How do I install the Distill skill?

Run npx skillmds@latest add concertonotes/distill in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Distill safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Distill?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Distill free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Distill?

ConcertoNotes (@concertonotes) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Distill by concertonotes — Agent Skill

Name: Distill
Rating: 4.2 (5 reviews)
Author: ConcertoNotes

Distill

Use when user wants to reduce noise, verbosity, or redundancy in markdown or text files. Use when user says distill, compress, clean up, tighten, denoise, reduce entropy, improve signal-to-noise ratio, or make text more concise. Accepts a file path, a directory (distills all .md/.txt files in it), or no argument (distills all .md/.txt files in the current directory).

ConcertoNotes Updated Sep 8, 2026 0 repo stars

File contents

Distill — Semantic Text Compression

ultrathink

Maximize facts-per-word while preserving every fact and keeping the text readable. Anything predictable from context carries zero information and can be removed.

This compresses stated meaning; it does NOT reverse euphemism or detect lies — an information-preserving compressor, not a fact-checker.

Target Resolution

From $ARGUMENTS: empty -> Glob top-level *.md + *.txt in cwd (non-recursive); directory -> Glob <dir>/**/*.md + <dir>/**/*.txt (recursive); file -> that single file.

Exclude non-prose files (requirements*.txt, LICENSE*). No matches -> say No .md or .txt files found in <path>. and STOP. Run Steps 1-4 per file, report in Step 5.

Step 1 — Measure

FILE="<path>"; echo "BEFORE: $(wc -c < "$FILE") chars | $(wc -w < "$FILE") words | $(wc -l < "$FILE") lines | $(gzip -c "$FILE" | wc -c) gzip"

gzip bytes are the entropy proxy: word count alone doesn't distinguish filler from signal, so track gzip before/after as the real-compression measure.

Step 1.5 — Deterministic Pre-Pass

Extract the fenced python block from references/deterministic-patterns.md (the canonical kill-lists) and run it against the file:

REF="references/deterministic-patterns.md"
awk '/^```python$/{f=1;next} /^```$/{f=0} f' "$REF" | python3 - "<path>"

Mechanical kill-lists first; Step 2 is then pure semantic work.

Step 2 — Distill

Rewrite the file. Read references/noise-patterns.md for the semantic noise taxonomy (buzzword replacement, structural padding, non-committal language, repetition, empty conclusions). Lead with the point, state each fact once, keep markdown structure that aids navigation and drop structure that only decorates.

Step 3 — Post-Verification

Before writing:

Tables: count rows matching |...|...| in original vs output; restore any dropped.
Code blocks: count fenced blocks (``` or ~~~); counts must match.
URLs: every http/https/ftp/mailto in original must appear in output.
If the original is under 100 words and already scored slop_score >= 90, apply only minimal changes — already dense.

Step 4 — Write and Measure

FILE="<path>"; echo "AFTER: $(wc -c < "$FILE") chars | $(wc -w < "$FILE") words | $(wc -l < "$FILE") lines | $(gzip -c "$FILE" | wc -c) gzip"

Step 5 — Report

Per file report words and gzip bytes before/after with reduction %, noise categories removed, and preserved counts (facts, code blocks, URLs). For a batch, one row per file plus a Total row (sum words before/after; reduction = (1 - total_after/total_before)*100).

Reduction under 10% -> mark already dense. Reduction over 60% -> re-verify no facts were lost before reporting.

If the user is unsatisfied, ask which passages lost information, restore them, and re-distill with a lighter touch.

---name: distilldescription: Use when user wants to reduce noise, verbosity, or redundancy in markdown or text files. Use when user says distill, compress, clean up, tighten, denoise, reduce entropy, improve signal-to-noise ratio, or make text more concise. Accepts a file path, a directory (distills all .md/.txt files in it), or no argument (distills all .md/.txt files in the current directory).---# Distill — Semantic Text CompressionultrathinkMaximize facts-per-word while preserving every fact and keeping the text readable. Anything predictable from context carries zero information and can be removed.<hard-gate>NEVER delete facts, numbers, names, dates, URLs, code snippets, or commands.NEVER change the meaning of a statement.NEVER add information not in the original.NEVER make the text telegraphic — output flows as natural prose.</hard-gate>This compresses stated meaning; it does NOT reverse euphemism or detect lies — an information-preserving compressor, not a fact-checker.## Target ResolutionFrom `$ARGUMENTS`: empty -> Glob top-level `*.md` + `*.txt` in cwd (non-recursive); directory -> Glob `<dir>/**/*.md` + `<dir>/**/*.txt` (recursive); file -> that single file.Exclude non-prose files (`requirements*.txt`, `LICENSE*`). No matches -> say `No .md or .txt files found in <path>.` and STOP. Run Steps 1-4 per file, report in Step 5.## Step 1 — Measure```bashFILE="<path>"; echo "BEFORE: $(wc -c < "$FILE") chars | $(wc -w < "$FILE") words | $(wc -l < "$FILE") lines | $(gzip -c "$FILE" | wc -c) gzip"```gzip bytes are the entropy proxy: word count alone doesn't distinguish filler from signal, so track gzip before/after as the real-compression measure.## Step 1.5 — Deterministic Pre-PassExtract the fenced python block from `references/deterministic-patterns.md` (the canonical kill-lists) and run it against the file:```bashREF="references/deterministic-patterns.md"awk '/^```python$/{f=1;next} /^```$/{f=0} f' "$REF" | python3 - "<path>"```Mechanical kill-lists first; Step 2 is then pure semantic work.## Step 2 — DistillRewrite the file. Read `references/noise-patterns.md` for the semantic noise taxonomy (buzzword replacement, structural padding, non-committal language, repetition, empty conclusions). Lead with the point, state each fact once, keep markdown structure that aids navigation and drop structure that only decorates.## Step 3 — Post-VerificationBefore writing:1. Tables: count rows matching `|...|...|` in original vs output; restore any dropped.2. Code blocks: count fenced blocks (``` or `~~~`); counts must match.3. URLs: every http/https/ftp/mailto in original must appear in output.4. If the original is under 100 words and already scored slop_score >= 90, apply only minimal changes — already dense.## Step 4 — Write and Measure```bashFILE="<path>"; echo "AFTER: $(wc -c < "$FILE") chars | $(wc -w < "$FILE") words | $(wc -l < "$FILE") lines | $(gzip -c "$FILE" | wc -c) gzip"```## Step 5 — ReportPer file report words and gzip bytes before/after with reduction %, noise categories removed, and preserved counts (facts, code blocks, URLs). For a batch, one row per file plus a Total row (sum words before/after; `reduction = (1 - total_after/total_before)*100`).Reduction under 10% -> mark already dense. Reduction over 60% -> re-verify no facts were lost before reporting.If the user is unsatisfied, ask which passages lost information, restore them, and re-distill with a lighter touch.

Frequently asked questions