machine-learning

name: machine-learning description: General machine learning delivery patterns for datasets, training, and evaluation. allowed-tools: Read, Write, Edit, Bash, Glob, Grep, Task, TodoWrite model: sonnet x-version: 3.2.0 x-category: platforms x-vcl-compliance: v3.1.1 x-cognitive-frames:

• HON
• MOR
• COM
• CLS
• EVD
• ASP
• SPC

Purpose

Plan and execute ML workflows with data contracts, experiments, and deployment gates.

Trigger Conditions

• Use this skill when: Need end-to-end ML lifecycle guidance from data to deployment.
• Reroute when: If targeting a specific platform (Gemini/Codex/Flow Nexus), use that platform skill instead.

Guardrails (Inherited from Skill-Forge + Prompt-Architect)

• Structure-first: every platform skill keeps SKILL.md, examples/, and tests/ populated; create resources/ and references/ as needed. Log any missing artifact and fill a placeholder before proceeding.
• Confidence ceilings are mandatory in outputs: inference/report 0.70, research 0.85, observation/definition 0.95. State as Confidence: X.XX (ceiling: TYPE Y.YY).
• English-only user-facing text; keep VCL markers internal. Do not leak internal notation.
• Adversarial validation is required before sign-off: boundary, failure, and COV checks with notes.
• MCP tagging for runs: WHO=machine-learning-{session}, WHY=skill-execution, namespace skills/platforms/machine-learning/{project}.

Execution Framework

1. Intent & Constraints — clarify task goal, inputs, success criteria, and risk limits; extract hard/soft/inferred constraints explicitly.
2. Plan & Docs — outline steps, needed examples/tests, and data contracts; confirm platform-specific policies.
3. Build & Optimize — apply platform playbook below; keep iterative checkpoints and diffs.
4. Validate — run adversarial tests, measure KPIs, and record evidence with ceilings.
5. Deliver & Hand off — summarize decisions, artifacts, and next actions; capture learnings for reuse.

Platform Playbook

• Workflow patterns:
  • Define dataset contracts, splits, and quality checks
  • Run experiment tracking with baselines and ablations
  • Package models with eval gates, monitoring, and rollback
• Anti-patterns to avoid: Training without baselines, No lineage between data and model artifacts, Shipping without eval or monitoring
• Example executions:
  • Train classifier with data versioning and tracked metrics
  • Deploy model behind API with canary and drift monitoring

Documentation & Artifacts

• SKILL.md (this file) is canonical; keep quick-reference notes in README.md if present.
• examples/ should hold runnable or narrative examples; tests/ should include validation steps or checklists.
• resources/ stores helper scripts/templates; references/ stores background links or research.
• Update metadata.json version if behavior meaningfully changes.

Verification Checklist

• Trigger matched and reroute considered
• Examples/tests present or stubbed with TODOs
• Constraints captured and confidence ceiling stated
• Validation evidence captured (boundary, failure, COV)
• MCP tags applied for this run

Confidence: 0.70 (ceiling: inference 0.70) - Standardized platform skill rewrite aligned with skill-forge + prompt-architect guardrails.