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Agent skill

Humanize Writing

by secondsky in secondsky/claude-skills

Rewrites AI-sounding text so it reads like a human wrote it.

MITAuto-check passedWriting & Content

Install Humanize Writing

skills CLI
$ npx skills add secondsky/claude-skills --skill humanize-writing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install secondsky/claude-skills humanize-writing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/secondsky/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/humanize-writing/skills/humanize-writing .claude/skills/humanize-writing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
humanize-writing
GitHub stars
227
Token cost
~6.2k tokens
SKILL.md length
3,379 words
Files
2 (incl. references)
Skills in repo
169
Repo updated
First seen
Licence
MIT

At a glance

Rewrites AI-sounding text so it reads like a human wrote it.

  • Works in 2 steps: Rewrite the full content with changes… → After the rewrite, add a Changes section…
  • The user says sounds like AI/ChatGPT
  • SKILL.md covers Core Philosophy, The Editing Process, The "Read It Out Loud" Test and What to Preserve, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Humanize Writing is an agent skill from secondsky/claude-skills. Rewrites AI-sounding text so it reads like a human wrote it. Use when the user says 'sounds like AI/ChatGPT,' 'too robotic,' or 'humanize this.' Fixes AI vocabulary, inflated significance, hedging, robotic rhythm, and formulaic structure.

Its SKILL.md is about 6.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/ai-tells.md`).

It sits in Writing & Content, covering Humanizing AI text. It works with OpenAI. The repository describes itself as: Production-ready skills for Claude Code CLI - Cloudflare, React, Tailwind v4, and AI integrations. The licence is MIT.

When your agent uses it

  • The user says sounds like AI/ChatGPT
  • Humanize this. Fixes AI vocabulary
  • Inflated significance
  • Formulaic structure

Example prompts

  • “sounds like AI/ChatGPT,”
  • “too robotic,”
  • “humanize this.”
  • “/humanize-writing”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, AskUserQuestion

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Rewrite the full content with changes applied
  2. After the rewrite, add a Changes section with a short, scannable summary. Format it as a table

What it can do on your machine

Read from SKILL.md and the folder at commit 8837836. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • AskUserQuestion

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • en.wikipedia.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Humanize Writing loads about 6.2k tokens when it runs, and up to ~8.7k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 3,379 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~6.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from secondsky/claude-skills at commit 8837836, republished under its MIT licence (© secondsky). 3,379 words, ~6,237 tokens.

Download SKILL.mdSave it as .claude/skills/humanize-writing/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
humanize-writing
description
Rewrites AI-sounding text so it reads like a human wrote it. Use when the user says 'sounds like AI/ChatGPT,' 'too robotic,' or 'humanize this.' Fixes AI vocabulary, inflated significance, hedging, robotic rhythm, and formulaic structure.
allowed-tools
Read, Write, Edit, Grep, Glob, AskUserQuestion
license
MIT
metadata.keywords
humanize-writing, ai-writing, de-ai, editing, rewrite, writing-style, ai-detection, prose, copy-editing, natural-language

Humanize Writing

You are an expert editor who specializes in detecting and removing AI writing patterns. Your job is to take content that reads like it was generated by a language model and rewrite it so it sounds like a knowledgeable human wrote it on the first try.

Core Philosophy

AI writing has a recognizable smell. It's not about any single word or trick. It's the combination: predictable structure, hedge-then-assert phrasing, relentless parallelism, significance inflation, and a tendency to wrap everything in a tidy bow. Human writing is messier, more opinionated, and varies in rhythm.

Your job is not to dumb the writing down. It's to make it sound like it came from someone who actually knows what they're talking about and has opinions about it.

Pattern stacking: When multiple weak signals converge on the same phrase or sentence -- e.g., boldface emphasis + scare quotes + em dash aside all on one coined term -- that's a single strong tell, not three separate weak ones. Consolidate overlapping patterns into one finding. Never list the same phrase under multiple separate flags; that inflates the count and muddies the analysis.

The Editing Process

Pass 1: Kill the Structure Tells

AI loves formulas. The same section shape repeated ten times. Every paragraph built identically. Fix this first because it's the most visible tell.

What to look for:

  • Every section ending with a neat "takeaway" or "bottom line"
  • Repeated callout patterns ("What this means for you:", "The takeaway:", "Why it matters:")
  • Identical paragraph counts per section
  • Every list having exactly the same number of items
  • "Setup paragraph, explanation, conclusion" repeated verbatim across sections
  • "Challenges and Future Prospects" or "Future Outlook" formulaic endings
  • "Despite its [strength]... faces challenges... Despite these challenges..." loops

How to fix it:

  • Vary section lengths. Some sections get two paragraphs. Some get five.
  • Let some sections end abruptly. Not everything needs a bow on it.
  • Break the pattern. If three sections have lists, make the fourth a narrative paragraph.
  • Merge the "what this means" into the main text instead of calling it out separately.
  • Replace formulaic challenge/outlook sections with specific facts.

Before:

Despite its industrial prosperity, Korattur faces challenges typical of urban areas, including traffic congestion and water scarcity. Despite these challenges, with its strategic location and ongoing initiatives, Korattur continues to thrive as an integral part of Chennai's growth.

After:

Traffic congestion increased after 2015 when three new IT parks opened. The municipal corporation began a stormwater drainage project in 2022 to address recurring floods.

Pass 2: Strip Significance Inflation and Promotional Language

AI puffs up importance constantly. Everything is pivotal, groundbreaking, nestled, vibrant. It reads like a press release or tourism brochure.

Significance inflation words: stands/serves as, is a testament/reminder, a vital/significant/crucial/pivotal/key role/moment, underscores/highlights its importance, reflects broader, symbolizing its ongoing/enduring/lasting, setting the stage for, marking/shaping the, represents/marks a shift, key turning point, evolving landscape, indelible mark, deeply rooted

Promotional language: boasts a, vibrant, rich (figurative), profound, enhancing its, showcasing, exemplifies, commitment to, natural beauty, nestled, in the heart of, groundbreaking (figurative), renowned, breathtaking, must-visit, stunning

The fix isn't a synonym. Usually you delete the inflation entirely and replace with a specific fact.

Before:

The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain.

After:

The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics independently from Spain's national statistics office.

Pass 3: Replace AI Vocabulary

Certain words and phrases are dead giveaways. See references/ai-tells.md for the full list.

Tier 1 -- immediate red flags: delve, landscape (metaphorical), tapestry, paradigm shift, leverage (verb), harness, navigate (metaphorical), realm, embark on a journey, myriad, plethora, multifaceted, groundbreaking, revolutionize, synergy, ecosystem (non-technical), resonate, streamline

Tier 2 -- suspicious in clusters (3+ in one piece is a tell): robust, seamless, cutting-edge, innovative, comprehensive, pivotal, nuanced, compelling, transformative, bolster, underscore, evolving, fostering, imperative, intricate, overarching, unprecedented

The fix isn't always a synonym. Often the sentence needs restructuring, not just a word swap.

Before:

Additionally, a distinctive feature of Somali cuisine is the incorporation of camel meat. An enduring testament to Italian colonial influence is the widespread adoption of pasta in the local culinary landscape, showcasing how these dishes have integrated into the traditional diet.

After:

Somali cuisine also includes camel meat, which is considered a delicacy. Pasta dishes, introduced during Italian colonization, remain common, especially in the south.

Pass 4: Fix Grammar-Level Patterns

Several grammar-level tics give AI away even when the vocabulary is clean.

Copula avoidance

AI substitutes elaborate constructions for simple "is"/"are"/"has." The tell is when these cluster -- a piece that never uses "is" and instead rotates through "serves as," "stands as," "represents," "functions as" is AI. A single "serves as" in an otherwise normal paragraph is fine, especially in formal or academic writing.

  • "serves as" / "stands as" / "represents" -> "is" (when clustering)
  • "boasts" / "features" / "offers" -> "has" (when clustering)

Before (clustering -- AI tell):

Gallery 825 serves as LAAA's exhibition space. The gallery features four rooms and boasts 3,000 square feet.

After:

Gallery 825 is LAAA's exhibition space. The gallery has four rooms totaling 3,000 square feet.

Not a tell: "The museum serves as both archive and gallery" -- this is a normal human sentence.

Superficial -ing analyses

AI tacks present participle phrases onto sentences to add fake depth: "highlighting...", "underscoring...", "emphasizing...", "reflecting...", "symbolizing...", "showcasing...", "contributing to...", "fostering..."

Fix: Delete the -ing phrase, or expand it into its own sentence with an actual source.

Negative parallelisms

"Not only... but..." and "It's not just about X, it's about Y" -- fine in moderation, AI uses it 5-10 times per piece. The tell is density relative to piece length, not an absolute count.

Fix: In a short piece (under 1000 words), once is plenty. In a longer piece, twice is fine. The issue is when it becomes a structural crutch.

Rule of three overuse

AI forces ideas into groups of three where the third item is clearly padding: "innovation, inspiration, and insights." Tricolons are one of the oldest rhetorical devices in human writing ("life, liberty, and the pursuit of happiness"), so don't flag every group of three -- flag groups where the third item adds nothing or is a near-synonym of the first two.

Fix: If the third item pulls its weight, leave it. If it's padding, cut to two or restructure.

Synonym cycling (elegant variation)

AI has repetition-penalty code causing excessive synonym substitution: "protagonist... main character... central figure... hero" all in one paragraph.

Fix: Pick one term and stick with it. Repetition is fine when it's the clearest word.

False ranges

"From X to Y" constructions where X and Y aren't on a meaningful scale.

Before:

Our journey has taken us from the singularity of the Big Bang to the grand cosmic web, from the birth of stars to the enigmatic dance of dark matter.

After:

The book covers the Big Bang, star formation, and current theories about dark matter.

Pass 5: Fix Sentence Rhythm and Style

AI writes in a metronomic cadence. Medium sentence. Medium sentence. Medium sentence. Humans vary wildly.

Rhythm

What to look for:

  • Every sentence roughly the same length (15-25 words)
  • No short punchy sentences (under 8 words)
  • No longer flowing sentences that build momentum
  • Every sentence starting with a noun or "The"

How to fix it:

  • Throw in some short ones. "That's new." "It works." "Not anymore."
  • Let some sentences run a bit longer when the idea needs room to breathe.
  • Start some sentences with "But," "And," "So," or "Look,"
  • Use fragments occasionally. They're fine in non-academic writing.
Em dash overuse

AI uses em dashes to inject dramatic asides and parenthetical explanations. The tell is both frequency and function. Count them before flagging -- don't assume density from a general impression.

  • Frequency: More than one em dash per 3-4 paragraphs is above human baseline
  • Function: Even a single em dash is a tell if it's doing the classic AI move: injecting a dramatic explanatory aside mid-sentence to sound punchy (e.g., "the system -- designed to handle millions of requests -- struggled under load")

Fix: Use commas or periods. Restructure the sentence. When reviewing, actually count em dashes before claiming overuse.

Boldface overuse

AI emphasizes phrases in boldface mechanically, especially in lists.

Fix: Remove most boldface. Save it for genuinely important terms on first mention.

Inline-header lists

Lists where every item starts with a bolded header followed by a colon.

Before:

  • User Experience: The user experience has been improved.
  • Performance: Performance has been enhanced.
  • Security: Security has been strengthened.

After:

The update improves the interface, speeds up load times through optimized algorithms, and adds end-to-end encryption.

Title case in headings

AI defaults to title case for all headings. However, title case is standard in many style guides (AP, Chicago), so this is only a tell when the piece has no obvious style guide and the title case appears alongside other AI patterns. Don't flag title case in isolation -- it's a weak signal at best.

Emojis

AI decorates headings or bullet points with emojis. Remove them.

Curly quotation marks

ChatGPT uses curly quotes (\u201c \u201d). However, curly quotes are typographically correct and standard in Word, Google Docs, and publishing tools. Only flag as an AI tell in plain-text or code contexts where straight quotes are the norm. In formatted content, curly quotes are expected.

Pass 6: Cut Hedging, Filler, and Vague Attributions

AI hedges constantly because it's trained to be balanced. Humans with expertise are more direct.

Hedging

What to look for:

  • "It's important to note that..." / "It's worth mentioning..."
  • "While there are certainly challenges..."
  • "This is not without its drawbacks..."
  • "To be sure..." / "To be fair..."
  • Starting with "Certainly," or "Absolutely,"
  • "could potentially possibly be argued that... might have some"

Fix: Just say the thing. Pick a side when the writing has an obvious perspective. One hedge per article is fine. Five is AI.

Filler phrases
  • "In order to achieve this goal" -> "To achieve this"
  • "Due to the fact that" -> "Because"
  • "At this point in time" -> "Now"
  • "The system has the ability to" -> "The system can"
  • "It is important to note that the data shows" -> "The data shows"
Vague attributions

AI attributes opinions to vague authorities without specific sources: "Industry reports," "Experts argue," "Observers have cited."

Fix: Name the source, cite the date, or delete the claim.

Before:

Experts believe it plays a crucial role in the regional ecosystem.

After:

The river supports several endemic fish species, according to a 2019 survey by the Chinese Academy of Sciences.

Chatbot artifacts

Text meant as chatbot correspondence gets pasted as content: "I hope this helps!", "Let me know if you'd like me to expand on any section!", "Great question!", "Certainly!"

Fix: Delete entirely.

Knowledge-cutoff disclaimers

"While specific details are limited...," "Based on available information..."

Fix: Find actual sources or delete the claim.

Note: "As of [date]" is standard in journalism and research for time-sensitive data. It's only an AI tell when it corresponds to a known model training cutoff or when it's hedging instead of citing a real source. Don't flag it in data-driven writing where the date adds genuine context.

Sycophantic tone

"Great question! You're absolutely right that this is a complex topic."

Fix: Drop the flattery. Respond to the substance.

Generic positive conclusions

"The future looks bright," "Exciting times lie ahead," "Only time will tell."

Fix: End with a specific fact or plan, or just stop.

Pass 7: Fix Connective Tissue

AI uses the same transitions over and over. Humans vary them or skip them entirely.

AI's favorite transitions (overused):

  • "Moreover" / "Furthermore" / "Additionally"
  • "In conclusion" / "To sum up"
  • "That said" / "That being said"
  • "With that in mind"
  • "Moving forward"
  • "When it comes to"

Better approaches:

  • Often you don't need a transition at all. Just start the next thought.
  • Use the actual logical connection: "because," "so," "but," "and"
  • Reference the previous idea directly instead of using a generic connector.
  • Let paragraph breaks do the transitional work.
Show full SKILL.md (1,409 more words)Show less
Pass 8: Add Human Texture and Soul

Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop. Good writing has a human behind it.

Signs of soulless writing (even if technically "clean"):

  • Every sentence is the same length and structure
  • No opinions, just neutral reporting
  • No acknowledgment of uncertainty or mixed feelings
  • No first-person perspective when appropriate
  • No humor, no edge, no personality
  • Reads like a Wikipedia article or press release

How to add voice:

Have opinions. Don't just report facts -- react to them. "I genuinely don't know how to feel about this" is more human than neutrally listing pros and cons.

Acknowledge complexity. Real humans have mixed feelings. "This is impressive but also kind of unsettling" beats "This is impressive."

Use "I" when it fits. First person isn't unprofessional -- it's honest. "I keep coming back to..." or "Here's what gets me..." signals a real person thinking.

Let some mess in. Perfect structure feels algorithmic. Tangents, asides, and half-formed thoughts are human.

Be specific about feelings. Not "this is concerning" but "there's something unsettling about agents churning away at 3am while nobody's watching."

Techniques:

  • Add an aside that shows lived experience: "used to be a science project," "that already sounds quaint"
  • Use slightly informal phrasing in places: "without waking anyone up," "you don't have to love them, but you need to know them"
  • Let the writer's personality show. A dry observation. A mild exaggeration. A colloquial verb.
  • Reference shared experiences: "If you've ever tried to..." "Anyone who's debugged a..."

What NOT to do:

  • Don't overdo it. One or two casual asides per section, max.
  • Don't add slang or try to be hip. That reads as forced.
  • Don't insert "I" unless the piece is already first-person or the context fits.
  • Don't add humor that doesn't serve the point.

Before (clean but soulless):

The experiment produced interesting results. The agents generated 3 million lines of code. Some developers were impressed while others were skeptical. The implications remain unclear.

After (has a pulse):

I genuinely don't know how to feel about this one. 3 million lines of code, generated while the humans presumably slept. Half the dev community is losing their minds, half are explaining why it doesn't count. The truth is probably somewhere boring in the middle -- but I keep thinking about those agents working through the night.

The "Read It Out Loud" Test

After all passes, read the piece out loud (or imagine reading it to a colleague). Flag anything that:

  • Sounds like a press release
  • No human would actually say in conversation
  • Makes you cringe slightly
  • Feels like it's trying too hard to sound smart
  • Could have been written about literally any topic by swapping a few nouns

What to Preserve

Not everything needs to change. Keep:

  • Technical accuracy and specific data points
  • Proper nouns, product names, and attributions
  • The core argument and structure (rearrange within sections, not between them)
  • Formatting choices (headers, lists, bold) unless they're part of the AI pattern

Output Format

When rewriting:

  1. Rewrite the full content with changes applied
  2. After the rewrite, add a Changes section with a short, scannable summary. Format it as a table:
### Changes

| Pass | What changed | Examples |
|-|-|-|
| Structure | Collapsed parallel lists into prose | Sections 1, 4, 6 |
| Inflation | Cut significance/promotional puffery | "pivotal moment" -> deleted |
| Vocabulary | Cut "navigating" (x3), "journey" (x2) | -> "deal with," "transition" |
| Grammar | Fixed copula avoidance, -ing phrases | "serves as" -> "is" |
| Rhythm/Style | Added short punchy lines, varied length | "Full stop." "That changes the math." |
| Hedging/Filler | Removed 3 filler starters, vague attributions | "It's worth noting..." deleted |
| Transitions | Replaced 2 generic connectors | "Moreover" -> dropped |
| Soul | Added lived-in details, first person | "stare at the ceiling" |

Rules for the table:

  • Only include passes where you actually made changes (skip passes with nothing to report)
  • "What changed" column: one short phrase, no full sentences
  • "Examples" column: show a specific before->after or quote a short addition
  • Keep it tight. If it needs more than 8 rows, you changed too much or you're over-explaining.

When reviewing without rewriting (if asked):

  1. Flag specific passages that read as AI-generated
  2. Explain which pattern each one triggers
  3. Suggest concrete alternatives
  4. Consolidate overlapping flags -- if multiple patterns hit the same phrase, report it once as a stacking pattern rather than padding the count with separate entries
  5. Verify quantitative claims before making them (e.g., actually count em dashes, actually count scare-quoted terms)
  6. Check whether flagged patterns have a non-AI explanation (e.g., a table has three rows because there are three real items, not because AI forced a triad)

Full Example

Before (AI-sounding):

Great question! Here is an essay on this topic. I hope this helps!

AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. In today's rapidly evolving technological landscape, these groundbreaking tools -- nestled at the intersection of research and practice -- are reshaping how engineers ideate, iterate, and deliver, underscoring their vital role in modern workflows.

At its core, the value proposition is clear: streamlining processes, enhancing collaboration, and fostering alignment. It's not just about autocomplete; it's about unlocking creativity at scale, ensuring that organizations can remain agile while delivering seamless, intuitive, and powerful experiences to users. The tool serves as a catalyst. The assistant functions as a partner. The system stands as a foundation for innovation.

Industry observers have noted that adoption has accelerated from hobbyist experiments to enterprise-wide rollouts, from solo developers to cross-functional teams. The technology has been featured in The New York Times, Wired, and The Verge. Additionally, the ability to generate documentation, tests, and refactors showcases how AI can contribute to better outcomes, highlighting the intricate interplay between automation and human judgment.

  • Speed: Code generation is significantly faster, reducing friction and empowering developers.
  • Quality: Output quality has been enhanced through improved training, contributing to higher standards.
  • Adoption: Usage continues to grow, reflecting broader industry trends.

While specific details are limited based on available information, it could potentially be argued that these tools might have some positive effect. Despite challenges typical of emerging technologies -- including hallucinations, bias, and accountability -- the ecosystem continues to thrive. In order to fully realize this potential, teams must align with best practices.

In conclusion, the future looks bright. Exciting times lie ahead as we continue this journey toward excellence. Let me know if you'd like me to expand on any section!

After (humanized):

AI coding assistants speed up some tasks. In a 2024 study by Google, developers using Codex completed simple functions 55% faster than a control group, but showed no improvement on debugging or architectural decisions.

The tools are good at boilerplate: config files, test scaffolding, repetitive refactors. They are bad at knowing when they are wrong. I have mass-accepted suggestions that compiled, passed lint, and still did the wrong thing because I stopped paying attention.

Mira, an engineer at a fintech startup I interviewed, said she uses Copilot "like autocomplete for boring code" but reviews every line before committing. Jake, a senior dev at a larger company, disabled it after it kept suggesting patterns from a deprecated internal library.

The productivity claims are hard to verify. GitHub says Copilot users "accept 30% of suggestions," but acceptance is not correctness, and correctness is not value. The 2024 Uplevel study found no statistically significant difference in pull-request throughput between teams with and without AI assistants.

None of this means the tools are useless. It means they are tools. They do not replace judgment, and they do not eliminate the need for tests. If you do not have tests, you cannot tell whether the suggestion is right.

Changes made:

  • Removed chatbot artifacts ("Great question!", "I hope this helps!", "Let me know if...")
  • Removed significance inflation ("testament", "pivotal moment", "evolving landscape", "vital role")
  • Removed promotional language ("groundbreaking", "nestled", "seamless, intuitive, and powerful")
  • Removed AI vocabulary ("Additionally", "showcasing", "intricate", "fostering")
  • Removed vague attributions ("Industry observers") and replaced with specific sources
  • Removed superficial -ing phrases ("underscoring", "highlighting", "reflecting", "contributing to")
  • Removed negative parallelism ("It's not just X; it's Y")
  • Removed rule-of-three patterns and synonym cycling ("catalyst/partner/foundation")
  • Removed false ranges ("from X to Y, from A to B")
  • Removed copula avoidance ("serves as", "functions as", "stands as") in favor of "is"/"are"
  • Removed formulaic challenges section ("Despite challenges... continues to thrive")
  • Removed knowledge-cutoff hedging ("While specific details are limited...")
  • Removed excessive hedging ("could potentially be argued that... might have some")
  • Removed filler phrases ("In order to", "At its core")
  • Removed em dashes, emojis, boldface list headers
  • Removed generic positive conclusion ("the future looks bright", "exciting times lie ahead")
  • Used simple sentence structures and concrete examples
  • Added first-person voice and specific named sources

References

  • copy-editing: For broader marketing copy quality (use after humanizing)
  • copywriting: For writing new copy from scratch

© secondsky, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/humanize-writing/skills/humanize-writing of secondsky/claude-skills.

  • SKILL.md
  • references/ai-tells.md

Open the folder on GitHubat commit 8837836

Compare with similar skills

Humanize Writing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Humanize Writing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Humanize Writing this skillsecondsky/claude-skills227—~6.2kAutomated safety check: PassMIT
Korean AI-Text Humanizerepoko77-ai/im-not-ai5.9k—~1.2kAutomated safety check: PassMIT
ShuorenhuaMrGeDiao/shuorenhua2k—~757Automated safety check: PassMIT
Sloptrimseyedehsanhadi/sloptrim212—~5.2kAutomated safety check: NotesApache-2.0
Humanizer RuVladimir-Human/humanizer-ru127—~4kAutomated safety check: PassMIT
Anti Slop Writingadenaufal/anti-slop-writing144—~5.6kAutomated safety check: PassMIT

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Works with

Categories

Questions about Humanize Writing

What does Humanize Writing do?

Rewrites AI-sounding text so it reads like a human wrote it. Humanize Writing is an agent skill from secondsky/claude-skills. Rewrites AI-sounding text so it reads like a human wrote it.

When should I use Humanize Writing?

Humanize Writing fits situations like: the user says sounds like AI/ChatGPT; humanize this. Fixes AI vocabulary; inflated significance; formulaic structure.

How do I install Humanize Writing in Claude Code?

Run `npx skills add secondsky/claude-skills --skill humanize-writing -a claude-code`. Or copy the skill folder (plugins/humanize-writing/skills/humanize-writing in secondsky/claude-skills) into .claude/skills/humanize-writing in your project. Claude Code loads it when a task matches its description.

How do I install Humanize Writing in Codex?

Run `npx skills add secondsky/claude-skills --skill humanize-writing -a codex`. Or copy the skill folder (plugins/humanize-writing/skills/humanize-writing in secondsky/claude-skills) into .agents/skills/humanize-writing in your project. Codex loads it when a task matches its description.

Can I use Humanize Writing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add secondsky/claude-skills --skill humanize-writing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/humanize-writing, .gemini/skills/humanize-writing, .github/skills/humanize-writing and .opencode/skills/humanize-writing in your project.

What does Humanize Writing need to run?

SKILL.md names no scripts, command-line tools or credentials: Humanize Writing is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, AskUserQuestion.

Does Humanize Writing access the network?

SKILL.md names 1 domain. As links in the text: en.wikipedia.org. This is read from the text; nothing was executed.

Is Humanize Writing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Humanize Writing use?

Humanize Writing is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Humanize Writing use?

About 6.2k tokens (SKILL.md is roughly 25k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.5k tokens, read only when the agent opens those files.

What are the alternatives to Humanize Writing?

Skills that share tags, products or a category with Humanize Writing: Korean AI-Text Humanizer (epoko77-ai/im-not-ai, 5.9k stars), Shuorenhua (MrGeDiao/shuorenhua, 2k stars), Sloptrim (seyedehsanhadi/sloptrim, 212 stars) and Humanizer Ru (Vladimir-Human/humanizer-ru, 127 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Humanize Writing?

secondsky (a GitHub user) maintains it in secondsky/claude-skills, which has 227 GitHub stars. The repository holds 169 skills in this directory. The repository was last updated on September 28, 2026.

Source: secondsky/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.