de-slopify
Removes AI writing artifacts from documentation and code. Use when editing LLM-generated prose, reviewing READMEs, polishing docs before publishing, or cleaning up AI-generated code. Use for emdash cleanup, formulaic phrase removal, tone calibration, over-commented code, verbose naming, and AI code smell detection.
What this skill does
# De-Slopify ## Overview De-slopify is a methodology for removing telltale signs of AI-generated content from documentation, prose, and code. LLMs produce statistically regular output with characteristic vocabulary, punctuation habits, and structural patterns that make text and code feel inauthentic. Some patterns appear over 1,000x more frequently in LLM output than human writing. **When to use:** Before publishing READMEs, after AI-assisted writing sessions, during documentation reviews, when reviewing AI-generated code for over-engineering, before committing prose or code that an LLM touched. **When NOT to use:** On code logic or algorithms where correctness matters more than style. On technical specifications where precision outweighs voice. On content that was already human-written and reads naturally. ## Quick Reference | Category | Pattern | Fix | | ----------- | ----------------------------------------------- | ---------------------------------------------------------- | | Punctuation | Emdash overuse | Semicolons, commas, colons, or split into two sentences | | Phrase | "Here's why" / "Here's why it matters" | Explain why directly without the lead-in | | Phrase | "It's not X, it's Y" | "This is Y" or restate the distinction | | Phrase | "Let's dive in" / "Let's get started" | Delete; just start the content | | Phrase | "It's worth noting" / "Keep in mind" | Delete the hedge; state the fact | | Phrase | "At its core" / "In essence" / "Fundamentally" | Delete; say the thing directly | | Vocabulary | "delve", "tapestry", "landscape", "nuanced" | Replace with plain, specific language | | Vocabulary | "revolutionize", "cutting-edge", "game-changer" | Replace with concrete claims or delete | | Structure | Uniform sentence length throughout | Mix short (5-word) and long (20+ word) sentences | | Structure | Perfectly balanced lists of exactly 3 items | Vary list length; humans use 2, 4, or odd counts | | Structure | Generic claims without specifics | Add names, dates, numbers, or first-person detail | | Sycophancy | "Great question!" / "Absolutely!" | Delete; answer the question directly | | Meta | "Let me break this down..." / "Let me explain" | Delete the preamble; just break it down | | Structure | Numbered lists where a sentence suffices | Use a sentence; reserve lists for genuinely parallel items | | Closer | "In conclusion" / "To summarize" | Delete or replace with a specific takeaway | | Code | Over-commented trivial functions | Remove comments that restate the code | | Code | Unnecessary abstractions and design patterns | Flatten to the simplest working solution | | Code | Verbose or overly descriptive variable names | Use domain-appropriate concise names | | Code | Defensive error handling on every operation | Handle errors only where failure is realistic | ## Common Mistakes | Mistake | Correct Pattern | | ---------------------------------------------- | ------------------------------------------------------------------------------------------ | | Replacing every emdash mechanically | Evaluate context; sometimes an emdash is the right choice | | Editing code blocks for style | Focus on prose; leave code examples and technical syntax untouched | | Removing all structure to sound casual | Keep headers, tables, and lists intact; rewrite prose only | | Over-correcting into choppy fragments | Read aloud after editing; recombine sentences that lost flow | | Applying fixes without defining target voice | Set persona, tone, and audience before starting edits | | Running regex replacements instead of reading | Manual line-by-line review is required; context determines fixes | | Ignoring AI code smells | Review AI-generated code for over-engineering, verbose names, and unnecessary abstractions | | Removing all LLM-typical words unconditionally | Some flagged words are perfectly natural in context; use judgment | ## Delegation - **Scan a repository for documentation files that need de-slopifying**: Use `Explore` agent - **Rewrite an entire documentation site to remove AI artifacts**: Use `Task` agent - **Plan a documentation voice guide and editorial workflow**: Use `Plan` agent - **Review AI-generated code for slop patterns**: Use `code-reviewer` agent > For systematic quality auditing across 12 dimensions (architecture, security, testing, performance, etc.), use the `quality-auditor` skill. ## References - [Prose patterns: emdash alternatives, phrase replacements, and voice calibration](references/prose-patterns.md) - [Before-and-after examples of common AI writing fixes](references/before-and-after.md) - [AI slop vocabulary: words and phrases that signal LLM authorship](references/slop-vocabulary.md) - [Code slop: detecting and fixing AI-generated code smells](references/code-slop.md) - [Review workflow: prompts, checklists, and integration](references/review-workflow.md)
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.