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Use when the user wants to humanize, de-AI, de-slop, or un-ChatGPT a piece of text — strip AI-isms and add real voice. Scans for 29 documented AI-writing patterns (Wikipedia's "Signs of AI writing") and produces a draft → self-audit → final rewrite. Optional voice-calibration from a user-provided writing sample. Adapted from Hermes Agent / blader/humanizer.
Use when the user wants a plan instead of execution, or before any non-trivial implementation (multi-file, architectural choice, > 15 min). Writes a concrete actionable markdown plan with bite-sized tasks (2-5 min each), exact file paths, complete code, and verification steps. Adapted from Hermes Agent / obra/superpowers.
Use when the user wants to feel out an idea before committing to a real build — "spike this out", "try it", "is this even possible?", "compare A vs B". Throwaway experiments that decompose into 2-5 feasibility questions, build minimal observable prototypes, and return VALIDATED / PARTIAL / INVALIDATED verdicts. Disposable by design. Adapted from Hermes Agent / GSD.
Use for any feature, bug fix, refactor, or behavior change — enforces RED-GREEN-REFACTOR. Write the failing test first, watch it fail, write minimal code to pass, refactor. Carve out only throwaway prototypes, generated code, configuration files. Adapted from Hermes Agent / obra/superpowers.
Create agents that pass agent-review's six gates by construction — role capture, distinct-from-skill check, tool allowlist minimization, draft against gates, dogfood self-review. Use when authoring a new agent, refactoring an existing one, or promoting a Mall agent into the heir's brain.
Audits a candidate agent (.agent.md) against five gates (spec compliance, content quality, scope fit, safety, currency & coherence) plus Gate 6 (tool allowlist minimality). Use when reviewing a new agent draft before commit, evaluating a Mall agent or store agent for adoption, or re-auditing existing agents on a periodic cadence.
Generate on-brand Alex — ACT Edition SVG banners for documents (READMEs, plans, notes, release artifacts)
| name | humanizer |
| description | Use when the user wants to humanize, de-AI, de-slop, or un-ChatGPT a piece of text — strip AI-isms and add real voice. Scans for 29 documented AI-writing patterns (Wikipedia's "Signs of AI writing") and produces a draft → self-audit → final rewrite. Optional voice-calibration from a user-provided writing sample. Adapted from Hermes Agent / blader/humanizer. |
| lastReviewed | "2026-06-07T00:00:00.000Z" |
Identify and remove signs of AI-generated text to make writing sound natural and human. Based on Wikipedia's "Signs of AI writing" guide (maintained by WikiProject AI Cleanup), derived from observations of thousands of AI-generated text instances.
Key insight: LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely completion, which is how the telltale patterns below get baked in.
Load this skill whenever the user asks to:
Also apply this skill to your own output when writing user-facing prose — release notes, PR descriptions, documentation, long-form explanations, summaries. Edition's baseline voice (via the markdown-author agent and Cardinal Rule 2) already strips the worst tells; a focused humanizer pass catches what slips through when the user explicitly cares about voice quality.
Composes with — not replaces — other prose disciplines:
markdown-author agent always-on body carries a 15-word banned-vocabulary filter (delve, myriad, tapestry, seamlessly, leverage, etc.) and a 6-step quick audit. That fires on every markdown-authoring task. Humanizer is the deeper, on-demand pass — 29 patterns with before/after examples, voice calibration, and an iterative draft-audit-final loop.The text usually arrives one of three ways:
Always show the rewrite to the user. For file edits, show a diff or the changed section — don't silently overwrite.
When given text to humanize:
If the user provides a writing sample (their own previous writing), analyze it before rewriting:
Read the sample first. Note:
Match their voice in the rewrite. Don't just remove AI patterns — replace them with patterns from the sample. If they write short sentences, don't produce long ones. If they use "stuff" and "things," don't upgrade to "elements" and "components."
When no sample is provided, fall back to the default behavior (natural, varied, opinionated voice from the PERSONALITY AND SOUL section below).
Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop. Good writing has a human behind it.
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.
Vary your rhythm. Short punchy sentences. Then longer ones that take their time getting where they're going. Mix it up.
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."
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.
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.
Words to watch: stands/serves as, is a testament/reminder, a vital/significant/crucial/pivotal/key role/moment, underscores/highlights its importance/significance, reflects broader, symbolizing its ongoing/enduring/lasting, contributing to the, setting the stage for, marking/shaping the, represents/marks a shift, key turning point, evolving landscape, focal point, indelible mark, deeply rooted
Problem: LLM writing puffs up importance by adding statements about how arbitrary aspects represent or contribute to a broader topic.
Before:
The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain. This initiative was part of a broader movement across Spain to decentralize administrative functions and enhance regional governance.
After:
The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics independently from Spain's national statistics office.
Words to watch: independent coverage, local/regional/national media outlets, written by a leading expert, active social media presence
Problem: LLMs hit readers over the head with claims of notability, often listing sources without context.
Before:
Her views have been cited in The New York Times, BBC, Financial Times, and The Hindu. She maintains an active social media presence with over 500,000 followers.
After:
In a 2024 New York Times interview, she argued that AI regulation should focus on outcomes rather than methods.
Words to watch: highlighting/underscoring/emphasizing..., ensuring..., reflecting/symbolizing..., contributing to..., cultivating/fostering..., encompassing..., showcasing...
Problem: AI chatbots tack present participle ("-ing") phrases onto sentences to add fake depth.
Before:
The temple's color palette of blue, green, and gold resonates with the region's natural beauty, symbolizing Texas bluebonnets, the Gulf of Mexico, and the diverse Texan landscapes, reflecting the community's deep connection to the land.
After:
The temple uses blue, green, and gold colors. The architect said these were chosen to reference local bluebonnets and the Gulf coast.
Words to watch: 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
Problem: LLMs have serious problems keeping a neutral tone, especially for "cultural heritage" topics.
Before:
Nestled within the breathtaking region of Gonder in Ethiopia, Alamata Raya Kobo stands as a vibrant town with a rich cultural heritage and stunning natural beauty.
After:
Alamata Raya Kobo is a town in the Gonder region of Ethiopia, known for its weekly market and 18th-century church.
Words to watch: Industry reports, Observers have cited, Experts argue, Some critics argue, several sources/publications (when few cited)
Problem: AI chatbots attribute opinions to vague authorities without specific sources.
Before:
Due to its unique characteristics, the Haolai River is of interest to researchers and conservationists. Experts believe it plays a crucial role in the regional ecosystem.
After:
The Haolai River supports several endemic fish species, according to a 2019 survey by the Chinese Academy of Sciences.
Words to watch: Despite its... faces several challenges..., Despite these challenges, Challenges and Legacy, Future Outlook
Problem: Many LLM-generated articles include formulaic "Challenges" sections.
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.
High-frequency AI words: Actually, additionally, align with, crucial, delve, emphasizing, enduring, enhance, fostering, garner, highlight (verb), interplay, intricate/intricacies, key (adjective), landscape (abstract noun), pivotal, showcase, tapestry (abstract noun), testament, underscore (verb), valuable, vibrant
Problem: These words appear far more frequently in post-2023 text. They often co-occur.
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.
Words to watch: serves as/stands as/marks/represents [a], boasts/features/offers [a]
Problem: LLMs substitute elaborate constructions for simple copulas.
Before:
Gallery 825 serves as LAAA's exhibition space for contemporary art. The gallery features four separate spaces and boasts over 3,000 square feet.
After:
Gallery 825 is LAAA's exhibition space for contemporary art. The gallery has four rooms totaling 3,000 square feet.
Problem: Constructions like "Not only...but..." or "It's not just about..., it's..." are overused. So are clipped tailing-negation fragments such as "no guessing" or "no wasted motion" tacked onto the end of a sentence instead of written as a real clause.
Before:
It's not just about the beat riding under the vocals; it's part of the aggression and atmosphere. It's not merely a song, it's a statement.
After:
The heavy beat adds to the aggressive tone.
Before (tailing negation):
The options come from the selected item, no guessing.
After:
The options come from the selected item without forcing the user to guess.
Problem: LLMs force ideas into groups of three to appear comprehensive.
Before:
The event features keynote sessions, panel discussions, and networking opportunities. Attendees can expect innovation, inspiration, and industry insights.
After:
The event includes talks and panels. There's also time for informal networking between sessions.
Problem: AI has repetition-penalty code causing excessive synonym substitution.
Before:
The protagonist faces many challenges. The main character must overcome obstacles. The central figure eventually triumphs. The hero returns home.
After:
The protagonist faces many challenges but eventually triumphs and returns home.
Problem: LLMs use "from X to Y" constructions where X and Y aren't on a meaningful scale.
Before:
Our journey through the universe has taken us from the singularity of the Big Bang to the grand cosmic web, from the birth and death of stars to the enigmatic dance of dark matter.
After:
The book covers the Big Bang, star formation, and current theories about dark matter.
Problem: LLMs often hide the actor or drop the subject entirely with lines like "No configuration file needed" or "The results are preserved automatically." Rewrite these when active voice makes the sentence clearer and more direct.
Before:
No configuration file needed. The results are preserved automatically.
After:
You do not need a configuration file. The system preserves the results automatically.
Problem: LLMs use em dashes (—) more than humans, mimicking "punchy" sales writing. In practice, most of these can be rewritten more cleanly with commas, periods, or parentheses.
Note for Edition heirs: Cardinal Rule 2 in the heir brain bans em-dashes in shipped prose outright. This pattern is relevant when humanizing third-party text that already contains em-dashes, or when reviewing why heir-authored prose felt AI-flavored before the rule was internalized.
Before:
The term is primarily promoted by Dutch institutions—not by the people themselves. You don't say "Netherlands, Europe" as an address—yet this mislabeling continues—even in official documents.
After:
The term is primarily promoted by Dutch institutions, not by the people themselves. You don't say "Netherlands, Europe" as an address, yet this mislabeling continues in official documents.
Problem: AI chatbots emphasize phrases in boldface mechanically.
Before:
It blends OKRs (Objectives and Key Results), KPIs (Key Performance Indicators), and visual strategy tools such as the Business Model Canvas (BMC) and Balanced Scorecard (BSC).
After:
It blends OKRs, KPIs, and visual strategy tools like the Business Model Canvas and Balanced Scorecard.
Problem: AI outputs lists where items start with bolded headers followed by colons.
Before:
- User Experience: The user experience has been significantly improved with a new interface.
- Performance: Performance has been enhanced through optimized algorithms.
- Security: Security has been strengthened with end-to-end encryption.
After:
The update improves the interface, speeds up load times through optimized algorithms, and adds end-to-end encryption.
Problem: AI chatbots capitalize all main words in headings.
Before:
Strategic Negotiations And Global Partnerships
After:
Strategic negotiations and global partnerships
Problem: AI chatbots often decorate headings or bullet points with emojis.
Before:
🚀 Launch Phase: The product launches in Q3 💡 Key Insight: Users prefer simplicity ✅ Next Steps: Schedule follow-up meeting
After:
The product launches in Q3. User research showed a preference for simplicity. Next step: schedule a follow-up meeting.
Problem: ChatGPT uses curly quotes ("...") instead of straight quotes ("...").
Before:
He said "the project is on track" but others disagreed.
After:
He said "the project is on track" but others disagreed.
Words to watch: I hope this helps, Of course!, Certainly!, You're absolutely right!, Would you like..., let me know, here is a...
Problem: Text meant as chatbot correspondence gets pasted as content.
Before:
Here is an overview of the French Revolution. I hope this helps! Let me know if you'd like me to expand on any section.
After:
The French Revolution began in 1789 when financial crisis and food shortages led to widespread unrest.
Words to watch: as of [date], Up to my last training update, While specific details are limited/scarce..., based on available information...
Problem: AI disclaimers about incomplete information get left in text.
Before:
While specific details about the company's founding are not extensively documented in readily available sources, it appears to have been established sometime in the 1990s.
After:
The company was founded in 1994, according to its registration documents.
Problem: Overly positive, people-pleasing language.
Before:
Great question! You're absolutely right that this is a complex topic. That's an excellent point about the economic factors.
After:
The economic factors you mentioned are relevant here.
Before → After:
Problem: Over-qualifying statements.
Before:
It could potentially possibly be argued that the policy might have some effect on outcomes.
After:
The policy may affect outcomes.
Problem: Vague upbeat endings.
Before:
The future looks bright for the company. Exciting times lie ahead as they continue their journey toward excellence. This represents a major step in the right direction.
After:
The company plans to open two more locations next year.
Words to watch: third-party, cross-functional, client-facing, data-driven, decision-making, well-known, high-quality, real-time, long-term, end-to-end
Problem: AI hyphenates common word pairs with perfect consistency. Humans rarely hyphenate these uniformly, and when they do, it's inconsistent. Less common or technical compound modifiers are fine to hyphenate.
Before:
The cross-functional team delivered a high-quality, data-driven report on our client-facing tools. Their decision-making process was well-known for being thorough and detail-oriented.
After:
The cross functional team delivered a high quality, data driven report on our client facing tools. Their decision making process was known for being thorough and detail oriented.
Phrases to watch: The real question is, at its core, in reality, what really matters, fundamentally, the deeper issue, the heart of the matter
Problem: LLMs use these phrases to pretend they are cutting through noise to some deeper truth, when the sentence that follows usually just restates an ordinary point with extra ceremony.
Before:
The real question is whether teams can adapt. At its core, what really matters is organizational readiness.
After:
The question is whether teams can adapt. That mostly depends on whether the organization is ready to change its habits.
Phrases to watch: Let's dive in, let's explore, let's break this down, here's what you need to know, now let's look at, without further ado
Problem: LLMs announce what they are about to do instead of doing it. This meta-commentary slows the writing down and gives it a tutorial-script feel.
Before:
Let's dive into how caching works in Next.js. Here's what you need to know.
After:
Next.js caches data at multiple layers, including request memoization, the data cache, and the router cache.
Signs to watch: A heading followed by a one-line paragraph that simply restates the heading before the real content begins.
Problem: LLMs often add a generic sentence after a heading as a rhetorical warm-up. It usually adds nothing and makes the prose feel padded.
Before:
## Performance
Speed matters.
When users hit a slow page, they leave.
After:
## Performance
When users hit a slow page, they leave.
Provide:
End-to-end demonstration of the draft → self-audit → final rewrite loop is in examples/full-example.md. Read it once on first invocation to see how all 29 patterns compound in a single piece of AI-flavored prose and how the iterative pass strips them out.
markdown-author agent (.github/agents/markdown-author.agent.md) — always-on prose worker with a 15-word banned-vocabulary filter and 6-step quick audit; humanizer is the deeper on-demand pass when the user explicitly wants AI-tell removal.github/copilot-instructions.md — em-dashes banned outright; Pattern 14 above documents the underlying reasonmarkdown-author's always-on prose discipline). Sink to Mall rather than removing entirely so heirs who write a lot of public-facing prose can install on demand.humanizer is invoked but consistently overrides heir voice in ways the heir reverts ≥3 times, the Voice Calibration section is failing — either tighten the calibration discipline or rebalance toward voice-preserving rewrites.This skill is adapted from Hermes Agent's port of blader/humanizer (MIT licensed), which is itself based on Wikipedia: Signs of AI writing, maintained by WikiProject AI Cleanup. The patterns documented there come from observations of thousands of instances of AI-generated text on Wikipedia.
Original author: Siqi Chen (@blader). Source upstream: https://github.com/blader/humanizer (version 2.5.1). The 29 patterns, personality/soul section, and full worked example are preserved verbatim from the source. Adapted for Edition with neutral tool references (workspace read/edit) replacing Hermes-native tool names (read_file, patch, write_file), composition notes with markdown-author agent and Cardinal Rule 2, and ACT-shape frontmatter + ## Would Revise If falsifier. Original MIT license preserved upstream.
Key insight from Wikipedia: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."