v0.6.0 - Complete Skillchain Validation Architecture

AI Design Components v0.6.0

Major milestone release implementing the complete Skillchain Improvement Plan, solving the "58% completeness problem" with end-to-end output validation.

Highlights

• Chain Context System - Shared state tracking across skillchain execution
• Validation Framework - 5 core validation functions with remediation flow
• 76 outputs.yaml files - Every skill declares its expected outputs
• Blueprint Deliverables - All 12 blueprints have deliverables specifications
• Comprehensive Documentation - New chain-context guide with architecture diagrams

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Chain Context System

The chain context is a shared state object that tracks skillchain execution:

chain_context:
  blueprint: "ml-pipeline"
  maturity: "starter"
  skill_outputs:
    implementing-mlops:
      status: "completed"
      files_created: ["src/models/train.py"]
  deliverables_status:
    "MLflow tracking":
      status: "fulfilled"

Key components:

• chain-context-schema.yaml - Full schema definition
• State flows: router → orchestrator → validation
• Tracks skill outputs, deliverables, and validation results

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Validation Framework

Post-generation validation ensures skillchains deliver on blueprint promises:

┌─────────────────────────────────────────────────┐
│ SKILLCHAIN COMPLETION REPORT                     │
├─────────────────────────────────────────────────┤
│ ✓ MLflow experiment tracking                     │
│ ✓ Training pipeline                              │
│ ○ Feature store (skipped - starter)              │
│ ✗ Sample data script - MISSING                   │
├─────────────────────────────────────────────────┤
│ Completeness: 75%                                │
│                                                  │
│ Would you like me to:                            │
│ A) Generate the missing components               │
│ B) Continue without them                         │
└─────────────────────────────────────────────────┘

Validation functions:

• validate_deliverable() - Single deliverable validation
• check_files_exist() - File existence with glob support
• run_content_checks() - Regex pattern verification
• validate_chain() - Complete chain validation
• run_basic_completeness_check() - Fallback validation

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outputs.yaml Files (76 skills)

Every completed skill now declares its expected outputs:

skill: implementing-mlops
version: "1.0"

base_outputs:
  - path: "src/models/train.py"
    reason: "Main training script"
    must_contain:
      - "import mlflow"

conditional_outputs:
  maturity:
    intermediate:
      - path: "src/models/hyperparameter_tuning.py"

---

Blueprint Deliverables

All 12 blueprints have deliverables specifications:

deliverables:
  "MLflow experiment tracking":
    primary_skill: implementing-mlops
    required_files:
      - src/models/train.py
    content_checks:
      - pattern: "import mlflow"
        in: src/models/train.py
    maturity_required:
      - starter
      - intermediate
      - advanced

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Updated Components

Component	Changes
Orchestrators (12)	Added Step 5.5: Post-Skill Validation
Validation package	Added blueprints.py, extended rules.yaml
CI/CD workflow	Blueprint validation, completeness_checker
Documentation	New chain-context.md, updated skill-validation.md

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Files Changed

• 124 files changed
• 37,471 insertions, 2,450 deletions
• 76 new outputs.yaml files
• New: chain-context-schema.yaml, validation.md, blueprints.py
• New: chain-context.md documentation page

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Documentation

• Chain Context & Validation - Architecture guide
• Skill Validation - CLI and API reference

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