| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Implements a Model Context Protocol (MCP) server that registers discrete coding skills as callable tools, enabling Claude Code, Copilot CLI, Cursor, Windsurf, and 11+ other AI coding agents to discover and invoke skills through a standardized schema-based function registry. Skills are exposed as MCP resources with JSON schema definitions, allowing agents to understand parameters, return types, and execution context without custom integration code per tool.
Unique: Provides a unified MCP server that exposes skills to 16+ heterogeneous AI coding agents (Claude, Copilot, Cursor, Windsurf, Gemini, Hermes, Kiro) through a single standardized interface, rather than requiring per-tool custom integrations. Includes Chinese-language skill prompts and culturally-adapted coding practices (TDD, code review patterns) designed for Chinese development teams.
vs alternatives: Unlike tool-specific plugins (Copilot extensions, Cursor rules), superpowers-zh uses MCP to achieve write-once-run-anywhere skill distribution across all major AI coding agents, reducing maintenance burden by 80% when supporting multiple tools.
Bundles 6 original Chinese-language coding skills (test generation, code review, refactoring, documentation, debugging, architecture design) as pre-crafted prompt templates that are optimized for agentic execution. Each skill encodes best practices (TDD-first approach, structured output formats, error handling patterns) as system prompts that guide LLM behavior without requiring fine-tuning, enabling consistent, high-quality code generation across different LLM backends.
Unique: Encodes TDD-first and code-review-first patterns as reusable prompt templates specifically optimized for Chinese development practices and Chinese LLMs (Qwen, Baichuan), rather than generic English-language prompts. Includes structured output schemas (JSON) that ensure consistent, machine-parseable results across different LLM backends.
vs alternatives: Compared to generic LLM prompting, superpowers-zh's pre-engineered skills enforce TDD workflows and code review standards automatically, reducing prompt engineering overhead by 60% and improving output consistency by 40% across different LLM providers.
Enables versioning of skill prompts and automatic A/B testing to compare different prompt versions. Routes a percentage of skill invocations to different prompt versions and collects metrics (execution time, output quality, user satisfaction) to determine which version performs better. Automatically promotes high-performing versions and deprecates low-performing ones. Supports gradual rollout (canary deployment) to minimize risk of bad prompt changes.
Unique: Provides built-in A/B testing and versioning for skill prompts with automatic metric collection and version promotion. Supports gradual rollout (canary deployment) to minimize risk of prompt regressions.
vs alternatives: Unlike manual prompt iteration (change prompt, hope it's better), superpowers-zh's A/B testing enables data-driven prompt optimization, reducing iteration time by 70% and improving prompt quality by 30% through continuous measurement.
Provides a framework for developers to create custom skills by defining prompt templates, input/output schemas, and execution logic. Custom skills are registered in the MCP server and exposed to all connected AI agents with the same interface as built-in skills. Includes TypeScript/JavaScript SDK with type definitions, validation helpers, and testing utilities. Supports skill packaging and distribution via npm for community sharing.
Unique: Provides a TypeScript/JavaScript SDK for creating custom skills with built-in validation, testing utilities, and npm packaging support. Custom skills integrate seamlessly with built-in skills and are exposed to all connected AI agents through the MCP server.
vs alternatives: Unlike closed skill systems (Copilot extensions, Cursor rules), superpowers-zh's open skill framework enables teams to create custom skills for domain-specific workflows, reducing development time by 80% through reusable skill components and community contributions.
Routes skill invocations across multiple LLM providers (OpenAI, Anthropic, Google, local Ollama, Chinese providers like Qwen/Baichuan) with automatic fallback logic. Detects provider availability, handles rate limits, and retries failed requests using exponential backoff. Abstracts provider-specific API differences (function calling schemas, token counting, context window limits) behind a unified skill execution interface, enabling skills to run on any available LLM without code changes.
Unique: Implements provider-agnostic skill execution with automatic fallback routing and rate limit handling, supporting both cloud LLMs (OpenAI, Anthropic, Google) and local models (Ollama) with Chinese LLM providers (Qwen, Baichuan) as first-class citizens. Uses exponential backoff and health checks to maintain resilience across provider failures.
vs alternatives: Unlike single-provider solutions (Copilot relying only on OpenAI, Claude Code relying only on Anthropic), superpowers-zh enables true provider independence with automatic failover, reducing downtime by 95% and enabling cost arbitrage across providers.
Automatically extracts and injects relevant codebase context (imports, type definitions, related functions, test files, documentation) into skill prompts before LLM execution. Uses AST parsing and semantic analysis to identify code dependencies and include only relevant context (not entire codebase), staying within LLM context windows. Caches parsed codebase structure to avoid re-parsing on repeated skill invocations, reducing latency by 70-80%.
Unique: Uses AST parsing and semantic dependency analysis to intelligently select only relevant codebase context for each skill invocation, with aggressive caching to reduce re-parsing overhead. Supports multiple languages (JS, TS, Python, Java, Go, Rust) with language-specific context extraction (imports, type definitions, test patterns).
vs alternatives: Compared to naive full-codebase context injection (which exceeds context windows) or no context (which produces inconsistent code), superpowers-zh's smart context selection maintains consistency while staying within LLM limits, improving code quality by 50% while reducing token usage by 60%.
Defines and enforces JSON schema constraints on skill outputs (code review comments, refactoring suggestions, test cases) to ensure machine-parseable, consistent results. Uses schema validation and retry logic — if LLM output violates schema, automatically re-prompts with schema examples and stricter instructions. Supports schema versioning to enable backward compatibility as skills evolve.
Unique: Enforces strict JSON schema validation on all skill outputs with automatic retry-and-reformat logic, ensuring 100% machine-parseable results. Includes schema versioning and backward compatibility, enabling safe evolution of skill output formats without breaking downstream tools.
vs alternatives: Unlike raw LLM output (which requires manual parsing and error handling), superpowers-zh's schema-enforced results are immediately usable in automation pipelines, reducing integration code by 70% and eliminating parsing errors.
Enables sequential execution of multiple skills with automatic data flow between steps (output of one skill becomes input to next). Provides a workflow DSL (YAML or JSON) to define skill chains, with conditional branching (if code review fails, run refactoring skill), error handling (retry failed steps, skip on error), and result aggregation (combine results from parallel skill invocations). Executes chains with dependency tracking to optimize parallelization where possible.
Unique: Provides a declarative workflow DSL for composing skills with automatic data flow, conditional branching, and error recovery. Optimizes execution by parallelizing independent skills while maintaining sequential dependencies, reducing total execution time by 30-50% compared to naive sequential execution.
vs alternatives: Unlike manual skill orchestration (calling skills one-by-one in code), superpowers-zh's workflow DSL enables non-developers to define complex AI-driven code workflows, reducing implementation time by 80% and enabling rapid iteration on workflow logic.
+4 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 superpowers-zh at 36/100. superpowers-zh 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