# U06936 Learning Curriculum Composition For Scientific Publishing Pipelines

> Build and operate the "Learning Curriculum Composition for scientific publishing pipelines" capability for scientific publishing pipelines. Use when this exact capability is required by autonomous or human-guided missions.

- Skill: `zwright8/u06936-learning-curriculum-composition-for-scientific-publis` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add zwright8/u06936-learning-curriculum-composition-for-scientific-publis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zwright8/u06936-learning-curriculum-composition-for-scientific-publis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: zwright8 (https://skillmd.com/u/zwright8)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/zwright8/u06936-learning-curriculum-composition-for-scientific-publis

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# Learning Curriculum Composition for scientific publishing pipelines

## Why This Skill Exists
Use learning curriculum composition in scientific publishing pipelines with emphasis on evidence quality, falsifiability, and calibration.

## When To Use
Use this skill when the request explicitly needs "Learning Curriculum Composition for scientific publishing pipelines" outcomes in the scientific publishing pipelines domain.

## Step-by-Step Implementation Guide
1. Define measurable outcomes for Learning Curriculum Composition for scientific publishing pipelines, including baseline and target metrics for scientific publishing pipelines.
2. Specify structured inputs/outputs for learning curriculum composition and validate schema contract edge cases.
3. Implement the core learning curriculum composition logic with deterministic scoring and reproducible execution traces.
4. Integrate orchestration policy, routing, approval gates, retries, and rollback for autonomous execution.
5. Run unit, integration, simulation, and regression suites for Learning Curriculum Composition for scientific publishing pipelines under maximally truth-seeking conditions.
6. Roll out behind a feature flag, monitor telemetry, and refine thresholds using observed operational outcomes.

## Required Deliverables
- Capability contract: input schema, deterministic scoring, output schema, and failure modes.
- Runtime profile: curriculum-engine using learning curriculum composition to produce learning-curriculum-composition-artifact-scientific-publishing-p.
- Orchestration integration: scientific-publishing-pipelines:curriculum-engine routing, approval gates, retries, and rollback controls.
- Validation evidence: unit, integration, simulation, regression-baseline suites and rollout telemetry.

## Operational Runbook
### Preflight
- Validate mission scope, contracts, and required inputs.
- Verify feature flag posture, dependencies, and approval prerequisites.

### Execution
- Execute learning curriculum composition workflow with deterministic scoring and trace capture.
- Track posture transitions and preserve reproducible evidence artifacts.

### Recovery
- Apply rollback strategy if posture is critical or guardrails fail.
- Escalate blocked execution to oversight with incident packet and trace references.

### Handoff
- Publish outcome report, scorecard, and telemetry links.
- Queue follow-up tasks for unresolved risks, approvals, or optimization work.

## Guardrails
- [quality] Require unit and integration validations before promoting Learning Curriculum Composition for scientific publishing pipelines. -> `run-validation:unit+integration+simulation+regression-baseline`
- [reliability] Trigger rollback on critical posture or repeated failures. -> `rollback:rollback-to-last-stable-baseline`
- [cost] Respect bounded resource pressure and execution budget during scaling. -> `budget-guard:resource-pressure-cap`