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Marketplace Framework AI-Driven Dev : Context Engineering, Plugins, Agents, Skills, Hooks, Templates, SDLC
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
85
review score
Problem solved
It solves the fragmentation of AI tools in development by offering a unified, extensible framework for context engineering and agent orchestration. Unlike standalone AI assistants, it enables reusable, composable components (skills, plugins) that integrate with existing SDLC processes.
Deployment reality
The available setup signal starts with: Install via npm: `npm install @ai-driven-dev/framework` or clone the repo and run `npm install`.. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build custom AI agents that automate code generation, review, testing, and deployment. Real-world use cases include automated PR reviews, context-aware code suggestions, and CI/CD pipeline integration. The ceiling is high: with plugins and skills, teams can create domain-specific assistants for any development lifecycle stage.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
ai-driven-dev/framework is tracked as a TypeScript project in the AI Agent area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 328 total stars, with +59 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 30 days ago, and the open issue queue is 39, about 11.89% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 24 forks and 328 watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 59. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyMIT is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
328 Stars
Reuse
24 Forks
Attention
328 Watchers
Maintenance
active
License
MIT
Open issues
39
AI-Driven Dev is a marketplace framework for context engineering, plugins, agents, skills, hooks, templates, and SDLC integration. It provides a modular ecosystem to build, share, and orchestrate AI-powered development workflows.
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
Install via npm: `npm install @ai-driven-dev/framework` or clone the repo and run `npm install`.65
Health Score
Active
Commit Activity
Feb 6, 2026
Created
Jul 24, 2026
Last push
+0
Today's growth
+0
7-day growth
+0
30-day growth
Forks
Open
Watchers
ai-driven-dev
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The agent that grows with you
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✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
It solves the fragmentation of AI tools in development by offering a unified, extensible framework for context engineering and agent orchestration. Unlike standalone AI assistants, it enables reusable, composable components (skills, plugins) that integrate with existing SDLC processes.
Capabilities
Developers can build custom AI agents that automate code generation, review, testing, and deployment. Real-world use cases include automated PR reviews, context-aware code suggestions, and CI/CD pipeline integration. The ceiling is high: with plugins and skills, teams can create domain-specific assistants for any development lifecycle stage.
Bottom Line
This framework is ideal for teams wanting to embed AI into their development workflow with modular, reusable components. Avoid if you need a turnkey AI assistant without customization. The key trade-off is flexibility vs. complexity: powerful but requires upfront investment in setup and skill creation.