distill
Skill0 starsChanged 5 months agoMITView on GitHub
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.
75 other projects use a version of this.
Adopted by
Most recently taken up by
- venhdev/skillstheir own version, first seen Nov 8, 2026
- kylehughes/distillation-for-agentstheir own version, first seen Nov 8, 2026
- komluk/scaffoldingtheir own version, first seen Nov 7, 2026
- Integralist/agent-skillstheir own version, first seen Nov 6, 2026
- theexperiencecompany/gaiatheir own version, first seen Nov 3, 2026
- JHostalek/dotclaudetheir own version, first seen Nov 1, 2026
Used with
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Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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Other versions75 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 75
- talesofai/cohub★ 585same name
- juxt/allium★ 506same name
- educlopez/ui-craft★ 441same name
- Michaelliv/napkin★ 428same name
- nexmoe/DayBee★ 424same name
- nexmoe/eve★ 424same name
- Southclaws/storyden★ 348same name
- 10xChengTu/Mux0★ 337same name
- theexperiencecompany/gaia★ 325same name
- AkaraChen/aghub★ 271same name
65 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository16 other files it gives its agents.
- assumption-auditSkill
- cite-checkSkill
- compactSkill
- confidence-tagSkill
- context-budgetSkill
- devils-advocateSkill
- first-principlesSkill
- handoffSkill
- pre-mortemSkill
- red-teamSkill
- rubber-duckSkill
- scope-cutterSkill
- scratchpadSkill
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- steelmanSkill
- teach-backSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.
0
Common
Most of the 71 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 26
Cut anything the reader could have guessed: If a point is obvious or generic, it's noise.
“Common” is measured: kept by at least 6 in 10 of the 71 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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?
Does it work?
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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