| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Orchestrates a linear 8-stage workflow (Documentation → Spec → Red Team Review → Quality Gate → Code Review Guide → AI Prompt → CI/CD → Migration) using a WorkflowEngine that enforces a mandatory 80+ quality score threshold at Stage 4 before proceeding to implementation stages. Each stage generates artifacts that feed into the next, creating an auditable chain of custody from requirements to production-ready code. The pipeline uses scenario detection and domain-aware context to adapt generation strategies based on project type and tech stack.
Unique: Implements a mandatory quality gate (Stage 4) with 80+ score threshold that blocks progression to implementation stages, combined with a red team review stage (Stage 3) that proactively identifies risks before code generation — this two-layer quality enforcement is distinct from tools that generate code first and review later
vs alternatives: Unlike Cursor or Claude Code which generate code directly from prompts, Super Dev enforces spec-first development with mandatory quality gates and red team review, reducing implementation rework and ensuring auditable decision trails
The DocumentGenerator class produces three categories of human-readable artifacts (PRD, Architecture, UI/UX) by leveraging domain knowledge (6 business domains × 4 tech platforms × common patterns) and project analysis results. Generation is context-aware: it detects project type (e.g., SaaS, mobile app, API service) and tech stack (e.g., React + Node.js + PostgreSQL) and adapts templates and content accordingly. Uses Claude to synthesize requirements into structured documents with sections for acceptance criteria, non-functional requirements, and architectural constraints.
Unique: Combines domain-aware generation (6 business domains × 4 tech platforms) with project analysis to produce tech-stack-specific documentation, rather than generic templates — e.g., generates different architecture docs for React+Node vs. Django+PostgreSQL
vs alternatives: Produces domain and tech-stack-aware documentation that reflects project context, whereas generic doc generators (Notion templates, ChatGPT) produce one-size-fits-all output without architectural awareness
Stage 5 of the pipeline that generates detailed code review guidelines and checklists specific to the project's architecture, tech stack, and quality standards. The guide includes acceptance criteria from specs, architectural compliance checks (e.g., microservices isolation, API contract validation), performance benchmarks, security requirements, and testing expectations. Formatted as a structured document that human reviewers or AI tools can follow during code review, with specific checks tied to the generated specifications and architecture documentation.
Unique: Generates spec-aligned code review guidelines with architectural compliance checks tied to generated specifications, rather than generic review templates
vs alternatives: Produces specification-aligned code review guidelines with architectural compliance checks, whereas generic code review tools (Gerrit, GitHub) provide generic frameworks without spec-driven context
Super Dev operates in two distinct modes that share core engines: (1) CLI tool for standalone artifact generation (specs, docs, prompts, CI/CD, migrations), and (2) Agent Skills for integration with Claude Code and other AI IDEs via OpenClaw/MCP protocols. The dual architecture enables both batch processing workflows (CLI) and interactive development workflows (agent skills). Both modes use the same underlying components (DocumentGenerator, ProjectAnalyzer, QualityGateChecker, etc.) but expose different interfaces and integration points.
Unique: Implements a dual-mode architecture where CLI tool and Claude Code agent skills share the same core engines (DocumentGenerator, QualityGateChecker, etc.), enabling consistent quality standards and reusable components across batch and interactive workflows
vs alternatives: Provides both CLI and IDE integration with shared core engines, whereas most tools focus on one interface (CLI or IDE) and require separate implementations
A WorkflowContext system that maintains state across the 8-stage pipeline, tracking artifacts, quality scores, approvals, and decisions at each stage. Implements an enforcement layer that ensures mandatory quality gates are met before stage progression and prevents skipping stages. Uses a memory system to persist workflow state (local or cloud-based) and enable resumption of interrupted workflows. Provides audit trails of all decisions, approvals, and quality checks for compliance and traceability.
Unique: Implements a stateful workflow context with mandatory enforcement of quality gates and audit trail tracking across the 8-stage pipeline, enabling resumption and compliance tracking — most tools are stateless or provide only basic logging
vs alternatives: Provides stateful workflow management with mandatory quality gate enforcement and audit trails, whereas most tools are stateless and require external workflow orchestration (Jenkins, Airflow)
Implements a spec-first development model where specifications are generated before code, and changes are tracked as delta specifications rather than code diffs. The SDD workflow manages a directory structure that separates specs, designs, and code artifacts, and tracks the lifecycle of each change (proposed → reviewed → approved → implemented). Uses OpenSpec format (machine-readable specification standard) to enable AI tools to consume specs directly. Supports incremental updates via delta specifications that describe only what changed, reducing context bloat for iterative development.
Unique: Tracks changes as delta specifications (spec-level diffs) rather than code diffs, enabling spec-first change management and reducing context for iterative development — most tools track code changes, not specification changes
vs alternatives: Enables spec-first development with delta specifications for incremental changes, whereas traditional workflows (Git-based) track code changes after the fact, losing specification-level intent
A design asset repository system that indexes design patterns, components, and tokens using BM25+ full-text search, enabling semantic retrieval of relevant design assets for new features. The engine generates design systems and design tokens (color palettes, typography, spacing scales) based on project context and tech stack. Uses a Design Asset Repository to store and retrieve design patterns, and a Design System Generator to synthesize tokens and component specifications from project analysis and domain knowledge.
Unique: Implements BM25+ full-text search over design assets combined with design token generation, enabling semantic retrieval and synthesis of design specifications — most design tools focus on visual editing, not specification generation
vs alternatives: Provides semantic search over design assets and auto-generates design tokens and specifications, whereas design tools (Figma, Sketch) focus on visual design and require manual specification extraction
An expert system that models domain expertise through expert personas (e.g., Backend Architect, Frontend Engineer, QA Lead) with associated knowledge bases and skills. Each persona has specialized knowledge for their domain and can be invoked as an agent skill in Claude Code or other AI IDEs. The system integrates with agent skill frameworks (OpenClaw, MCP) to expose expert personas as callable functions that AI tools can invoke during development. Uses a knowledge base per persona to provide context-specific guidance and best practices.
Unique: Models domain expertise as callable agent personas that integrate with Claude Code and other AI IDEs via OpenClaw/MCP, enabling AI tools to consult expert knowledge during development — most tools embed expertise as static rules, not interactive personas
vs alternatives: Provides interactive expert personas as agent skills that AI tools can invoke, whereas linters and style guides are passive and require manual consultation
+5 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs super-dev at 34/100. super-dev leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities