agentleFS
nortonxnortonx/ai-tooling-free/skills/deslop-prose/SKILL.md

deslop-prose

Skill1 starsChanged 2 months agoMITView on GitHub

Review a prose draft (report, doc, spec, ADR, PR description) before it is saved or sent: strip AI slop, catch pt-BR calques, gloss jargon, turn parallel structure into tables, enforce the length budget. For source code use `deslop`. Args: <file>

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    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
  1. Distinctive:

    Run it on the draft, before writing to disk and before showing the user.

    line 11
  2. Distinctive:

    Rule of thumb: if the team says the English word out loud in a code review, keep the English word and italicize it.

    line 44
  3. Distinctive:

    Every library, product and named concept gets a one-line gloss at its first occurrence, or a row in the glossary table.

    line 52
  4. Distinctive:

    No section the calling skill did not authorize.

    line 64
  5. Distinctive:

    Every sentence carries a fact, a decision or a constraint.

    line 66

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Deslop (prose)

Review gate for text, not code. deslop handles code; this handles anything a human will read as prose.

Run it on the draft, before writing to disk and before showing the user.

Step 0: load the destination's own rules

If the target path is inside a tree that has a CLAUDE.md at its root (for example docs/CLAUDE.md), read that file first. It outranks everything below. Those files carry the project's own substitution tables, terminology bans, and glossary thresholds, and they are usually the only place a house rule is written down.

Skipping this step is the single most common way this gate fails. Do not skip it because the rules "look generic".

Step 1: mechanical checks

Grep the draft. Any hit is a defect.

grep -nP '\x{2014}|\s-\s' <file>      # em dashes and spaced dashes
grep -niE 'robust|seamless|abrangente|poderoso|simplesmente|essencialmente|basicamente|obviamente|vale ressaltar|é importante notar|In this change'

Then run whatever verification greps the destination CLAUDE.md defines.

Step 2: calques and jargon (pt-BR drafts only)

A literal translation that is grammatically valid can still be wrong. Check the destination's substitution table first, then these.

EnglishUseAvoid
harden / hardening (noun)hardening, in italics"endurecimento"
harden (verb)"fazer o hardening de""endurecer"
finding (audit / security audit)"achado", the established audit termrewriting a documented audit glossary
finding (engineering investigation)"o que foi encontrado""achado" as a generic heading
bypassbypass, in italics"contorno", "desvio"
pool / poolingpool, in italics"piscina", "agrupamento"
placeholderplaceholder, in italics"marcador de posição"

Rule of thumb: if the team says the English word out loud in a code review, keep the English word and italicize it. If nobody says it, translate the concept.

Check the corpus before inventing a term. Grep the destination tree for both candidates and follow what is already there, unless what is there is provably a repeated mistake.

Step 3: gloss the jargon that is not an acronym

Glossary rules usually trigger on acronym count and therefore miss the terms that actually confuse people: library names, product names, and internal concept names.

Every library, product and named concept gets a one-line gloss at its first occurrence, or a row in the glossary table. Ask of each one: would a competent dev outside this repo know what this is?

Worked examples of failures this catches:

  • "undici": a reader cannot tell it is the HTTP library built into Node that implements fetch.
  • "agente interno": reads like an HTTP client. It is an https.Agent, a connection pool plus TLS options.

Glossing the term is not enough when the term is the crux of the document. If the whole point turns on why two things do not fit together, spend a short paragraph on the mechanism.

Step 4: shape

  • Parallel structure with 3 or more items becomes a table. Repeated ### file headings with one paragraph each are parallel structure.
  • No section the calling skill did not authorize. If a section feels necessary and is not on the list, ask instead of adding it.
  • Respect the caller's line budget. Count the body, excluding frontmatter, and report the number.
  • Every sentence carries a fact, a decision or a constraint. Delete the rest.

Step 5: report

State what changed and the before/after line and word count. If the draft is over budget, say by how much and why, rather than cutting content the user explicitly asked for.

Red Flags: STOP and fix

  • Reviewing a doc bound for a tree with its own CLAUDE.md without having read it.
  • Translating a technical term the team says in English.
  • A library or product name appearing with no gloss.
  • Three or more sibling sections with identical shape, left as headings.
  • A section that the calling skill never listed.
  • Claiming the review passed without running the greps.

Rationalization Table

ExcuseReality
"The tree's CLAUDE.md is probably generic"It is where the house rules live. Read it.
"The corpus already uses this word 20 times"Check whether those 20 came from the same generator. Frequency is not authority.
"Everyone knows what undici is"The reader is a colleague in six months, not you today. Gloss it.
"The table would lose nuance"Parallel content in headings loses more, and costs three times the lines.
"Two lines over budget is fine"Say so out loud with the number. Silent overrun is how 45 becomes 80.
"It reads fine to me"Read it aloud. If it would be awkward in a code review with senior peers, rewrite.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.