/matrix

Domain Patterns

Purpose: Use this file when the domain is known and you need default axes, common constraints, scoring hints, and downstream routing.

Contents

• Test
• Load
• Deploy
• UX
• Risk
• Experiment
• Compatibility
• AI/ML
• Custom

Test

Typical axes:

Common constraints:

constraints:
  exclude:
    - {browser: Safari, os: Windows}
    - {browser: Chrome, os: iOS}
  conditional:
    - if: {network: offline}
      then: {auth_state: logged_in}

Suggested next agent: Voyager, Radar, or Siege.

Load

Typical axes:

Common constraints:

constraints:
  conditional:
    - if: {concurrent_users: 5000}
      then: {duration: "1min"}
  exclude:
    - {data_volume: large, concurrent_users: 5000}

Suggested next agent: Siege, Beacon, or Bolt.

Deploy

Typical axes:

Default ordering:

1. dev -> staging -> production
2. nearest region first
3. 1% -> 10% -> 50% -> 100%

Suggested next agent: Scaffold, Gear, or Beacon.

UX

Typical axes:

Suggested next agent: Cast, Echo, or Field.

Risk

Typical axes:

Default risk tiers:

Suggested next agent: Triage, Sentinel, Probe, or Scout.

Experiment

Typical axes:

Suggested next agent: Experiment or Pulse.

Compatibility

Typical axes:

Suggested next agent: Shift (detect/radar) or Builder.

AI/ML

Typical axes:

Common constraints:

constraints:
  conditional:
    - if: {input_dimension: multimodal}
      then: {model_type: generative}
  exclude:
    - {deployment_target: edge, model_type: generative}

Notes:

• Use NIST CT for AI-Enabled Systems guidance for input space modeling.
• Fairness axes require at least 3-way strength to cover intersectional bias (e.g., gender × age × ethnicity).
• Dataset coverage uses combinatorial coverage difference (NIST CSWP 19) to compare train vs. test distribution.
• For training data quality, use data frequency coverage instead of simple tuple presence — measure how often each feature interaction appears, not just whether it appears. Skewed frequency distributions degrade model performance even when all tuples are nominally covered (NIST 2025). Use CoDEX (Coverage of Data Explorer) for frequency analysis.
• Key finding: performance may increase or decrease with data skew, feature importance methods do not reliably predict skew impact, and adding more data may not mitigate skew effects — frequency rebalancing of specific interactions is needed.

Suggested next agent: Oracle, Radar, or Experiment.

Custom

Use custom when none of the built-in domains fit. In that case:

• preserve the generic matrix model
• do not invent a downstream specialist
• ask only if domain ambiguity changes the outcome materially

Per-Recipe Behavior Notes (SKILL.md excerpt)

• combine: End-to-end combination explosion control workflow. Parse axes/values/constraints and generate the minimum coverage set.
• cover: Focus on selecting the optimization algorithm (pairwise / OA / high-strength 3-way+).
• plan: Generate an execution plan (priority, assigned agents) from the coverage set. Emphasize PLAN phase.
• prioritize: Focus on Critical/High/Medium/Low prioritization and bias detection.
• pairwise: Apply IPOG / IPOG-F algorithm (NIST ACTS) or Orthogonal Array Testing (OATS) to produce the smallest 2-way 100%-covering test set. Output: test-case table + uncovered 3-way tuple list + reduction ratio. Hand off to Radar (unit/integration), Voyager (E2E), or Siege (load). Use cover instead when the user wants a general n-wise selection without the IPOG-specific method rationale.
• equiv-class: Partition input domain into equivalence classes (valid/invalid), derive representative test cases, and add boundary value analysis (BVA) with ON/OFF/IN/OUT points for each class boundary. Emit one-defect-per-case negative test rule (never mask defects by combining invalid values). Use when axes are primarily input ranges rather than enumerated values. Hand off to Radar (unit), Builder (input validator), Probe (negative security cases).
• qa-scenario: Manual QA scenario authoring for human testers and regulated-domain audits. Compose techniques: BVA (boundaries), equivalence class (input domain), decision table (rule combinations), state transition (workflow), exploratory charter (time-boxed discovery). Output: numbered procedures (Preconditions → Steps → Expected Results → Postconditions) + traceability matrix (test ID ↔ AC/PRD ID) + regression suite seed. Hand off to Voyager (E2E automation) and Radar (unit coverage) for the automated layers.
• risk-cover: Compute Risk Priority Number (RPN = Severity × Occurrence × Detection) per combination, weight coverage priority by RPN, and align with FMEA findings. Classify into Action Priority (AP) H/M/L per AIAG-VDA. Emit risk-sorted coverage set plus a residual-risk report for uncovered combinations. Consumes omen FMEA output when available. Hand off to omen (depth analysis), Sentinel (security RPN), Siege (load-risk combinations).