Can it track the progress of specific development phases?

Yes, it parses metadata in the .aiwg/planning directory to calculate completion percentages and identify pending gate criteria for phases like Elaboration or Construction.

How does this benefit other Claude Code skills?

It builds a structured context object that other skills (like gate-evaluation or artifact-orchestration) use to ensure their actions are aligned with the current project state.

How does the skill detect the project type?

It scans root configuration files such as package.json, Cargo.toml, go.mod, or pyproject.toml to identify the language, framework, and project structure automatically.

Does it support monorepo structures?

Yes, the detection logic recognizes indicators like turbo.json or lerna.json to distinguish between single projects and complex monorepos.

Project Awareness & Context Engine

Name: Project Awareness & Context Engine
Author: jmagly

byjmagly

•

生産性とワークフロー

Detects and synchronizes project state, technology stacks, and SDLC phases to provide autonomous agents with comprehensive situational awareness.

概要

This skill serves as the central sensory system for AI-driven development environments, automatically identifying tech stacks, team configurations, and project milestones. It integrates with the AI Writing Guide (AIWG) framework to track lifecycle phases—from inception through production—allowing Claude to provide context-aware recommendations, identify blockers, and suggest the most logical next steps based on real-time repository data, git activity, and artifact status.

主な機能

Team role and agent assignment mapping
67 GitHub stars
Real-time git activity and artifact status monitoring
SDLC phase tracking (Inception, Elaboration, Construction, etc.)
Automatic technology stack and framework detection
Intelligent next-step recommendations and blocker identification

ユースケース

Determining if a project is ready to transition between development phases via gate checks
Generating an instant status report of active work, branches, and team responsibilities
Onboarding an AI agent to a complex, multi-layered repository

概要

主な機能

Team role and agent assignment mapping
67 GitHub stars
Real-time git activity and artifact status monitoring
SDLC phase tracking (Inception, Elaboration, Construction, etc.)
Automatic technology stack and framework detection
Intelligent next-step recommendations and blocker identification

ユースケース

Determining if a project is ready to transition between development phases via gate checks
Generating an instant status report of active work, branches, and team responsibilities
Onboarding an AI agent to a complex, multi-layered repository