substance-preserving-editor
Protocol for high-fidelity document consolidation and conceptual shifting, preventing lossy 'banal summarization'.
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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Protocol for high-fidelity document consolidation and conceptual shifting, preventing lossy 'banal summarization'.
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 this skill when the user wants to analyze a YouTube video forensically using a .info.json (metadata + comments) and/or .en.srt (transcript) produced by yt-dlp. Covers any narrative video genre: scams, true crime, interviews, controversies, documentaries. Triggers include: any mention of .info.json or .en.srt files, requests to analyze YouTube comments, mine crowd reactions, run sentiment analysis, cluster topics, find narrative pivots in a transcript, or extract the video URL from a yt-dlp JSON. The skill covers two tiers: (1) quick forensics via jq/Python, and (2) full statistical analysis via pandas/sklearn/matplotlib. Do NOT use for general YouTube searches, video downloading, or tasks where no .info.json/.en.srt files are present.
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| name | substance-preserving-editor |
| description | Protocol for high-fidelity document consolidation and conceptual shifting, preventing lossy 'banal summarization'. |
This skill provides a methodology for consolidating large amounts of deep technical, poetic, or analytical text into fewer files without losing the "substance" or "artefacts" (metaphors, nuances, complex dialogues). It is designed to counteract the AI tendency to perform lossy compression (banal bullet points).
Treat the source material as a collection of immutable logic blocks and artefacts. Your goal is to relocate these blocks into a new structure, removing only literal noise while preserving the full cognitive weight of the original thoughts.
Before making any changes, establish a quantitative baseline.
wc (Word/Byte Count): Calculate the total size of all source files.Do not read to "understand the gist." Read to identify discrete units of value.
Pruning is limited to "connective tissue" and literal duplication.
Verify the result against the initial floor.
wc check: Compare the final size to the Substance Floor.Based on the size of the source material, use the appropriate execution mode:
Single-Pass Synthesis (20KB–100KB):
read_file call, identify duplicates in one "muse" turn, and emit the final result in one write_file turn.Multi-Pass / Tower of Hanoi (>100KB):
Quantitative Baseline -> Block Mapping -> Literal Transfer -> Surgical De-noising -> Final Audit.
Use this skill when moving between conceptual frameworks or cleaning up "messy" analytical directories to ensure that the AI's "System 1" doesn't amputate the "System 2" substance.