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seldon-course-pipeline
End-to-end university course production — research findings to published multilingual course material with review gates
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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End-to-end university course production — research findings to published multilingual course material with review gates
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use when a pull request needs review by a different AI model than the one that authored it, or when configuring cross-model code review pipelines in CI/CD.
Run Demerzel's autonomous driver — full cycle or individual phases across all repos
Capture meaningful session state (current cursor, in-flight work, live hypotheses, open questions, do-NOT-carry-forward, success criteria) to state/digests/latest.md so the next session — including one after auto-compaction — can re-enter without re-discovering context cold. Distinct from /learnings (which captures surprises). Validates against docs/contracts/digest-schema.json.
Autonomous research scheduler — selects the most under-researched department by coverage ratio, generates questions from completeness gaps, cross-model validates, auto-produces courses at T>=0.8, rate-limited to 6 cycles/day with kill switch
Personalized AI tutor — turns any topic into a customized, progress-tracked learning course. Use when the user wants to LEARN a concept, skill, language, tool, or domain. Creates a MISSION, curated RESOURCES, iterative lessons with self-checks, and learning-records that persist across sessions. NOT for interrogating the user about their own design (use brainstorming/IDSD for that).
Run one bounded supervised autonomous cycle on the Demerzel governance repo. Picks the smallest unchecked governance slice inside allow_edit, implements it, runs the oracle (`pwsh scripts/verify.ps1`), emits cycle evidence, and stops. Refuses to run unless dev-process-overseer reports loop-eligible and the preflight passes. Use when asked to "run one supervised loop", "advance governance autonomously", or "do one cycle and stop".
| name | seldon-course-pipeline |
| description | End-to-end university course production — research findings to published multilingual course material with review gates |
Chains research findings into published course material through a governed pipeline with quality gates.
/seldon course-pipeline [department] — run full pipeline for department (triggers research-cycle first)
/seldon course-pipeline --from-cycle [cycle_id] — produce course from an existing research cycle
/seldon course-pipeline batch [department] — produce courses for all confirmed findings lacking courses
RESEARCH ──→ OUTLINE ──→ CONTENT ──→ TRANSLATE ──→ REVIEW ──→ PUBLISH
│ │ │ │ │ │
└─ /seldon └─ structure └─ write └─ ES/PT/FR └─ quality └─ commit
research- sections full cultural gate + log
cycle + duration module adaptation (>0.7)
If --from-cycle is provided, load the existing cycle from state/streeling/research-cycles/{cycle_id}.json.
Otherwise, invoke /seldon research-cycle [department] to run a fresh research cycle.
Gate: Research must produce confirm or discover_question conclusion with confidence >= 0.5. If not, pipeline stops with status rejected and logs the reason.
Generate a course outline from research findings:
state/streeling/departments/{dept}.department.json)state/streeling/courses/{dept}/en/)Output: Outline object with section titles, estimated durations, exercise sketches.
Write the full course module in English following the standard format:
---
module_id: {dept-code}-{sequence}-{slug}
department: {department}
course: "{curriculum area}"
level: {level}
prerequisites: [{list}]
estimated_duration: "{N} minutes"
produced_by: seldon-course-pipeline
research_cycle: {cycle_id}
pipeline_id: {pipeline_id}
version: "1.0.0"
---
# {Title}
> **{Department Full Name}** | Level: {level} | Duration: {duration}
## Objectives
[3-5 learning objectives]
---
## {Sections with content}
### Practice Exercise
[Hands-on exercises]
---
## Key Takeaways
## Further Reading
---
*Produced by Seldon Course Pipeline {pipeline_id}.*
*Research: {cycle_id} | Belief: {value} ({confidence})*
Content quality requirements:
Save to: state/streeling/courses/{department}/en/{module_id}.md
Check policies/multilingual-policy.yaml for translation requirements.
For each required language (currently: es, pt, fr):
Save to: state/streeling/courses/{department}/{lang}/{module_id}.{lang}.md
Gate: Translation must preserve all sections, exercises, and key terms. Quick verification: section count must match English version.
Quality gate before publishing:
Verdicts:
approved — proceed to publishrevise — return to CONTENT phase with specific feedback (max 2 revisions)reject — pipeline ends, log reason, flag for human reviewIf confidence < 0.7 but >= 0.5: flag for human review instead of auto-approving. If confidence < 0.5: auto-reject (should have been caught at Phase 1 gate).
state/streeling/course-productions/{pipeline_id}.json conforming to schemas/course-production.schema.jsonfeat: {DEPT-CODE}-{SEQ} — {course title}
Course Published: {module_id}
Department: {department}
Title: {title}
Level: {level} | Duration: {duration}
Languages: en, es, pt, fr
Research: {cycle_id} (belief: {value}, confidence: {confidence})
Files: {count} files across {language_count} languages
When invoked with batch, scan state/streeling/research-cycles/ for cycles with:
conclusion = confirm or discover_questionbelief_confidence >= 0.5state/streeling/courses/{dept}/en/Run the pipeline (phases 2-6) for each qualifying cycle. Print batch summary.
Pipeline runs are logged to state/streeling/course-productions/{pipeline_id}.json.
This allows: