Morningside Xml Pipeline
D 59 completed
Other
unknown / xml · tiny
24
Files
33,978
LOC
0
Frameworks
5
Languages
Pipeline State
completedRun ID
#1541062Phase
doneProgress
0%Started
2026-04-16 20:29:05Finished
2026-04-16 20:29:05LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
32.67Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:02Deduplication group #47499
Member of a group with 156 similar repo(s) — canonical #1582697 view group →
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/
🧪 Code Distillation
Browse all specs →AI Prompt
Create a Python pipeline called "Morningside XML Pipeline" that auto-generates rough cuts from raw talking-head YouTube footage. It should support two modes: Mode A (MP4 $\rightarrow$ XML) which uses ffmpeg for audio extraction and OpenAI Whisper for transcription, followed by GPT-5.4 analysis; and Mode B (Transcript $\rightarrow$ XML) which takes a pre-exported Premiere transcript. The final output must be an FCPXML file for Premiere Pro. The system should use structural rules learned from RLHF reviews to guide the LLM analysis. Please include command-line usage examples for both modes, referencing the necessary Python files like `main_v2.py` and handling dependencies like `openai` and `ffmpeg`.
python xml video-processing openai fcpxml transcription llm automation command-line
Generated by gemma4:latest
Catalog Information
Create a Python pipeline called "Morningside XML Pipeline" that auto-generates rough cuts from raw talking-head YouTube footage. It should support two modes: Mode A (MP4 $\rightarrow$ XML) which uses ffmpeg for audio extraction and OpenAI Whisper for transcription, followed by GPT-5.4 analysis; and Mode B (Transcript $\rightarrow$ XML) which takes a pre-exported Premiere transcript. The final output must be an FCPXML file for Premiere Pro. The system should use structural rules learned from RLHF
Tags
python xml video-processing openai fcpxml transcription llm automation command-line
Quality Score
D
59.1/100
Structure
47
Code Quality
75
Documentation
57
Testing
20
Practices
63
Security
100
Dependencies
90
Strengths
- Consistent naming conventions (snake_case)
- Good security practices — no major issues detected
Weaknesses
- No LICENSE file — legal ambiguity for contributors
- No CI/CD configuration — manual testing and deployment
- 113 duplicate lines detected — consider DRY refactoring
Recommendations
- Add a test suite — start with critical path integration tests
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
Languages
Frameworks
None detected
Symbols
function61
constant25
variable4
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Generated by Repobility's multi-pass static-analysis pipeline (https://repobility.com)
BinComp Dependency Hardening
All packages →1 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.