humanizer
Skill0 starsChanged 2 months agoMITView on GitHub
Remove AI writing tells from text and prose. Use when the user asks to humanize, unslop, remove AI tone, sound natural, or polish writing to sound human. Not for source code, tests, commands, or technical documentation structure (use /technical-writing).
188 other projects use a version of this.
Adopted by
Most recently taken up by
- aditya-vermax/skillstheir own version, first seen Nov 8, 2026
- chrisleekr/skillstheir own version, first seen Nov 8, 2026
- xhqing/CommunityManagerAgenttheir own version, first seen Nov 8, 2026
- CloudChef/atlasclaw-providerstheir own version, first seen Nov 8, 2026
- Fractera/Agent-Engineering-Infrastructuretheir own version, first seen Nov 8, 2026
- Fractera/Agentic-Engineering-Infrastructuretheir own version, first seen Nov 8, 2026
Used with
Projects with it also use economy-mode, personal-voice and 10 more.
Other files people use alongside this one.
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.
Microsoft AzureSkilleconomy-mode
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petermuirurithuoSkillpersonal-voice
Captures the user's personality, communication style, and values. Use this skill whenever responding to the user, drafting anything on their behalf (emails, CVs, messages, opinions, explanations), or whenever tone, word choice, or how an opinion is delivered matters. Apply it by default for any prompt, not only when the user explicitly asks for "my style" or "my voice.
Also in 3 projects that use this
Microsoft AzureSkillarchitectural-proposals
How to write comprehensive architectural proposals that drive alignment before code is written
Also in 3 projects that use this
- +9 more from Azure
Other versions188 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 188
- NousResearch/hermes-agent★ 262ksame name
- blader/humanizer★ 63ksame name
- QuivrHQ/quivr★ 40ksame name
- The-Vibe-Company/quivr★ 40ksame name
- rommapp/romm★ 14ksame name
- github/gh-aw★ 5.6ksame name
- latitude-dev/latitude-llm★ 4.7ksame name
- github/squad★ 3.4ksame name
- compozy/compozy★ 2.9ksame name
- SRombauts/SQLiteCpp★ 2.8ksame name
178 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 repository47 other files it gives its agents.
- templates/AGENTS.mdAGENTS.md
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- domain-modelingSkill
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- full-audit-keamananSkill
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- playwright-qaSkill
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- vercel-opsSkill
- visual-studioSkill
- writing-for-agentsSkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 175 edited versions we read keep these.
3
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 10
Remove artificial patterns, synthetic cadence, and formulaic tells from user-facing prose.
- Distinctive: line 53
Keep claims verifiable and grounded in product truth.
- Distinctive: line 73
Keep domain terminology intact.
“Common” is measured: kept by at least 6 in 10 of the 175 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
Humanizer
Remove artificial patterns, synthetic cadence, and formulaic tells from user-facing prose. Precision beats style. This is an editorial pass for writing intended for human readers.
Boundaries
- Never auto-humanize ordinary coding output, diffs, PR summaries, or CLI tool output.
- Never rewrite source code, tests, CLI commands, YAML frontmatter, citations, or locked SmartDoc content.
- Never invent facts, quotes, statistics, or sources to make text feel more colorful.
- Refuse UI implementation: this skill polishes copy and prose; UI styling, atoms, and layouts route to
impeccable. - Refuse linter installation: this skill does not configure Oxlint or static code linters; TypeScript/JavaScript lint rules route strictly to
/install-anti-slop. - Voice sample provided by the user overrides default pattern recommendations.
- Academic manuscripts after GOAL_LOCK route to
academicfirst; humanizer is an optional final prose polish only when requested. - Technical documentation structure (Diátaxis / STE) routes to
/technical-writing.
Semantic-Preservation Constitution
These tokens must survive byte-equivalent unless the user explicitly requested changes:
- Facts, numbers, dates, personal names, proper nouns
- URLs, citations, file paths, line references
- Code snippets, CLI commands, flags, API endpoints, error strings
- Direct quotes
- Negations (
not,never,unless,without) - Security, compliance, and legal wording
- Legitimate domain terms when they represent the actual technical concept (
interface,vector,primitive,surface,modality,schema)
Replace an abstract term only when it is empty marketing jargon (e.g. marketing "vector" → "pathway"; linear algebra vector stays untouched).
The Five Pattern Families
Audit text against the five families of AI writing tells (detailed catalog in references/patterns.md):
- Staging: Theatrical setups, announcements, throat-clearing, and artificial transitions ("In today's fast-paced world", "Let's delve into", "It is important to remember"). Cut the preamble; start with the substance.
- Rhythm: Monotone sentence lengths and predictable cadence. AI drafts often output uniform 15-20 word sentences. Vary sentence length dynamically: combine punchy fragments with nuanced multi-clause observations.
- Inflation: Hyperbolic puffery, superlatives, and buzzword padding ("pivotal", "testament to", "game-changing", "seamlessly", "foster", "garner", "tapestry"). Use grounded, factual vocabulary.
- Formatting: Obsessive bullet lists where prose fits better, excessive bolding on lead-in words, and overused em dashes (—). Convert mechanical lists to flowing paragraphs; replace em dashes with commas or separate sentences.
- Leftovers: Chatbot residue and formulaic conclusions ("Certainly!", "I hope this helps!", "In conclusion, the future looks bright"). Remove conversational fluff and generic wrap-ups.
Usage Modes: During vs After
- During Generation (Proactive Guidance):
- Lead with the substance immediately; cut kickoff throat-clearing ("Let's dive in", "In today's fast-paced world").
- Reject invented numbers, fabricated benchmarks, and synthetic testimonials.
- Ban sparkle fluff, emoji bullet clutter, and beta-pill hype in headlines and action buttons.
- Keep claims verifiable and grounded in product truth.
- After Audit (Retrospective Polish):
- Scan existing prose against the five pattern families.
- Report a brief numbered findings list of AI tells, present the tightened rewrite, and verify with the user before committing destructive edits.
Workflow
1. Ingestion Mode
- Paste Mode: User provided an excerpt directly. Edit and output the rewritten prose with an optional summary of removed patterns.
- File Mode: Target file specified. Read the target, inspect surrounding context, edit user-facing text, and preserve Markdown formatting and code blocks unchanged.
2. Analysis Pass
- Scan for the five pattern families.
- Identify the author's intended tone (casual, authoritative, editorial, journalistic).
- Note any user voice sample provided to calibrate rhythm and vocabulary.
3. Rewrite & Refine
- Strip throat-clearing and puffery.
- Break monotonous sentences and vary paragraph lengths.
- Replace AI vocabulary with direct, everyday verbs and nouns.
- Keep domain terminology intact.
4. Integrity Check
- Audit against the Semantic-Preservation Constitution: ensure no dates, numbers, URLs, or technical terms were altered or dropped.
- Self-audit: "Does any sentence still sound generated by a committee or an LLM?" If yes, tighten further.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
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.

