# Openclaw 6936 Learning Curriculum Composition

> Learning Curriculum Composition for scientific publishing pipelines. Use when work requires learning curriculum composition for scientific publishing pipelines with guardrails, traceable execution, and measurable outcomes.

- Skill: `zwright8/openclaw-6936-learning-curriculum-composition` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds add zwright8/openclaw-6936-learning-curriculum-composition`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zwright8/openclaw-6936-learning-curriculum-composition/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/openclaw-6936-learning-curriculum-composition

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# Learning Curriculum Composition for scientific publishing pipelines

## Mission
Use learning curriculum composition in scientific publishing pipelines with emphasis on evidence quality, falsifiability, and calibration.

## Activation Cues
- Task requires learning curriculum composition in scientific publishing pipelines.
- Task needs explicit risk controls, approval gates, and traceable outcomes.
- Task output must include artifact handoff for humans and agents.

## Execution Plan
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.

## 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. Automation: `run-validation:unit+integration+simulation+regression-baseline`
- [reliability] Trigger rollback on critical posture or repeated failures. Automation: `rollback:rollback-to-last-stable-baseline`
- [cost] Respect bounded resource pressure and execution budget during scaling. Automation: `budget-guard:resource-pressure-cap`

## Success Metrics
- Primary metric: accuracy lift
- Secondary metrics: contradiction reduction, evidence coverage in scientific publishing pipelines
- Review cadence: weekly

## Output Contract
- Return a concise execution summary with key decisions.
- Return risk and mitigation notes with unresolved blockers.
- Return artifact target: `learning-curriculum-composition-artifact-scientific-publishing-p`.
- Return recommended follow-up tasks for next wave execution.