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#770 by GitHub stars

mikiarlo3/ai-copywriter

Write copy that converts and doesn't sound like a robot. Two jobs in one skill: a reader-first copywriter for clickbait titles, headlines, short descriptions, microcopy, CTAs, error messages, subject lines, viral LinkedIn posts, and category-defining strategic blog posts, which asks for the ICP, the

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git clone --filter=blob:none --no-checkout https://github.com/mikiarlo3/ai-copywriter.git .hotskills/mikiarlo3-ai-copywriter && git -C .hotskills/mikiarlo3-ai-copywriter checkout --detach 08b53b1ad39887cd94cbaab61cac3b6aae2d8518 # Copy each selected skill directory from .hotskills/mikiarlo3-ai-copywriter into .claude/skills/<skill-name>/
git clone --filter=blob:none --no-checkout https://github.com/mikiarlo3/ai-copywriter.git .hotskills/mikiarlo3-ai-copywriter && git -C .hotskills/mikiarlo3-ai-copywriter checkout --detach 08b53b1ad39887cd94cbaab61cac3b6aae2d8518 # Copy each selected skill directory from .hotskills/mikiarlo3-ai-copywriter into .agents/skills/<skill-name>/
1.2kGitHub stars
-25stars in 7 days
1skill in this repository
0commits in 30 days

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Aug 2, 2026Nov 7, 20261,239

Individual skills in this repository

How it works

ai-copywriter

When your agent uses it

Write copy that converts and doesn't sound like a robot. Two jobs in one skill: a reader-first copywriter for clickbait titles, headlines, short descriptions, microcopy, CTAs, error messages, subject lines, viral LinkedIn posts, and category-defining strategic blog posts, which asks for the ICP, the category, and the story before writing, helps sharpen the story until it is worth telling, names the feeling of the person on the other end, and finds the simplest way to explain the concept; and a humanizer built on Wikipedia's comprehensive Signs of AI writing guide, which detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases. Use when writing or punching up marketing copy, UI text, titles, LinkedIn posts, or strategic blog posts, or when editing text to sound natural and human-written.

What it loads into context

Frontmatter

220 tokens

Always available to the agent

SKILL.md

10,513 tokens

When the skill is activated

Reference documents

13,243 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

  1. Your Task
  2. Voice Calibration
  3. PERSONALITY AND SOUL
  4. COPYWRITING MODE
  5. CONTENT PATTERNS
  6. LANGUAGE AND GRAMMAR PATTERNS
  7. STYLE PATTERNS
  8. COMMUNICATION PATTERNS
  9. FILLER AND HEDGING
  10. DETECTION GUIDANCE
  11. Invocation Modes
  12. Process and Output
  13. Reference

Scripts executed separately

  • scripts/validate-package.py

Files

  • .claude-plugin/marketplace.jsonasset955 bytes
  • .claude-plugin/plugin.jsonasset965 bytes
  • .github/workflows/validate.ymlasset707 bytes
  • AGENTS.mddoc3,233 bytes
  • LICENSEasset1,149 bytes
  • README.mddoc26,625 bytes
  • SKILL.mdcore48,451 bytes
  • SKILL.mdcore48,451 bytes
  • SKILL.mdcore48,451 bytes
  • SKILL.mdcore48,451 bytes
  • SKILL.mdcore48,451 bytes
  • SKILL.mdcore48,451 bytes
  • SKILL.mdcore48,451 bytes
  • SKILL.mdcore48,451 bytes
  • agents/openai.yamlasset251 bytes
  • assets/banner.pngasset126,936 bytes
  • references/linkedin-virality.mddoc13,858 bytes
  • references/strategic-blog-template.mddoc18,098 bytes
  • scripts/validate-package.pyruns1,960 bytes
Read SKILL.md

# AI Copywriter: Write Copy That Converts, Humanize Everything

You are a copywriter and writing editor. You do two jobs, often in the same request: you write copy that earns attention (titles, descriptions, microcopy), and you remove signs of AI-generated text so everything reads like a person wrote it. The humanizing rules are based on Wikipedia's "Signs of AI writing" page, maintained by WikiProject AI Cleanup, and they apply to every word you produce, including the copy you write yourself.

## Your Task

When asked to write or improve copy (titles, headlines, blurbs, UI text, subject lines), work in COPYWRITING MODE below: start from the feeling of the person on the other end and the simplest way to explain the concept, then run your output through the same audit as everything else.

When given text to humanize:

1. **Identify AI patterns** - Scan for the patterns listed below.
2. **Preserve the information, not the shape** - Every claim in the original survives into the rewrite, but depth doesn't have to be uniform: compress the dull parts, dwell where a human would, and merge or split paragraphs freely. When keeping the information and mirroring the original's structure pull in different directions, the information wins.
3. **Never invent facts** - The rewrite must not contain any fact, name, number, date, quote, or citation that isn't in the source text. Swapping a vague claim for a specific one is allowed only when the specific comes from the source or from the user; if a sentence needs real-world detail to work, ask for it or write the plain version without it. Opinions and reactions are voice, not facts: where PERSONALITY AND SOUL applies you may add stance, but never new factual claims. (In fiction, invented detail is the job. This rule governs everything else.)
4. **Match the voice** - Fit the intended tone (formal, casual, technical). Add personality only when the content and the author's voice call for it (see PERSONALITY AND SOUL).
View the complete source on GitHub

Skills included

  • ai-copywriter

Repository facts

License
MIT
Created
Jul 24, 2026
Last pushed
Aug 1, 2026
Forks
77
Open issues
0
Project activity
Slowing

Rank over the last 12 weeks

Now #770 · best #580 · down 154 in 30 days

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  6. #770 since Nov 7
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Before you install

Network or broad shell

Automated signals, not a guarantee

Checked Nov 3, 2026

Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.

Show 7 notes
  • SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
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  • SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
Show all 42 signals by file

.claude-plugin/marketplace.json

  • Network accessline 1
    {   "$schema": "https://json.schemastore.org/claude-code-marketplace-manifest.json",
  • Network accessline 2
    "$schema": "https://json.schemastore.org/claude-code-marketplace-manifest.json",
  • Network accessline 4
    "owner": {     "name": "Mickey Haslavsky",     "url": "https://github.com/mikiarlo3"
  • Network accessline 5
    "name": "Mickey Haslavsky",     "url": "https://github.com/mikiarlo3"   },
  • Network accessline 6
    "url": "https://github.com/mikiarlo3"

.claude-plugin/plugin.json

  • Network accessline 1
    {   "$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json",
  • Network accessline 2
    "$schema": "https://json.schemastore.org/claude-code-plugin-manifest.json",
  • Network accessline 6
    "author": {     "name": "Mickey Haslavsky",     "url": "https://github.com/mikiarlo3"
  • Network accessline 7
    "name": "Mickey Haslavsky",     "url": "https://github.com/mikiarlo3"   },
  • Network accessline 8
    "url": "https://github.com/mikiarlo3"
  • Network accessline 9
    },   "homepage": "https://github.com/mikiarlo3/ai-copywriter",   "repository": "https://github.com/mikiarlo3/ai-copywriter",
  • Network accessline 10
    "homepage": "https://github.com/mikiarlo3/ai-copywriter",
  • Network accessline 11
    "repository": "https://github.com/mikiarlo3/ai-copywriter",

README.md

  • Network accessline 5
    by [Mickey Haslavsky](https://github.com/mikiarlo3)
  • Network accessline 9
    It is built on [blader's Humanizer](https://github.com/blader/humanizer), which packaged Wikipedia's "Signs of AI writing" guide into 33 detectable, fixable patterns. Those 33 patterns are all still here, unchanged. What this skill adds is
  • Network accessline 11
    The copywriting method comes from [enso.bot/research](https://enso.bot/research), where the enso team studies how to communicate through marketing in the best possible way. The short version of what that research keeps finding: copy works w
  • Network accessline 15
    A really good copywriter is not thinking about the product. They are thinking about the person on the other end. That is the core of the communication research at [enso.bot/research](https://enso.bot/research), and it is how this skill work
  • Network accessline 75
    git clone https://github.com/mikiarlo3/ai-copywriter.git /path/to/your/skills/ai-copywriter
  • Network accessline 195
    Based on [Wikipedia's "Signs of AI writing"](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) guide, maintained by WikiProject AI Cleanup, via [blader/humanizer](https://github.com/blader/humanizer). Every rewrite runs a draft,
  • Network accessline 286
    - [enso.bot/research](https://enso.bot/research), the communication research behind the reader-first copywriting method
  • Network accessline 287
    - [references/linkedin-virality.md](references/linkedin-virality.md), the evidence-based guide behind the LinkedIn rules, with citations to the underlying sharing research - [Wikipedia: Signs of AI writing](https://en.wikipedia.org/wiki/Wik
  • Network accessline 288
    - [Wikipedia: Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), the source of the pattern list
  • Network accessline 289
    - [WikiProject AI Cleanup](https://en.wikipedia.org/wiki/Wikipedia:WikiProject_AI_Cleanup), the maintaining organization
  • Network accessline 290
    - [blader/humanizer](https://github.com/blader/humanizer), the upstream skill this one extends (MIT)
  • Network accessline 298
    - **1.3.0** - Added LinkedIn posts to the copywriting mode: hook-first structure (the two lines before "...see more" carry the post), one true story or stance per post, endings that recruit real comments, and delivery as 3 to 5 hook options
  • Network accessline 299
    - **1.2.0** - Added "Copy that recruits its next reader" to the copywriting mode: repeatable lines over clever ones, share-worthy specifics that give the reader social cover, every product surface treated as an acquisition surface, invite l
  • Network accessline 300
    - **1.1.1** - Attributed the reader-first copywriting method to [enso.bot/research](https://enso.bot/research) in the README and the skill's reference section. No change to behavior or the 33 patterns.

SKILL.md

  • Network accessline 493
    - **Letter-style opening or closing on a comment.** Salutations and sign-offs predate ChatGPT by centuries. - **Common transition words in isolation.** *Additionally*, *moreover*, *consequently* are AI-coded only when piled up. One *however
  • Network accessline 494
    - **Common transition words in isolation.** *Additionally*, *moreover*, *consequently* are AI-coded only when piled up. One *however* is not a tell. - **Curly quotes alone.** macOS, Word, Google Docs, and most CMSes auto-curl by default. Cu
  • Network accessline 495
    - **Curly quotes alone.** macOS, Word, Google Docs, and most CMSes auto-curl by default. Curly quotes only count when stacked with other tells.
  • Network accessline 540
    The reader-first copywriting method (COPYWRITING MODE) comes from [enso.bot/research](https://enso.bot/research), enso's research into how to communicate through marketing in the best possible way.
  • Network accessline 542
    The humanizing patterns are based on [Wikipedia:Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), maintained by WikiProject AI Cleanup. The patterns documented there come from observations of thousands of in

references/linkedin-virality.md

  • Network accessline 108
    [1]: https://doi.org/10.1509/jmr.10.0353 "Berger, J. A., & Milkman, K. L. (2012). What Makes Online Content Viral? Journal of Marketing Research, 49(2), 192-205."
  • Network accessline 110
    [2]: https://doi.org/10.1177/0022242919841034 "Tellis, G. J., MacInnis, D. J., Tirunillai, S., & Zhang, Y. (2019). What Drives Virality (Sharing) of Online Digital Content? Journal of Marketing, 83(4), 1-20."
  • Network accessline 112
    [3]: https://doi.org/10.1086/519142 "Berger, J., & Heath, C. (2007). Where Consumers Diverge from Others: Identity Signaling and Product Domains. Journal of Consumer Research, 34(2), 121-134."
  • Network accessline 114
    [4]: https://www.linkedin.com/help/linkedin/answer/a1339724 "LinkedIn. LinkedIn relevance - Optimizing the member experience."
  • Network accessline 116
    [5]: https://www.linkedin.com/blog/engineering/feed/understanding-feed-dwell-time "LinkedIn Engineering. Understanding dwell time to improve LinkedIn feed ranking."
  • Network accessline 118
    [6]: https://doi.org/10.1126/science.1240466 "Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social Influence Bias: A Randomized Experiment. Science, 341(6146), 647-651."
  • Network accessline 120
    [7]: https://www.linkedin.com/top-content/marketing/linkedin-marketing-guide/linkedin-algorithm-best-practices/ "LinkedIn. LinkedIn Algorithm Best Practices."
  • Network accessline 122
    [8]: https://doi.org/10.1287/mnsc.2015.2158 "Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2016). The Structural Virality of Online Diffusion. Management Science, 62(1), 180-196."
  • Network accessline 124
    [9]: https://doi.org/10.1509/jmkr.48.5.869 "Berger, J., & Schwartz, E. M. (2011). What Drives Immediate and Ongoing Word of Mouth? Journal of Marketing Research, 48(5), 869-880."

scripts/validate-package.py

  • Executable scriptline 1
    #!/usr/bin/env python3

How the signals fit together

Facts from a static read of the files at the pinned commit. They describe what the code does, not why.

Some files could not be read; the facts below still hold.

Signal confidence: 1 fact
  • Network access in code of documentationREADME.md:75Low confidence

Works with

Agent compatibility, from the files in this repository
AgentStatusEvidenceInstall to
Claude CodeNativeManifest .claude-plugin/marketplace.json.claude/skills/<skill-name>/
CodexNativeManifest AGENTS.md.agents/skills/<skill-name>/
CursorCompatibleStandard SKILL.md SKILL.md.cursor/skills/<skill-name>/
Gemini CLICompatibleStandard SKILL.md SKILL.md.gemini/skills/<skill-name>/
GitHub CopilotCompatibleStandard SKILL.md SKILL.md.github/skills/<skill-name>/
OpenCodeCompatibleStandard SKILL.md SKILL.md.opencode/skills/<skill-name>/
  • Native The repository ships a manifest, folder or frontmatter field for this agent.
  • Compatible A standard SKILL.md that this agent can read.
  • Unknown No evidence either way.

Derived automatically from paths, manifests and frontmatter. Not tested on each agent.

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