GitPulse/_bmad-output/planning-artifacts/prd.md

stepsCompleted, inputDocuments, workflowType, documentCounts, classification

stepsCompleted	inputDocuments	workflowType	documentCounts	classification
step-01-init step-02-discovery step-02b-vision step-02c-executive-summary step-03-success step-04-journeys step-05-domain-skipped step-06-innovation step-07-project-type step-08-scoping step-09-functional step-10-nonfunctional step-11-polish step-12-complete	product-brief-gitpulse.md product-brief-gitpulse-distillate.md market-git-dashboard-ia-research-2026-04-24.md	prd	briefs research projectDocs 2 1 0	projectType domain complexity projectContext desktop_app general low greenfield

Product Requirements Document - GitPulse

Author: Ramez Date: 2025-04-25

Executive Summary

GitPulse is a local-first, open source desktop application that serves as the multi-repository command center for developers managing five or more Git repositories on a single machine. It automatically discovers all Git repos on disk, presents their status in a unified visual dashboard, enables batch git operations, and provides model-agnostic AI insights — all without requiring a login, configuration file, or cloud connection.

Target users: Indie developers, freelances, and power users managing 10-50+ repositories across client work, open source contributions, and personal projects. These developers lose significant time daily to manual git status checks, forgotten pushes, stale branches, and the cognitive overhead of tracking repository state across directories.

The problem: No open source tool combines automatic disk discovery, a visual multi-repo dashboard, batch git operations, and AI-powered insights in a performant, cross-platform, zero-configuration package. GitKraken (Electron, paid, 3.1/5 rating) paywalls private repos. GitButler ($17M raised, Tauri/Rust) is single-repo by design and Fair Source licensed. lazygit and gita offer no visual dashboard. The niche is empty.

The product: Built on Tauri v2 with a Rust backend and React frontend, GitPulse delivers a ~15 MB binary with <200 MB RAM. The AI layer is model-agnostic — users bring their own intelligence via Ollama (local, private) or cloud APIs (Gemini, DeepSeek, OpenAI, Anthropic). Privacy is the default; cloud is the opt-in, with a clear visual indicator showing data flow mode. The system tray agent provides passive monitoring without requiring the dashboard to be open.

What Makes This Special

1. Model-agnostic AI infrastructure. Unlike GitKraken (Gemini-only) or GitButler (Claude-only), GitPulse lets developers choose their AI engine per task — Ollama for private commit messages, DeepSeek for cost-effective analysis, Gemini for complex reasoning. The product provides the plumbing; the user provides the intelligence.

2. Zero-friction onboarding. From download to full dashboard in under 30 seconds. No sign-up, no token, no network, no configuration. The first scan — seeing all repositories appear instantly — is the primary conversion moment. Every competitor gates value behind auth flows or manual setup.

3. Structural performance moat. Tauri + Rust produces binaries 10-15x smaller and 3-5x less memory-hungry than Electron competitors. This is architectural — competitors would need to rebuild from scratch to match it.

4. Truly open source (MIT/Apache). GitButler's Fair Source license prohibits building competitors and is not OSI-approved. GitPulse's MIT/Apache licensing allows forking, commercial use, and community trust — a genuine adoption accelerator in the open source ecosystem.

5. Complementary to AI IDEs. Cursor, Copilot, and Windsurf solve intra-repo productivity. GitPulse solves inter-repo visibility. Developers use both without conflict — eliminating the "us vs. the giants" positioning trap.

Project Classification

Attribute	Value
Project Type	Desktop Application (Tauri v2 + React + Rust)
Domain	Developer Productivity / Developer Infrastructure
Complexity	Low (no regulatory requirements; complexity is technical, not compliance-driven)
Project Context	Greenfield — new product, no existing system
License	MIT/Apache (truly open source)
Target Platforms	Windows, macOS, Linux

Success Criteria

User Success

The 5-Second Wow: From launch to a fully populated dashboard in under 30 seconds. No login, no config, no wizard — just instant visibility. This is the primary conversion moment. If the first scan doesn't delight, nothing else matters.

Measurable Success Signals:

• Average repos scanned per user > 10 (validates the multi-repo problem exists for this user)
• Batch operation used within first session (validates the "command center" value proposition)
• Smart Status consulted > 3 times per session (validates AI layer adds real value)
• System tray notification acted upon within 24 hours (validates passive monitoring utility)
• At least 2 AI backends configured per active user (validates model-agnostic approach)

Emotional Success:

• Monday morning relief: "I can see exactly which repos need attention" replaces the dread of 23 git status commands
• Control feeling: "I know the state of all my code" replaces anxiety about forgotten pushes and stale branches
• Trust moment: "My code never leaves my machine" (local mode badge) establishes the privacy foundation

Business Success

Timeframe	Metric	Target
Month 3	GitHub stars	500+
Month 3	Monthly active users	500+
Month 3	Cumulative downloads	2,000+
Month 3	Hacker News Show HN score	200+ upvotes
Month 6	GitHub stars	2,000+
Month 6	Monthly active users	2,000+
Month 6	Paying subscribers (cloud tier)	50
Month 6	D30 retention	40%+
Month 12	GitHub stars	5,000+
Month 12	Monthly active users	10,000+
Month 12	Paying subscribers	200
Month 12	MRR	$1,500+
Month 12	D30 retention	50%+

Fundraising trigger: Seed round ($1–2M) if 5,000 MAU + 3% free-to-cloud conversion by month 9.

North Star & KPIs

North Star KPI: Monthly active users who scan 10+ repositories (validates both adoption and problem-solution fit).

Secondary KPIs:

• D7 retention > 30%, D30 retention > 40% (product stickiness)
• Batch operations per session > 1 (power feature adoption)
• Smart Status engagement rate > 50% of sessions (AI value validation)
• System tray notification action rate > 60% (passive monitoring value)
• GitHub star-to-download ratio > 0.25 (organic enthusiasm signal)

Technical performance targets are defined in the Non-Functional Requirements section (NFR1–NFR8).

Product Scope & Phased Development

MVP Strategy

Approach: Problem-Solving MVP — deliver the core "repo visibility" problem solved in 30 seconds, then layer intelligence and automation on top.

• The first scan IS the product. If it doesn't delight, nothing else matters.
• AI is a progressive enhancement, never a dependency. The product works perfectly without it.
• Zero-friction is non-negotiable. Every config step or login screen is a conversion killer.
• Cross-platform from day 1 — the target audience (indie devs, freelancers) is platform-diverse.
• Resource assumption: Solo developer with potential community contributors post-launch. MVP designed for one-person execution over 2-3 months.

Phase 1 — MVP Core (Weeks 1-6)

• Weeks 1-2: Rust backend — disk scanner, git status parser, Tauri scaffolding
• Weeks 3-4: React frontend — Card Grid, List View, batch operations UI
• Weeks 5-6: System tray + Ollama integration + privacy indicator + packaging
• Delivers: Auto-scan + Dashboard + Batch Ops + System Tray + Smart Status (Ollama) + Zero-config launch

Phase 1.5 — MVP Complete (Weeks 7-10)

• Cloud AI integration (Gemini, DeepSeek, OpenAI, Anthropic)
• Dormancy detection, .gitpulseignore refinements
• Code signing for Windows/macOS, i18n framework scaffolding
• Delivers: Full MVP as originally scoped — all Day 1 features

Must-have capabilities for Day 1 launch:

Capability	Why Must-Have
Auto disk scan	Core value proposition — no scan, no product
Card Grid view	Primary visual interface for pattern recognition
List View with batch ops	Differentiator vs single-repo tools
Per-repo error reporting	Batch ops fail partially — silent failures destroy trust
System tray agent	Passive monitoring = sustained engagement
Smart Status (Ollama)	"Wow" moment — AI-generated health summaries
Privacy indicator	Trust foundation — shows data flow mode
Zero-config launch	Conversion moment — 30 seconds to value
Cross-platform (Win/Mac/Linux)	Target audience is platform-diverse
.gitpulseignore	Privacy control — exclude sensitive repos

Nice-to-have for Day 1 (cut if timeline pressure):

Capability	Rationale	Target
Cloud AI (4 providers)	Ollama covers core AI use case	Phase 1.5
Dormancy detection	Useful but not conversion-critical	Phase 1.5
i18n framework	English sufficient for launch	Phase 2

Phase 2 — Growth (Months 4-6)

• Repository grouping, tagging, workspace contexts
• Advanced Smart Status: cross-repo dependency detection
• File watcher for real-time status updates
• Git worktree integration, CLI companion (gitpulse-cli)
• Premium cloud tier: multi-machine sync, team analytics

Phase 3 — Expansion (Months 7-12)

• Inter-repository dependency graph visualization, MCP support
• Autonomous AI agents for code review and anomaly detection
• GitHub/GitLab integration (PRs, issues, CI status)
• Enterprise tier: SSO, compliance dashboards, audit trails

Explicitly Out of MVP

Cloud sync/team collaboration, built-in code editor/diff viewer, merge conflict resolution UI, CI/CD integration, GitHub/GitLab API integration (issues, PRs, CI), mobile companion, enterprise features (SSO, compliance, audit logs). Telemetry: opt-in only with clear disclosure.

Risk Mitigation

Technical:

Risk	Probability	Impact	Mitigation
Scan performance on slow/network drives	Medium	High	Benchmark early (week 2); async scan with progress; scan timeout
Tauri v2 tray inconsistencies across platforms	Medium	Medium	Use stable tray API; test all 3 platforms weekly
Ollama model loading times	Low	Medium	Smart Status runs async — don't block dashboard
Large repo count (100+) UI perf	Medium	Medium	Virtualized list/grid rendering from day 1
SmartScreen/code signing friction	High	High	Budget for signing cert ($200-500/yr); prioritize in Phase 1.5

Market:

Risk	Probability	Impact	Mitigation
GitButler ships multi-repo first	Low	High	First-mover speed; model-agnostic as durable differentiator
No adoption (tooling fatigue)	Medium	High	Zero-friction is the moat; Show HN as primary launch
AI features perceived as gimmick	Medium	Medium	Deterministic fallback always available
Provider API changes break integration	Medium	Low	Abstract AI behind provider interface

Resource:

Risk	Probability	Impact	Mitigation
Solo dev bandwidth — all platforms	Medium	High	Tauri handles cross-platform; test all OS weekly
Burnout from scope creep	Medium	High	Strict Phase 1/1.5 boundary; cut cloud AI to 1.5 if needed
Post-launch support drain	High	Medium	GitHub Issues triage; community contribution guidelines from day 1

Innovation & Novel Patterns

Detected Innovation Areas

1. Model-Agnostic AI Plumbing for Git (Primary Innovation)

No git tool offers per-task AI engine selection. GitKraken locks to Gemini. GitButler locks to Claude. GitPulse provides the plumbing — the user provides the intelligence. Per-task routing (Ollama for routine commits, cloud for complex analysis) is unprecedented in this category.

Assumption challenged: A git tool must choose its AI provider for the user. GitPulse: the user chooses per task. Validation: Users configuring 2+ AI backends (Success Criteria metric).

2. Vacant Quadrant — Auto-Discovery + Dashboard + Batch + AI

No open source tool occupies the intersection of automatic disk discovery, multi-repo visual dashboard, batch git operations, and AI-powered insights. This is a first-mover position, not incremental improvement.

Assumption challenged: Developers don't need a dedicated multi-repo tool — shell scripts and CLI suffice. GitPulse: instant visual visibility fundamentally changes the experience. Validation: D7 retention > 30% validates visibility as a game-changer.

3. Multi-Level Privacy Indicator

The visual badge switching between "Your code never leaves your machine" (Ollama) and "Cloud — data transits to [provider]" is novel UX. No competitor offers this transparency. Aligns with post-Copilot data policy momentum (April 2025).

Assumption challenged: Developers trust tools by default. GitPulse: in a post-Copilot world, trust is earned through explicit transparency. Validation: If local mode remains most-used even when cloud keys are configured, privacy-first hypothesis is validated.

4. Zero-Friction as Architectural Competitive Advantage

No login, no config, no wizard. The first scan is the conversion moment. Made possible by Tauri + Rust architecture (<30 seconds total). Electron competitors physically cannot deliver this experience.

Assumption challenged: Guided onboarding improves retention. GitPulse: retention comes from instant value, not hand-holding. Validation: Time from download to first batch operation < 2 minutes.

Validation Approach

Innovation	Validation Method	Success Signal
Model-agnostic AI	Track backend configuration count per user	2+ backends per active user
Vacant quadrant	D7/D30 retention + repos scanned per user	D7 > 30%, avg repos > 10
Privacy indicator	Local vs cloud usage ratio	Local mode dominant even with cloud configured
Zero-friction	Time-to-first-action measurement	< 2 minutes download to first batch op

Innovation Risk Mitigation

Risk	Mitigation
Model-agnostic too complex to configure	Smart defaults: Ollama auto-detected, cloud as opt-in guided setup
Users stay in "no AI" mode	Smart Status works in deterministic mode — AI is enhancement, not dependency
Performance degrades at 100+ repos	Lazy loading, pagination, incremental scan
GitButler adds multi-repo	First-mover + MIT/Apache vs Fair Source + model-agnostic as durable differentiator

User Journeys

Journey 1: Sarah's Monday Morning (Indie Dev — Happy Path)

Persona: Sarah, 32, freelance full-stack developer. She juggles 23 repositories: 8 client projects, 6 open source contributions, 5 personal experiments, and 4 configuration/utility repos. She context-switches daily and routinely loses track of uncommitted changes.

Opening Scene: Monday 9 AM. Sarah sits down with coffee, dreading the ritual of checking each repo. She's been burned before — last month she lost 3 hours of work in a client repo because she forgot to push before switching machines.

Rising Action: She launches GitPulse for the first time. No login screen. No setup wizard. The binary opens instantly and begins scanning her home directory. In under 30 seconds, 23 tiles populate the dashboard — each showing branch name, status color (green/yellow/red), ahead/behind count, and last commit timestamp.

Climax: Three tiles immediately catch her eye:

• client-acme-app — yellow, "3 uncommitted changes, last activity Friday 6 PM"
• oss-contrib-lib — red, "12 commits behind origin/main"
• personal-scripts — yellow, "uncommitted changes, 2 weeks ago"

She selects all three with Shift+Click, clicks "Batch Pull" — the two behind repos update in seconds. For the uncommitted changes, she clicks into client-acme-app, sees the diff summary, and Ollama suggests a commit message: "Update authentication middleware for session timeout handling." She accepts, commits, pushes. Total time: 2 minutes.

Resolution: Her entire repo fleet is green. She minimizes GitPulse to the system tray and starts coding with confidence. The tray icon will pulse if anything changes during the day — she doesn't need to check again.

Emotional arc: Dread → surprise (it just works) → relief (visible clarity) → confidence (everything handled) → trust (passive monitoring has her back).

Capabilities revealed: Auto-discovery, Card Grid view, batch operations, Smart Status (Ollama), commit message suggestions, system tray agent, per-repo status visualization.

---

Journey 2: Leo's Post-Sprint Audit (Power User / Tech Lead — Advanced Workflow)

Persona: Leo, 38, senior backend engineer at a mid-size SaaS company. He manages 18 microservice repositories in a monorepo-like architecture. His team just finished a 2-week sprint, and he needs to verify all services are synchronized and identify any cross-repo dependencies that need attention.

Opening Scene: Sprint retrospective just ended. Leo needs to confirm all 18 services are on the latest version and identify any cascade effects from a shared library update.

Rising Action: He opens GitPulse and switches to List View — sortable rows let him see all 18 repos at once. He clicks the "Behind" column header — 7 repos are behind their remotes. He selects all 7, executes "Batch Pull." Two fail with merge conflicts (clearly reported per-repo with error details). Five succeed.

Climax: For the two conflicts, Leo doesn't panic — the per-repo error report shows exactly which files conflict. He switches to his terminal for manual resolution (GitPulse doesn't do merge conflict UI in MVP). Back in GitPulse, he refreshes — both now show green.

Now he enables Smart Status in cloud mode (his Gemini API key is already configured). The privacy badge shifts from "Local" to "Cloud — data transits to Google." He runs a cross-repo health analysis. Gemini reports: "shared-auth-lib was updated in 5 repos but not in payment-service and notification-service — these may need version bumps." He selects the two flagged repos, pulls, and verifies.

Resolution: All 18 repos green. He screenshots the dashboard for his team standup. The Smart Status saved him from a potential auth token mismatch in production.

Emotional arc: Responsibility → systematic control → alert (conflicts detected, handled) → insight (AI catches what manual checks missed) → professional confidence.

Capabilities revealed: List View with sorting/filtering, batch pull with per-repo error reporting, Smart Status cloud mode (Gemini), cross-repo dependency analysis, privacy indicator, graceful error handling with partial success UX.

---

Journey 3: Max's Great Cleanup (Maker / Side-Project Builder — Dormancy Discovery)

Persona: Max, 28, creative developer with a habit of starting projects. He has 47 repositories — some active, many dormant, a few he's forgotten entirely. He hasn't done a cleanup in over a year.

Opening Scene: It's a Saturday afternoon. Max decides it's time to organize his digital life. He's vaguely aware that some old projects might have uncommitted changes or API keys he should secure.

Rising Action: He launches GitPulse. The scan finds 47 repos across 4 directories. The Card Grid is overwhelming at first — too many tiles. He switches to List View and sorts by "Last Activity." The Smart Status (Ollama, local mode — privacy matters here) generates summaries:

• 12 repos dormant for 3+ months
• 3 repos with uncommitted changes older than 2 weeks
• 8 repos with unmerged feature branches
• 2 repos with remote URLs pointing to deleted GitHub repositories

Climax: Max creates a mental triage:

1. The 3 repos with uncommitted changes — he reviews each, commits what's worth keeping, discards the rest
2. The 2 repos with deleted remotes — he archives them locally
3. The 8 repos with stale branches — he bulk-deletes branches that were clearly experiments (branch names like test-thing, wip-new-api)

For the 12 dormant repos, he doesn't delete anything — but he tags them "dormant" for future reference (a Phase 2 feature — for now he makes a mental note).

Resolution: In 20 minutes, Max has cleaned up 15 repos. His dashboard now shows a manageable set of active projects. He feels lighter — like cleaning a cluttered desk.

Emotional arc: Overwhelm → discovery (visual clarity from chaos) → triage control → satisfaction (order restored) → lightness.

Capabilities revealed: Large repo count handling, List View sorting by activity, Smart Status dormancy detection (Ollama), commit message suggestions for forgotten changes, batch operations for cleanup, clear per-repo status even at scale.

---

Journey 4: Jay's 30-Second Trial (Newcomer — Conversion Moment)

Persona: Jay, 25, junior developer. He saw GitPulse on Hacker News Show HN with 200+ upvotes. He has only 5 repos — mostly personal projects and a fork of a popular library. He's curious but skeptical.

Opening Scene: Jay clicks the download link. 15 MB binary. No installer — he double-clicks the executable. On Windows, SmartScreen shows an "unrecognized app" warning (a known friction point for new desktop apps). He clicks "Run anyway."

Rising Action: GitPulse opens. No login screen. No "Welcome to GitPulse!" onboarding overlay. Just a clean window asking which directories to scan. He selects his ~/Projects folder. 5 repos appear in under 3 seconds. Each tile shows meaningful data — branch, status, last commit.

Climax: Jay doesn't have 50 repos, so the multi-repo value isn't immediately life-changing. But he notices one tile is yellow — a personal project with uncommitted changes from 3 weeks ago that he forgot about. He clicks it, sees a Smart Status summary: "2 files modified, likely CSS adjustments based on file names." He doesn't even need AI to remember what it was — the visibility alone solved the problem.

He decides to try the AI feature. He configures Ollama (already installed from previous experiments), runs Smart Status on his forked library. The one-line summary is genuinely useful: "Up to date with upstream, 1 local modification in README." He stars the GitHub repo.

Resolution: Jay keeps GitPulse running in the system tray. With 5 repos it's not essential — but it's lightweight enough to justify keeping. When his repo count grows (new job, more side projects), GitPulse will already be there.

Emotional arc: Curiosity → skepticism → mild surprise (it works immediately) → small delight (found forgotten changes) → investment decision (keeping it for future value).

Capabilities revealed: Small footprint binary download, zero-friction launch, SmartScreen handling (implicit requirement), small repo count UX (dashboard shouldn't feel empty with <10 repos), Smart Status value even at small scale, system tray persistence.

---

Journey Requirements Summary

Capability Area	Journeys Revealing Need	Priority
Auto Discovery	Sarah, Max, Jay	MVP
Card Grid View	Sarah, Jay	MVP
List View (sort/filter/select)	Leo, Max	MVP
Batch Operations	Sarah, Leo, Max	MVP
Per-repo Error Reporting	Leo	MVP
System Tray Agent	Sarah, Jay	MVP
Smart Status — Ollama	Sarah, Max, Jay	MVP
Smart Status — Cloud APIs	Leo	MVP
Privacy Indicator	Leo	MVP
Commit Message Suggestions	Sarah, Max	MVP
Dormancy Detection	Max, Jay	MVP
Cross-repo Analysis	Leo	MVP
Small Repo Count UX	Jay	MVP
Download/First-launch UX	Jay	MVP
Repo Tagging/Grouping	Max	Phase 2
SmartScreen/Code Signing	Jay	MVP (implicit)

Functional Requirements

Repository Discovery

• FR1: The user can initiate a recursive disk scan to discover Git repositories on their machine
• FR2: The user can configure which root directories to scan
• FR3: The user can exclude directories from scanning via a .gitpulseignore file (glob syntax)
• FR4: The system can detect Git repositories by identifying .git folders
• FR5: The user can manually trigger a rescan to refresh the repository list
• FR6: The system can gracefully handle inaccessible directories (skip and log, no crash)
• FR7: The system can cache scan results for instant reload on subsequent launches

Dashboard & Visualization

• FR8: The user can view repositories in a card grid showing branch, status (clean/dirty/ahead/behind), last commit, and stash count
• FR9: The user can view repositories in a sortable, filterable list view with multi-select
• FR10: The user can toggle between card grid and list view
• FR11: The user can sort repositories by status, last activity date, branch name, or ahead/behind count
• FR12: The user can filter repositories by status (clean, dirty, ahead, behind, dormant)
• FR13: The system can display visual status indicators (color/badges) for instant pattern recognition across all repos
• FR14: The system can render large repository lists (100+) without UI degradation

Batch Operations

• FR15: The user can select multiple repositories and execute batch git pull
• FR16: The user can select multiple repositories and execute batch git push
• FR17: The user can select multiple repositories and execute batch git fetch
• FR18: The user can batch git status across multiple repositories
• FR19: The system can report per-repo results for batch operations (success/failure with details)
• FR20: The system can handle partial success — repos that succeed continue, repos that fail are reported with error details
• FR21: The user can see specific error details (e.g., merge conflicts, auth errors) for failed repos

Smart Status (AI Integration)

• FR22: The user can enable Ollama integration for local, private repository health summaries
• FR23: The user can configure cloud API keys (Gemini, DeepSeek, OpenAI, Anthropic) for AI-powered analysis
• FR24: The system can generate one-line repository health summaries via the configured AI backend
• FR25: The system can suggest commit messages based on staged changes via the configured AI backend
• FR26: The system can detect and flag dormant repositories beyond a configurable threshold
• FR27: The user can see a visual privacy indicator showing whether data processing is local or cloud-based
• FR28: The system can operate entirely without any AI backend configured (deterministic status parsing as default)
• FR29: The user can route different AI tasks to different providers (e.g., Ollama for commits, cloud for analysis)
• FR30: The system can provide cross-repo analysis when using a cloud AI model (e.g., shared library dependencies across repos)

System Tray Agent & Notifications

• FR31: The user can run GitPulse as a background system tray agent with the dashboard closed
• FR32: The system can monitor for remote changes and alert the user via native OS notifications
• FR33: The system can send push reminders for repos with unpushed commits older than a configurable threshold
• FR34: The user can configure GitPulse to auto-launch at system startup (opt-in)
• FR35: The user can access common operations (status, pull, push) via the system tray context menu

Settings & Configuration

• FR36: The user can configure scan root directories via a settings interface
• FR37: The user can configure AI backend settings (Ollama endpoint, cloud API keys, per-task routing)
• FR38: The user can configure notification behavior (frequency, types, thresholds)
• FR39: The user can configure system tray agent behavior (launch at startup, monitoring interval)
• FR40: User settings are persisted locally and survive application restarts
• FR41: The user can reset all settings to defaults

Onboarding & Distribution

• FR42: The user can launch GitPulse without any account, login, or network configuration
• FR43: The system can operate fully offline (all core features work without network access)
• FR44: The user can manually check for application updates via system tray or settings
• FR45: The user can download GitPulse as a single binary (no installer wizard required)
• FR46: The system can run on Windows, macOS, and Linux from initial release

Non-Functional Requirements

Performance

ID	Requirement	Measurement	Target
NFR1	Cold startup	Time from launch to interactive dashboard	< 3 seconds
NFR2	Disk scan speed	Time to discover all repositories	< 5 seconds (SSD, <100 repos)
NFR3	Memory usage	RAM during normal operation	< 200 MB with 50+ repos
NFR4	Binary size	Distribution artifact size	~15 MB
NFR5	Batch operation throughput	Time to pull/push 20 repos	< 30 seconds
NFR6	UI rendering responsiveness	No perceptible jank during scrolling or view switching	< 16ms per frame (60fps)
NFR7	Async AI loading	Smart Status does not block dashboard	AI results appear asynchronously; UI stays responsive
NFR8	Incremental scan	Reload scan on subsequent launch	< 1 second from cache

Security

ID	Requirement	Details
NFR9	API key storage	Cloud API keys stored using OS keychain/secure storage, not plaintext in config files
NFR10	Privacy by default	No repository data leaves the machine unless user explicitly enables a cloud backend
NFR11	Data flow transparency	Visual indicator always clearly shows whether data transits to a cloud provider
NFR12	Scan scope control	Directories excluded via .gitpulseignore are never scanned or indexed
NFR13	Zero telemetry by default	No usage data collected unless user explicitly opts in
NFR14	Git execution isolation	Git commands run in isolated processes; a malicious repo cannot compromise the application
NFR15	Dependency auditing	Rust and npm dependencies audited for known vulnerabilities before each release

Integration

ID	Requirement	Details
NFR16	Ollama version compatibility	Supports Ollama stable releases (API v1) with graceful fallback on version mismatch
NFR17	Cloud API compliance	Supports stable API versions for Gemini, DeepSeek, OpenAI, Anthropic; handles API error responses and rate limits gracefully
NFR18	Git version compatibility	Supports Git >= 2.20 on all platforms; graceful fallback with clear error if Git is missing or too old
NFR19	Native OS integration	Uses platform-native system tray APIs, notification centers, and startup launch methods — no workarounds
NFR20	AI offline resilience	Ollama failure or cloud unavailability does not block any feature — deterministic fallback with AI status indication

Reliability

ID	Requirement	Details
NFR21	Scan fault tolerance	Scan completes even if individual directories are inaccessible, corrupted, or permission-denied
NFR22	Batch fault tolerance	Batch operations complete for successful repos even when individual repos fail
NFR23	Crash recovery	Application state preserved across crashes — reopening does not require reconfiguration
NFR24	User data persistence	Settings, scan configurations, and API keys survive application updates

Accessibility

ID	Requirement	Details
NFR25	Keyboard navigation	All core operations (selection, batch ops, view switching) accessible via keyboard
NFR26	High contrast support	Visual status indicators remain distinguishable in high contrast/dark mode
NFR27	Screen reader compatibility	Key dashboard components use ARIA markup for basic screen reader support

Desktop Application Specific Requirements

Platform Support

Platform	Architecture	Distribution	Notes
Windows	x64	.exe (portable) + optional MSI	SmartScreen handling required (code signing needed)
macOS	x64 + ARM (Apple Silicon)	.dmg	Universal binary preferred; Apple notarization required
Linux	x64	.deb + .AppImage + .rpm	AppImage for zero-install, deb/rpm for package managers

Tauri v2 strategy: Native webview per platform (WebView2/WebKit/WebKitGTK). Rust backend compiles identically across platforms. React frontend platform-agnostic. Platform-specific code (system tray, auto-launch) isolated behind feature flags.

System Integration

• System tray: Windows (native via Tauri), macOS (menu bar), Linux (D-Bus/libappindicator)
• Notifications: Native OS notifications (Windows Action Center, macOS Notification Center, libnotify)
• Auto-launch: Opt-in via settings. Windows Registry, macOS LaunchAgent, Linux XDG autostart
• Disk scanner: Recursive .git detection with .gitpulseignore support (glob syntax)

Update Strategy

• Manual update check via system tray / settings; GitHub Releases as distribution channel
• No forced auto-update — respect user control
• Update check is the only outbound network request in local-only mode
• Post-MVP: Tauri built-in updater (opt-in), delta updates
• Code signing required: Authenticode (Windows), Apple Developer + notarization (macOS), GPG (Linux)

Offline Capabilities

• Core functionality (scan, dashboard, batch ops, deterministic status) requires zero network
• Ollama works entirely offline when running locally
• Cloud AI gracefully degrades to deterministic parsing
• Settings stored locally (JSON/TOML in app data directory)
• Scan results cached for instant reload on subsequent launches