---
title: "AI Writing Agent Skills: Make AI Sound Human"
canonical: https://wavect.io/blog/ai-writing-agent-skills/
language: en
description: "Make AI writing sound human with reusable Agent Skills. Compare No AI Slop, i-have-adhd and book-to-skill, plus an evaluation and rollout plan."
image: "https://wavect.io/img/blog/headers/header_ai-writing-agent-skills.png"
---

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[![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/)

[Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt

11 min read · 9 Aug 2026 Last reviewed August 9, 2026

[**Next**](/blog/mcp-vs-rag-vs-agent-skills-vs-custom-gpts/)

# How to Make AI Writing Sound Human with Agent Skills

TL;DR

Do not ask a model to “sound human” and hope for consistent results. Separate the workflow into three layers: source knowledge, output structure and editorial quality. No AI Slop is a focused post-editor for recurring synthetic prose patterns. i-have-adhd reshapes assistant responses around action-first, numbered output. book-to-skill converts longer books or document sets into a progressively disclosed knowledge skill. They complement one another, but none can supply original judgment, factual evidence or a brand voice that was never documented. For production, pin reviewed versions, keep source and style rules separate, create representative eval cases, compare against a no-skill baseline and retain human approval for published claims. Install an existing skill for a personal workflow; build a governed writing system when several people, channels, sensitive sources or approval rules are involved.

**Do not give an AI one vague instruction to “sound human.” Give it a small, testable writing system.** Separate source knowledge, response structure and editorial quality. Then measure whether each layer improves the work. That is the practical lesson behind three fast-growing open-source projects: No AI Slop, i-have-adhd and book-to-skill.

They are often presented as three ways to fix the same model. They are not. No AI Slop edits recurring prose patterns. i-have-adhd changes how an assistant structures an answer. book-to-skill turns a large reference set into knowledge the agent loads when relevant. Used together, they can reduce repetitive cleanup. Used without source material, evals or human judgment, they can produce tidy writing that is still empty or wrong.

This article owns the writing-workflow question. If you first need to understand the technology category, our [MCP vs RAG vs Agent Skills vs Custom GPTs decision tree](/blog/mcp-vs-rag-vs-agent-skills-vs-custom-gpts/) explains where skills sit in an AI system.

## The short verdict: which AI writing skill solves what?

| Problem | Best starting point | What it cannot supply |
| --- | --- | --- |
| Drafts use predictable AI phrases and empty transitions | No AI Slop as a final editorial pass | Your ideas, evidence or personal voice |
| Answers bury the action in preamble and tangents | i-have-adhd as an output-structure rule set | Domain knowledge or factual accuracy |
| Your brand book, manual or source library is too large to paste repeatedly | book-to-skill for progressive, on-demand reference loading | A finished editorial policy or reliable extraction by default |
| Several writers, channels and approval rules must stay aligned | A custom, versioned skill plus evals and workflow controls | Accountability for what gets published |

For one person, install the narrowest skill and keep editing. For a company, treat the skill as a versioned product component. Define what it may change, how success is scored, who approves claims and how a bad update is rolled back.

## Why does AI writing sound generic?

Most weak output starts before the sentence is written. The model receives an underspecified task, little first-hand material and no acceptance test. It then optimizes for a plausible average answer. The result is smooth structure, broad claims, repeated rhetorical patterns and conclusions that sound more important than the evidence supports.

A longer prompt is not automatically better. Mixing facts, tone, process, forbidden phrases, audience, formatting and examples into one permanent block makes conflicts harder to see. It also turns every change into prompt surgery. A reusable skill gives those instructions an owner, a version and a place for examples or deterministic checks. The related concept is [prompt engineering](/glossary/prompt-engineering/), but the operational improvement comes from packaging and testing the procedure, not finding one magical sentence.

## Three open-source skills, three different jobs

### 1. No AI Slop: a focused post-editor

[No AI Slop](https://github.com/petergyang/no-ai-slop) documents more than 20 recurring patterns, including throat-clearing openings, fake-insight setups, dramatic fragments, vague attribution and inflated conclusions. It can edit a draft or identify matching lines without claiming to detect who or what wrote the text. The repository includes the skill instructions and an evaluation file, which makes its editorial rules inspectable.

That narrow scope is its strength. Run it after the factual draft exists. Do not let a style pass invent proof, strip necessary qualifications or force every author into one cadence. “Human” is not a style preset. The useful goal is specific writing whose source, argument and voice remain intact.

### 2. i-have-adhd: action-first response structure

[i-have-adhd](https://github.com/ayghri/i-have-adhd) tells a coding assistant to lead with the next action, number multi-step work, suppress tangents, use concrete time estimates and remove ceremonial openings or closers. Its own documentation says no diagnosis is needed. The skill is an output style for reducing the distance between reading and acting, not a medical tool.

This distinction matters for search and procurement. It does not clone a brand voice or improve the factual basis of an article. It makes operational answers easier to scan and execute. That can be valuable in support, runbooks and internal delivery, but it can be too rigid for essays, sensitive messages or explanations that need nuance. Apply it by task, not as an unquestioned rule for every channel.

### 3. book-to-skill: turn reference material into on-demand knowledge

[book-to-skill](https://github.com/virgiliojr94/book-to-skill) accepts a book, document folder or collection of sources and generates a skill with a core index plus chapter, glossary, pattern and cheat-sheet files. The agent can load the relevant section instead of receiving the whole source on every request. The project explicitly lists brand and design systems as one use case.

This solves a knowledge-packaging problem, not an editorial-quality problem. A generated skill inherits omissions, contradictions and licensing constraints from its inputs. Review the output against the original documents, keep provenance, and do not turn material you cannot lawfully reuse into a shared company asset. For a brand system, start from approved examples, not every text the company has ever published.

## How should the three layers work together?

1. **Ground the task.** Supply the product facts, approved claims, audience, channel and desired reader action.
2. **Load only relevant knowledge.** Use a small reference or a progressively disclosed skill for the applicable product, market and voice rules.
3. **Shape the response.** Apply concise, action-first structure when the task needs it. Do not impose support-answer formatting on a long-form article.
4. **Edit the draft.** Run a focused slop and clarity pass after the argument exists, with an explicit rule to preserve facts, qualifications and author voice.
5. **Verify and approve.** Check every consequential claim against its source, score the output and keep a person responsible for publication.

The sequence matters. Style cleanup before grounding can make unsupported copy sound more credible. Grounding without editorial review can leave correct information unreadable. One skill can call or compose with another, but each should own a coherent responsibility.

## How do you build a company writing skill?

The open [Agent Skills specification](https://agentskills.io/specification) defines a skill as a directory with a required `SKILL.md` file and optional scripts, references and assets. Its progressive-disclosure model loads basic metadata first, full instructions on activation and supporting files only when needed. That structure is a good fit for a writing system because not every task needs every product sheet, legal rule or channel template.

1. **Choose one bounded job.** Start with “edit an evidence-backed B2B article to our house style,” not “write everything for marketing.”
2. **Collect good and bad examples.** Use approved source text, explain why each example passes or fails, and remove confidential customer material.
3. **Separate invariants from preferences.** Claims need evidence and legal wording must stay intact. Sentence rhythm and contractions can vary by channel.
4. **Write the smallest useful procedure.** The official [skill creator guidance](https://agentskills.io/skill-creation/best-practices) recommends coherent scope, moderate detail and progressive disclosure instead of an exhaustive master prompt.
5. **Bundle deterministic checks.** A script can catch banned phrases, missing citations, title length, broken links or unresolved placeholders more reliably than another prose instruction.

## How do you evaluate whether the writing skill works?

Build a test set before tuning the skill. Include short support answers, technical explanations, product pages, executive summaries, weak source material and cases where the correct behavior is to ask for evidence. Run each case with and without the skill on the same model snapshot.

| Metric | How to score it | Failure signal |
| --- | --- | --- |
| Factual preservation | Every number, name and qualification matches the approved source | A smoother draft changes meaning |
| Specificity | Claims name an actor, action, object or measurable condition | Abstract benefits replace useful detail |
| Voice retention | Reviewers can identify the intended author or brand from blinded samples | Every channel acquires the same generic cadence |
| Actionability | The reader can identify the next decision or action without rereading | The point appears after a long preamble |
| Review cost | Median minutes and material edits needed for approval | Clean-looking output still requires full reconstruction |

The official [Agent Skills evaluation guide](https://agentskills.io/skill-creation/evaluating-skills) recommends learning from failed assertions, human feedback and execution transcripts. That is the right loop for writing too. If the agent ignores a rule, clarify it. If it follows the rule and quality falls, remove or narrow the rule. A larger instruction file is not a success metric.

## What are the production risks?

- **Instruction supply chain.** A mutable repository can change after review. Pin a commit or release, inspect scripts and retest upgrades.
- **False confidence.** Natural prose can make a weak claim look trustworthy. Keep citation and approval gates outside the style pass.
- **Voice flattening.** A universal list of forbidden patterns can erase intentional rhythm, humor or cultural differences. Localize judgment, not only words.
- **Source leakage.** Brand books, customer examples and internal documents may contain confidential or personal data. Control what enters model context.
- **Rule collisions.** Multiple active skills can disagree about length, tone or formatting. Define precedence and test the combined stack.

A production workflow needs version control, a model and prompt change log, source permissions, evals, approval roles and rollback. Our [AI agent SLA template](/blog/ai-agent-sla-template/) shows how to make those operating boundaries measurable.

## Install a writing skill or build a custom system?

| Situation | Recommendation |
| --- | --- |
| One person edits low-risk drafts | Install one reviewed skill and keep the final human pass |
| A small team shares a stable style guide | Fork and version a focused skill, then add 20 to 30 representative evals |
| Several channels use different legal, product and tone rules | Build a routed skill set with channel-specific references and approval gates |
| Content uses customer data or regulated claims | Design the data and evidence boundary before automating prose |
| Publishing volume justifies integration | Connect the evaluated skill to the CMS through a controlled workflow, not an unrestricted publishing agent |

The commercial decision is not “prompt versus agency.” It is whether the cost of repeated manual correction now exceeds the cost of owning a tested content system. Wavect's [AI enablement team](/services/ai-enablement/) can turn an approved editorial process into a versioned agent workflow. The [Twinsoft AI case study](/case-studies/twinsoft-ai/) shows the delivery discipline behind a production AI product, while our [prototype-to-production guide](/software-development-guide/vibe-coded-prototype-to-production/) helps scope the engineering gap. For a concrete architecture and pilot estimate, [book an AI workflow review](/contact/).

## Frequently Asked Questions

### Can AI writing really sound human?

It can become more specific, natural and consistent, but no skill creates lived experience or original judgment. The strongest workflow starts from human ideas and approved evidence, uses AI for bounded drafting or editing, and keeps a responsible human final pass.

### What is AI slop?

AI slop is low-value generated content that looks polished but relies on repetitive rhetorical patterns, vague claims, superficial analysis or inflated conclusions. A pattern checker can help, but factual value and editorial judgment matter more than passing a style checklist.

### Is No AI Slop an AI detector?

No. It can identify documented prose patterns without deciding whether a human or model wrote the text. That is more defensible than treating a writing pattern as proof of authorship.

### Can I turn our brand guidelines into an Agent Skill?

Yes. Keep the core procedure short, place product and channel references in focused files, include approved examples, and add eval cases. Review licensing, privacy and factual provenance before converting existing documents.

### How much does a custom AI writing workflow cost?

Cost depends on channels, source systems, privacy, model routing, evaluation and approval integration. A bounded pilot is cheaper than a publishing platform. Scope the first phase around one content type and measurable reviewer-time or quality improvement.

## Final thoughts

The useful idea in these viral skills is not that one file can make a model human. It is that repeated editorial judgment can be made visible, versioned and tested. No AI Slop cleans recurring prose habits. i-have-adhd makes operational answers easier to act on. book-to-skill packages larger reference sets for on-demand use.

Keep those responsibilities separate. Ground before editing, measure against a baseline, preserve human approval and build only as much system as the publishing risk requires. The result should not merely sound less like AI. It should be more specific, more faithful to its sources and cheaper to review.

## You may also like..

[**MCP vs RAG vs Agent Skills vs Custom GPTs** Place writing skills in the wider AI architecture and choose the right layer for knowledge, tools and procedure.](/blog/mcp-vs-rag-vs-agent-skills-vs-custom-gpts/) [**AI enablement vs generic AI consulting** Compare a governed working system with a strategy-only engagement before choosing an implementation partner.](/compare/ai-enablement-vs-generic-ai-consultancy/)

Agent engineering

## Continue through this cluster

[Start with the cornerstone**Graph Engineering for AI Agents: When Does a Knowledge Graph Pay Off?**](/blog/graph-engineering-ai-agents/)

- [NVIDIA NOOA Review: Are Object-Oriented Agents Production-Ready?](/blog/nvidia-nooa-object-oriented-agents-review/)
- [Strix AI Pentesting: 30-Day Pilot and Buying Guide for 2026](/blog/strix-ai-pentesting-pilot-guide-2026/)
- [Hark Handoff Review: The Agent That Actually Clicks](/blog/hark-handoff-computer-use-agent-review/)
- [Meta Muse Code Pricing: Is the Contributor Tier Safe for Client Code?](/blog/meta-muse-code-pricing-contributor-tier/)
- [PII Redaction Before LLM Prompts: A Practical Pipeline](/blog/pii-redaction-before-llm-prompts/)

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11 min read · 9 Aug 2026 Last reviewed August 9, 2026

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Install an existing skill for a personal workflow; build a governed writing system when several people, channels, sensitive sources or approval rules are involved. Do not give an AI one vague instruction to “sound human.” Give it a small, testable writing system. Separate source knowledge, response structure and editorial quality. Then measure whether each layer improves the work. That is the practical lesson behind three fast-growing open-source projects: No AI Slop, i-have-adhd and book-to-skill. They are often presented as three ways to fix the same model. They are not. No AI Slop edits recurring prose patterns. i-have-adhd changes how an assistant structures an answer. book-to-skill turns a large reference set into knowledge the agent loads when relevant. Used together, they can reduce repetitive cleanup. Used without source material, evals or human judgment, they can produce tidy writing that is still empty or wrong. This article owns the writing-workflow question. If you first need to understand the technology category, our MCP vs RAG vs Agent Skills vs Custom GPTs decision tree explains where skills sit in an AI system. The short verdict: which AI writing skill solves what? ProblemBest starting pointWhat it cannot supply Drafts use predictable AI phrases and empty transitionsNo AI Slop as a final editorial passYour ideas, evidence or personal voice Answers bury the action in preamble and tangentsi-have-adhd as an output-structure rule setDomain knowledge or factual accuracy Your brand book, manual or source library is too large to paste repeatedlybook-to-skill for progressive, on-demand reference loadingA finished editorial policy or reliable extraction by default Several writers, channels and approval rules must stay alignedA custom, versioned skill plus evals and workflow controlsAccountability for what gets published For one person, install the narrowest skill and keep editing. For a company, treat the skill as a versioned product component. Define what it may change, how success is scored, who approves claims and how a bad update is rolled back. Why does AI writing sound generic? Most weak output starts before the sentence is written. The model receives an underspecified task, little first-hand material and no acceptance test. It then optimizes for a plausible average answer. The result is smooth structure, broad claims, repeated rhetorical patterns and conclusions that sound more important than the evidence supports. A longer prompt is not automatically better. Mixing facts, tone, process, forbidden phrases, audience, formatting and examples into one permanent block makes conflicts harder to see. It also turns every change into prompt surgery. A reusable skill gives those instructions an owner, a version and a place for examples or deterministic checks. The related concept is prompt engineering, but the operational improvement comes from packaging and testing the procedure, not finding one magical sentence. Three open-source skills, three different jobs 1. No AI Slop: a focused post-editor No AI Slop documents more than 20 recurring patterns, including throat-clearing openings, fake-insight setups, dramatic fragments, vague attribution and inflated conclusions. It can edit a draft or identify matching lines without claiming to detect who or what wrote the text. The repository includes the skill instructions and an evaluation file, which makes its editorial rules inspectable. That narrow scope is its strength. Run it after the factual draft exists. Do not let a style pass invent proof, strip necessary qualifications or force every author into one cadence. “Human” is not a style preset. The useful goal is specific writing whose source, argument and voice remain intact. 2. i-have-adhd: action-first response structure i-have-adhd tells a coding assistant to lead with the next action, number multi-step work, suppress tangents, use concrete time estimates and remove ceremonial openings or closers. Its own documentation says no diagnosis is needed. The skill is an output style for reducing the distance between reading and acting, not a medical tool. This",
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      "name": "Can I turn our brand guidelines into an Agent Skill?"
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