anti-prose-slop
Stop AI agents from shipping generic AI prose when writing books with Proseify.
- Installs
- 53.2K
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://proseify.xyz --skill anti-prose-slop- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads anti-prose-slop automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Stop AI writing agents from shipping generic prose.
Stop Making Prose Slop
Write distinctive, publishable longform with the Proseify MCP — a curated public-domain corpus plus a guided drafting pipeline.
Prerequisite: connect the Proseify MCP first. No key, no tools.
{
"mcpServers": {
"proseify": {
"type": "http",
"url": "https://mcp.proseify.xyz/mcp",
"headers": { "Authorization": "Bearer YOUR_KEY" }
}
}
}
Get a key at https://proseify.xyz (sign in → pick a plan → paste your config).
The one rule
Genre before prose. Every generation decision after the first sentence
depends on picking the right tradition. A gothic ghost story and a Regency
romance want different sentence lengths, different climates, different endings.
Call get_genre_recipe before writing; don't use plan_book to skip it.
The workflow
- Agree the length FIRST. Before planning anything, ask the user one
question: "Full novel (~70–90k words) or novella (~20–30k)?" Then pass
explicit arguments —
plan_book("...", genre, target_chapters=40, target_words=80000)for a novel, or omit for the default (30 chapters ≈ 48k words). Never accept the bare default without confirming: agents that "just go" produce novellas when the customer asked for a book, and stop mid-book to ask about tools instead of finishing the manuscript. - Identify the genre. Don't guess. Cross-check against
list_genresandget_genre_recipe(genre)before writing. Mislabeled genre = mis-voiced book. - Read the recipe. It returns pacing beats, dialogue ratio, and stylistic anchors from the tradition. Read them all before planning.
- Search the corpus. Use
search_corpusfor 3–5 model passages that match the register you want. Quote a line to yourself; match its rhythm. - Plan before drafting.
plan_bookproduces the architecture. Use it — don't freestyle 40 chapters without a map. - Draft EVERY chapter, in chapter order, without stopping. The common failure: the agent drafts chapters 1–5, summarises the rest, and calls it done. A book isn't done until every planned chapter exists at its full target length. If you have a per-session step budget, write the next chapter at the start of each new turn rather than summarising.
- Self-edit between drafts. Anti-slop pass, every time:
- Cut "seemed to", "began to", "settled into".
- Kill adverb-stacking ("quietly, almost reverently…").
- Delete sentences that only restate the previous one.
- Check chapter openings — vary them. Never six chapters in a row that open with weather.
- Read the last page out loud; if it sounds like a summary, rewrite it.
- Verify with
evaluate_book. If the score is below your own bar, don't ship it — revise the weakest chapters and re-evaluate.
Anti-slop checklist (per chapter)
- Voice matches the recipe, not "ChatGPT normal".
- Dialogue keeps its register (era, class, region).
- Scene boundaries do work — each has a job, not just a location change.
- No repeated sentence-opening constructions within three paragraphs.
- Concrete nouns beat abstractions. "The bell rang" beats "A sound occurred."
Traps the corpus protects against
- The thesaurus trap. Don't swap synonyms for variety; swap for precision.
- The cliffhanger trap. One per book, not one per chapter.
- The filter-words trap. "She felt / saw / heard / noticed" — cut them.
- The dialogue-as-exposition trap. Nobody explains the plot over tea.
Pitfalls
plan_bookresult is large. Don't call it repeatedly for the same book; call once, hold the outline in context, work chapter by chapter.- Default is 30 chapters ≈ 48k words (novella-length). A full novel needs
explicit
target_chapters=40, target_words=80000(cap is 40 chapters). Confirm this with the user before planning (step 1). - The summarise trap: the #1 way agents "finish" a book is writing chapters
1–5 fully and compressing the rest into one-line summaries.
evaluate_bookcatches thin chapters — run it before declaring done, and re-draft any chapter under its per-chapter target. - Long agent sessions get cut off. Persist progress: keep the assembled
manuscript in a file (
assemble_bookmarkdown) and append chapter by chapter so an interrupted session resumes instead of restarting. search_corpussupports FTS5 syntax (quotes, AND/OR/NOT,*wildcards) but long operator chains are clamped to 10 terms.
Licensing
Books sourced from Project Gutenberg and similar public-domain corpora; safe commercial use. See the corpus page on https://proseify.xyz for per-book details.
FAQ
What does the anti-prose-slop skill do?
Stop AI agents from shipping generic AI prose when writing books with Proseify.
How do I install anti-prose-slop?
Run npx skills add https://proseify.xyz --skill anti-prose-slop in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/anti-prose-slop/ (personal) or .claude/skills/anti-prose-slop/ (project).
Which AI agents can use anti-prose-slop?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is anti-prose-slop safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains anti-prose-slop?
It is published by proseify.xyz in proseify.xyz. It has 53,172 recorded installs.
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available