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OS

Fission-AI/OpenSpec

AI Agent

Spec-driven development (SDD) for AI coding assistants.

62k Stars4.3k Forks372 Open Issues62k WatchersTypeScriptMIT
AI App
Review Readiness

This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.

100

review score

Indexable review
Decision Snapshot

Problem solved

OpenSpec solves the problem of AI agents generating code without proper context or planning, leading to misaligned or incomplete implementations. Unlike ad-hoc prompting, it enforces a spec-first workflow where requirements, design, and tasks are documented and reviewed before coding, making it suitable for brownfield projects and team collaboration.

Deployment reality

The available setup signal starts with: npm install -g @fission-ai/openspec@latest. 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 features end-to-end with AI assistance, from exploration and proposal to implementation and archiving. Real-world use cases include adding dark mode, cross-repo features, and shared requirements across teams. The ceiling includes managing complex, multi-repo projects with a single source of truth for specs and changes.

Source and compliance noteLast synced: Jul 24, 2026

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.

Original GitHub sourceMethodologyEditorial Policy
Editorial Evaluation

Fission-AI/OpenSpec is tracked as a TypeScript project in the AI App 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 62k total stars, with +191 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 Moderate; the latest push was 30 days ago, and the open issue queue is 372, about 0.60% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.

Adoption check: 4.3k forks and 62k 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 191. Follow the original GitHub repository for final install, security, and release information.

Evidence Checklist

Analysis

Ready

Original problem, capability, and verdict guidance are available.

Sources

Ready

Repository metadata and README/source references are attached.

License

Ready

MIT is recorded for review.

Maintenance

Ready

Recent activity is visible in repository metadata.

Alternatives

Ready

Enough nearby projects exist for comparison.

Best For
  • AI prototypes, LLM workflows, and agent-style applications
  • TypeScript teams evaluating ecosystem-native tooling
  • teams that prefer mature projects with broad adoption signals
Avoid When
  • you need a legal review, security audit, or production SLA
Adoption Signals

Momentum

62k Stars

Reuse

4.3k Forks

Attention

62k Watchers

Maintenance

moderate

License

MIT

Open issues

372

Overview

OpenSpec is a specification framework that helps teams and AI agents plan, document, and implement features collaboratively. It provides a structured workflow for exploring ideas, proposing changes, writing specs, and tracking implementation tasks.

Key Features

- Artifact-guided workflow with slash commands (/opsx:explore, /opsx:propose, /opsx:apply, /opsx:archive) - Stores for cross-repo planning and shared requirements - Plain Markdown specs with concrete scenarios, no special syntax needed

Tool Positioning

AI App

End-user AI applications and AI-native product examples

Quick Start
npm install -g @fission-ai/openspec@latest
View on GitHub Project Homepage
Project Activity

70

Health Score

Moderate

Commit Activity

Aug 5, 2025

Created

Jul 23, 2026

Last push

Source Trail

GitHub repository metadata

metadata

GitHub README

readme_summary

Star History

Star history will appear after daily syncs accumulate data.

Community Health
4.3k

Forks

372

Open

62k

Watchers

Owner
OS

Fission-AI

GitHub profile
Topics & Language
TypeScriptaicontext-engineeringengineeringplanningprdsddsdlcspecspec-driven-developmentspecification
Ecosystem & Usage
GitHub Repository Project Website Search on npm
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License
MIT
CreatedAug 5, 2025
Last pushJul 23, 2026
Last syncedJul 24, 2026
Repository Standards

✓

License

✓

Forked

~ Moderate

Maintained

AI AnalysisAnalyzed by Git-Stars

Problem Solved

OpenSpec solves the problem of AI agents generating code without proper context or planning, leading to misaligned or incomplete implementations. Unlike ad-hoc prompting, it enforces a spec-first workflow where requirements, design, and tasks are documented and reviewed before coding, making it suitable for brownfield projects and team collaboration.

Capabilities

Developers can build features end-to-end with AI assistance, from exploration and proposal to implementation and archiving. Real-world use cases include adding dark mode, cross-repo features, and shared requirements across teams. The ceiling includes managing complex, multi-repo projects with a single source of truth for specs and changes.

Bottom Line

OpenSpec is ideal for developers and teams using AI coding assistants who want structured, reviewable planning before code generation. It's less suited for rapid prototyping or solo developers who prefer minimal overhead. The key trade-off is upfront spec effort versus reduced rework and improved alignment.

Full AI Analysis