Distill
17 model-agnostic thinking & context-engineering skills for Claude — clarify, attack your own plan, manage the context window, verify before trusting. grill-me-style process skills.
npx -y skills add opelpleple/meta-skills --skill distillAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 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
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Compresses a long article, paper, report, or transcript into a tiny high-signal output — one-sentence core thesis, three load-bearing bullets, and one open question that remains — optimizing for maximum insight per word rather than coverage. Use this skill when the user shares a long read and says "summarize this", "tl;dr", "give me the gist", "what's the key takeaway", "what's the one thing I need to know", "distill this", or when they're triaging research, papers, reports, meeting transcripts, or long threads and need to decide fast whether something is worth their full attention.
SKILL.md
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Distill
A summary lists what was said; a distillation tells you what matters and what's still missing. This skill crushes a long source into the smallest output that still changes how the reader thinks.
When to use
- The user shares a long article, paper, report, transcript, or thread and wants the essence.
- They say "tl;dr", "summarize", "key takeaway", "the gist", or "is this worth reading".
- They're triaging a stack of sources and need fast signal to decide what to read fully.
- They want the argument, not the table of contents.
When NOT to use
- They need a faithful, comprehensive summary that preserves every section (use a structured summary instead).
- The source is already short — distilling adds nothing.
- They need exact quotes, numbers, or compliance-grade fidelity where dropping detail is dangerous.
- They want analysis/critique rather than compression (do that separately and say so).
The method (numbered, concrete — the heart)
- Read for the spine, not the surface. Find the author's actual claim — the thing the whole piece exists to argue. Ignore throat-clearing intros, hedges, and filler examples.
- Write the core thesis in ONE sentence. It must be a claim, not a topic. Bad: "This paper is about remote work." Good: "Remote work raises individual output but quietly erodes the weak ties that drive promotions." If you can't say it in one sentence, you haven't found it yet.
- Extract the 3 load-bearing points. Pick the three things that, if removed, would collapse the thesis — the evidence, mechanism, or consequences that carry the argument. Not the three most interesting facts; the three most necessary ones. Each bullet ≤ 20 words.
- Cut anything the reader could have guessed. If a point is obvious or generic, it's noise. Every surviving word must earn its place by adding insight.
- Surface the 1 open question. Name the sharpest thing the source leaves unresolved — a gap, an untested assumption, a "but what about..." This is the part that tells the reader where their own thinking should go next.
- Pressure-test signal density. Reread your output: could any sentence be deleted without loss? Could any be sharper? Maximize insight per word; the whole thing should fit on a phone screen.
What good looks like
- The thesis is a sharp, falsifiable-sounding claim — someone could disagree with it.
- The three bullets are the argument's load-bearing beams, not trivia.
- Nothing is padding; every line would hurt to remove.
- The open question is genuinely unresolved and points somewhere useful.
- A reader who never opens the source still walks away meaningfully smarter.
Anti-patterns
- Summarizing structure ("First the author discusses X, then Y") instead of extracting the argument.
- A thesis that's a topic, not a claim ("explores the future of AI").
- Five vague bullets that hedge instead of three sharp ones that commit.
- Repeating the source's own marketing language uncritically.
- Padding to seem thorough — length is the enemy here.
Example (short, vivid)
Source: a 6,000-word report on why most corporate AI pilots fail.
Core thesis: Most AI pilots fail not because the models are weak but because companies bolt them onto broken workflows nobody redesigned. Key points:
- Pilots optimize a demo, not a daily job — adoption dies on contact with real work.
- The bottleneck is process and data ownership, not model accuracy.
- Teams that redesigned the workflow first saw 3x sustained usage. Open question: Is the winning factor the redesign itself, or just having an executive owner forced to care?