Tldr eli5
Skill nardovibecoding/simply-skills-curation/skills/discovery/tldr-eli5
A curated AI coding skill and hook pack for safe local workflows.
npx -y skills add nardovibecoding/simply-skills-curation --skill tldr-eli5Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Adaptive summarization — output length scales with input length for comfortable reading. Triggers: "summarize", "summarize this", "tldr", "too long", "sum up", "key points", "give me summary", "eli5", "explain like I'm 5", "explain simply". NOT FOR: translation (just ask), content creation (use content-humanizer). Produces: summary at optimal compression ratio based on input length.
SKILL.md
3.6 KB, as published. Nobody here has run it
Adaptive Summarization
Summary length automatically scales with input for comfortable reading.
Compression Ratios — English
| Input | Target output | Ratio | Style |
|---|---|---|---|
| < 500 words | 2-3 sentences | ~20% | Key takeaway only |
| 500-1,500 words | 80-120 words | ~10% | Core points condensed |
| 1,500-3,000 words | 150-250 words | ~8% | Structured summary |
| 3,000-8,000 words | 300-500 words | ~6% | Section-by-section |
| 8,000-20,000 words | 500-800 words | ~4% | Executive brief |
| 20,000+ words | 800-1,200 words | ~3% | Full executive summary |
Compression Ratios — Chinese (中文)
Chinese packs ~1.5x more meaning per character. Summaries should be shorter.
| Input | Target output | Ratio | Style |
|---|---|---|---|
| < 300字 | 1-2句话 | ~15% | 一句话概括 |
| 300-1,000字 | 50-80字 | ~8% | 要点浓缩 |
| 1,000-2,000字 | 100-160字 | ~6% | 分段总结 |
| 2,000-5,000字 | 200-350字 | ~5% | 按主题分段 |
| 5,000-15,000字 | 350-550字 | ~3% | 摘要+关键引用 |
| 15,000字+ | 500-800字 | ~2.5% | 完整摘要报告 |
Language Detection
- Detect input language automatically
- Summarize in the SAME language as the input (unless asked otherwise)
- Mixed language input → summarize in the dominant language
Process
- Estimate input length — count words or estimate from tokens
- Pick ratio from table above
- Summarize following the target length
- Format based on output length:
- < 100 words → plain text, no headers
- 100-300 words → bullet points
- 300+ words → headers + bullets + key quotes
Rules
- Lead with the MOST important point, not chronological order
- Include specific numbers, names, dates — not vague generalities
- If the source has a conclusion/recommendation, always include it
- For technical content: explain jargon in parentheses
- For news: who, what, why, impact
- For research: methodology → findings → implications
- Preserve the TONE of the original (formal stays formal, casual stays casual)
- End with: "Worth reading in full?" + honest 1-line verdict
Simple Explanation Mode
When triggered by "eli5", "explain like I'm 5", or "explain simply", switch to a structured explanation framework instead of summarization.
Answer these 6 questions in order:
- Simple version — one metaphor, one sentence. What is this like in real life?
- What it affects — if we add/change/remove this, what breaks or changes? Side effects on architecture.
- How others do it — what's the common pattern? How do teams usually solve this?
- Risks — what can go wrong? Worst case scenario in plain English.
- Cost — does it increase API cost, maintenance burden, or complexity?
- Suggestion — what should we actually do? One clear recommendation.
Rules for simple explanation mode:
- Max 2 sentences per answer
- No technical acronyms without explaining them first
- Use analogies from everyday life (address book, recipe, light switch, etc.)
- If the topic is about the user's specific system, tie the metaphor to something they already know
- End with the suggestion bold and actionable