Chalk Labs
Need help deploying unslop?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/vantanev-unslop
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: documents, writing
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/unslop
What's inside
- Skill name
unslop- Description
- Cut AI tells from any writing. Use when you write or edit prose that a person reads, such as a doc, PR description, commit message, ticket, review comment, or report, or when the user asks to remove slop or mannered prose.
- License
- MIT
- Instructions
- 58 lines in SKILL.md · view on GitHub
Files (4)
- LICENSE
- SKILL.md
- UPSTREAM.md
- openai.yaml · agents/
SKILL.md (first lines)
# Unslop
Edit text to remove AI patterns. Scan for the patterns below, then rewrite. Preserve meaning and match the intended tone.
## Patterns to detect and fix
Rule numbers are stable ids that other skills cite. A removed rule leaves a gap.
### Content
3. **Superficial -ing phrases.** "highlighting...", "ensuring...", "reflecting...", "showcasing...", "fostering...". Delete or expand with real sources.
5. **Vague attributions.** "Experts believe", "Industry reports suggest", "Some critics argue". Name the source or delete.
### Language
7. **AI vocabulary.** Additionally, crucial, delve, enduring, enhance, fostering, garner, interplay, intricate, landscape (abstract), pivotal, showcase, tapestry (abstract), testament, underscore, vibrant. Replace with plain words.
8. **Fancy ways to say "is".** "serves as", "stands as", "boasts", "features". Say "is" or "has".
9. **"Not just X, but Y."** State the point directly instead.
10. **Rule of three.** Don't force ideas into groups of three. Use the natural number.
11. **Synonym cycling.** Protagonist, main character, central figure, hero all in one paragraph. Pick one, repeat it.
12. **False ranges.** "from X to Y" where X and Y aren't on a meaningful scale. List topics directly.
### Style
13. **Em dash overuse.** Avoid em dashes entirely. Use periods or commas only (no parentheses, no en dashes, no hyphen-as-dash substitutes). If a thought needs separation, end the sentence or use a comma.
14. **Colon overuse.** Colons are fine before a list or example, not as mid-sentence connectors. "If you're coming from traditional automation: instead of registering event handlers, you describe conditions" adds nothing with the colon. Rewrite to let the point stand on its own without comparison framing. "Describing when the scheduler should fire works best as plain English." Same meaning, no crutch punctuation.
15. **Boldface overuse.** Don't bold every proper noun or acronym.
16. **Inline-header lists.** The tell is a bold label and colon that restates the line: "**Performance:** Performance improved...". Convert those to prose. A bold lead-in that ends in a period, names the item, and is followed by genuinely new detail ("**Schema in TypeScript.** Tables live in one file.") is fine, not a tell.
17. **Title case headings.** Use sentence case.
18. **Decorative emojis.** Remove from headings and bullets.
19. **Curly quotes.** Use straight quotes.
### Communication artifacts
20. **Chatbot phrases.** "I hope this helps!", "Let me know if...", "Of course!", "Certainly!", "Found the smoking gun!" Remove.
22. **Sycophantic tone.** "Great question! You're absolutely right!" Respond directly.
### Filler
23. **Filler phrases.** "In order to" becomes "To". "Due to the fact that" becomes "Because". Delete "It is important to note that".
24. **Excessive hedging.** "could potentially possibly be argued that it might" becomes "may".
25. **Generic conclusions.** "The future looks bright." State specific plans or facts.
### Jargon
26. **Abstract metaphor nouns.** Substrate (base), wedge, wedge in (add), vector (way, method), locus, vantage, nexus, primitive as a noun, harness as a metaphor, surface as in "API surface", bedrock, scaffolding as a metaphor, modality, paradigm, gold-plating (more than the job needs), ratchet as a metaphor (the mechanism's real name, or a limit that only tightens), evacuate for moving code (move out), endgame (the last phase), north star, flywheel. These read as technical but usually have a plainer concrete word. Pick the concrete word.
### Plain speech
27. **Say what it does, not how it feels.** "the database stays close at hand", "SQL you can read", "types that follow your schema" name a feeling. The fix names the mechanism or a number: "`.toSQL()` returns the exact string sent to the database", "a column rename fails the build". Ask what the sentence tells the reader to do or know, then write that. If you can't restate it as a concrete instruction, fact, or number, cut it. One more check: if the sentence could appear unchanged in another project's docs, it says nothing about this one. Cut it.
28. **Shorten or split dense sentences.** If the reader has to backtrack to parse a sentence, break it in two or drop clauses. One idea per sentence.
29. **Active voice.** Prefer it. Catch "is/are/was/were + past participle" and name the actor: "queries are validated" becomes "the compiler validates queries", "the file is parsed by the loader" becomes "the loader parses the file". Passive is fine only when the actor is unknown or genuinely doesn't matter.
30. **Cut adverbs, or use a stronger verb.** "runs quickly" becomes "is fast" or the number, and "significantly improves" becomes the measured delta. An adverb propping up a weak verb means the verb is wrong.
31. **Prefer the plain word.** "utilize" and "leverage" become "use", "facilitate" becomes "help", "numerous" becomes "many", "in the event that" becomes "if". The fancier synonym is rarely clearer.
32. **Mannered prose.** Metaphor or flourish where a literal phrase exists: aphorisms ("wire it or delete it"), rhetorical fragments for effect, personified code ("the plan holds it"), figurative verbs ("rides along", "stands on"), stock framing phrases. "A dial worth turning" becomes "a parameter worth varying". Say what you mean. Rule 26 covers the metaphor nouns.
33. **Over-compression.** Dropped articles, verbless fragments, symbol-speak, and abbreviations that make the reader decode instead of read. "Parser rejects bad date → exit 2, no write" becomes "The parser rejects a bad date, exits with code 2, and writes nothing." Write whole sentences with their articles and verbs, and spell out arrows and abbreviations.From VanTanev/skills, MIT.
More from VanTanev/skills
- 0.570score
hillclimb
VanTanev
Improve one measured number in a loop against a target: one change, one measurement, keep or revert.
SkillAFreshness: AClaude Code +9 - 0.567score
why
VanTanev
Find why code has its shape.
SkillAFreshness: AClaude Code +9 - 0.567score
correct
VanTanev
Find the mistakes that agents repeat in a repo, and make each one impossible with architecture, types, a lint, or a test.
SkillAFreshness: AClaude Code +9 - 0.565score
risk-first
VanTanev
Risk-first implementation: hunt the risks before code, settle each with an oracle, pin it with a discriminating check.
SkillAFreshness: AClaude Code +9 - 0.565score
benchmark-checklist
VanTanev
Vet a measured number (limiter, tuning, limits, errors, repeatability, relevance, and whether the work happened) before you report or act on it.
SkillAFreshness: AClaude Code +9 - 0.564score
no-comments
VanTanev
Spawn Comment Sicko on the scoped diff, act on accepted findings, and offer encodings for claimed constraints.
SkillAFreshness: AClaude Code +9 - 0.564score
interrogate
VanTanev
Reviewers from different model families challenge changes from independent angles.
SkillAFreshness: AClaude Code +9 - 0.560score
bash
VanTanev
Write, review, or debug Bash script files in a repository.
SkillAFreshness: AClaude Code +9 - 0.558score
build-spec
VanTanev
Implement the result of /to-spec and /to-tickets in code, building each ticket risk-first.
SkillAFreshness: AClaude Code +9 - 0.556score
expert-test
VanTanev
Run the decision at hand through the best expert in its field, kill the choice that expert would reject, and state every trade-off.
SkillAFreshness: AClaude Code +9 - 0.550score
build
VanTanev
Implement a spec or tickets risk-first, then /code-review
SkillAFreshness: AClaude Code +9 - 0.541score
create-verification-skill
VanTanev
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform.
SkillAFreshness: AClaude Code +9
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 14, 2026 (16 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/VanTanev/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from VanTanev
- 0.570score
hillclimb
VanTanev
Improve one measured number in a loop against a target: one change, one measurement, keep or revert.
SkillAFreshness: AClaude Code +9 - 0.567score
correct
VanTanev
Find the mistakes that agents repeat in a repo, and make each one impossible with architecture, types, a lint, or a test.
SkillAFreshness: AClaude Code +9 - 0.567score
why
VanTanev
Find why code has its shape.
SkillAFreshness: AClaude Code +9 - 0.565score
benchmark-checklist
VanTanev
Vet a measured number (limiter, tuning, limits, errors, repeatability, relevance, and whether the work happened) before you report or act on it.
SkillAFreshness: AClaude Code +9 - 0.565score
risk-first
VanTanev
Risk-first implementation: hunt the risks before code, settle each with an oracle, pin it with a discriminating check.
SkillAFreshness: AClaude Code +9 - 0.564score
interrogate
VanTanev
Reviewers from different model families challenge changes from independent angles.
SkillAFreshness: AClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get unslop running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
unslop skill. Agent Mart. https://agentmart.dev/skills/vantanev-unslop (last updated 2026-10-24)