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EXAMPLES.md

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EXAMPLES

Minimal Context Pack Layout (example)

context_pack/
  README.md
  manifest.json
  MAP/
    index.json
  SPEC/
    report.schema.md
  EVIDENCE/
    top_findings/
  ARTIFACTS/
    report.json
    event_graph.json
    api_surface.json
    quality_gate.json
    backend_verifier_report.json
    patterns.json

Typical AI Usage Scenario

Goal: prepare a safe backend maintenance plan for the next sprint.

Suggested flow:

  1. read README.md + MAP/index.json
  2. inspect quality_gate.json
  3. inspect backend_verifier_report.*
  4. inspect patterns.json and ai_risks/risks.json
  5. open top evidence refs for uncertain items

Output expectation:

  • scoped maintenance checklist
  • high/medium transaction-side-effect risks
  • explicit unknowns requiring drilldown

Facts-only then Drilldown Example

Facts-only phase:

  • identify queue.dispatch without observed consume
  • identify write-like API without explicit guard

Drilldown phase (only if needed):

  • open evidence-linked files/ranges
  • confirm whether consumer/guard signals appear near evidence regions
  • keep confirmed/inferred/unknown separated

Upgrade Before/After Example

Given baseline inputs:

  • baseline_in.json
  • baseline_diff.json
  • quality_gate.json

Use Context Pack to:

  1. compare new transaction/side-effect signals vs baseline
  2. identify newly introduced medium/high risk patterns
  3. prepare upgrade notes for next AI-assisted modification round

This is evidence-backed upgrade context support, not a guarantee of safe upgrades.