@aiready/deps

AIReady Spoke: Analyzes dependency health and calculates AI training-cutoff skew.

Overview

The Dependency Health analyzer evaluates your project manifests to compute timeline skews against AI knowledge cutoff dates.

Language Support

• Supported Manifests: package.json (JS/TS), requirements.txt (Python), pom.xml (Java), go.mod (Go)
• Capabilities: Deprecated detection, training-cutoff skew, version drift.

🏛️ Architecture

                    🎯 USER
                      │
                      ▼
         🎛️  @aiready/cli (orchestrator)
          │     │     │     │     │     │     │     │     │     │
          ▼     ▼     ▼     ▼     ▼     ▼     ▼     ▼     ▼     ▼
        [PAT] [CTX] [CON] [AMP] [DEP] [DOC] [SIG] [AGT] [TST] [CTR]
          │     │     │     │     │     │     │     │     │     │
          └─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┴─────┘
                               │
                               ▼
                      🏢 @aiready/core

Legend:
  PAT = pattern-detect        CTX = context-analyzer
  CON = consistency           AMP = change-amplification
  DEP = deps-health ★         DOC = doc-drift
  SIG = ai-signal-clarity     AGT = agent-grounding
  TST = testability           CTR = contract-enforcement
  ★   = YOU ARE HERE

Features

• Deprecated Detection: Identifies usage of long-deprecated packages.
• Training-Cutoff Skew: Measures your stack's timeline against standard AI knowledge cutoff dates.

Installation

pnpm add @aiready/deps

Usage

aiready scan . --tools deps-health

License

MIT