is yours
- No code access: We only read metadata from GitHub API
- No message content to LLM: Commit messages are classified locally, never sent to AI
- User-controlled: You choose which repos to analyze
- Deletable: Disconnect a repo and all analysis data is removed
Vibe Coding Profile builds on research and concepts from the developer tooling community:
- "Vibe coding": Term coined by Andrej Karpathy in February 2025, later named Collins Dictionary Word of the Year 2025.
- Orchestrator vs Conductor patterns: From Addy Osmani's work on agentic coding.
- Code analytics research: Informed by GitClear's developer productivity studies.
- Persona taxonomy: Original synthesis drawing from academic TDD research, GitHub Copilot documentation, and developer workflow studies.
Our internal research documents are available in docs/research/.
- Technical Architecture: Deep dive with Mermaid diagrams
- Vibe Coding Profile Metrics v2: Axis computation details
- AI Tool Metrics: Tool detection pipeline and registry
- PRD: Vibe Coding Profile Narrative Layer: Product requirements
- PRD: Profile Aggregation: Multi-repo aggregation
This document should be updated whenever analysis logic, personas, or metrics change.