codex/skills/de-slopify/SKILL.md
Remove telltale signs of AI-generated "slop" writing from documentation. Use when polishing README files, API docs, or any public-facing text to sound authentically human.
npx skillsauth add tkersey/dotfiles de-slopifyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Core Insight: You can't do this with regex or a script. It requires manual, systematic review of each line.
I want you to read through the complete text carefully and look for any telltale
signs of "AI slop" style writing; one big tell is the use of emdash. You should
try to replace this with a semicolon, a comma, or just recast the sentence
accordingly so it sounds good while avoiding emdash.
Also, you want to avoid certain telltale writing tropes, like sentences of the
form "It's not [just] XYZ, it's ABC" or "Here's why" or "Here's why it matters:".
Basically, anything that sounds like the kind of thing an LLM would write
disproportionately more commonly than a human writer and which sounds
inauthentic/cringe.
And you can't do this sort of thing using regex or a script, you MUST manually
read each line of the text and revise it manually in a systematic, methodical,
diligent way. Use ultrathink.
Review this text and remove any AI slop patterns: excessive emdashes, "Here's why"
constructions, "It's not X, it's Y" formulas, and other LLM writing tells. Recast
sentences to sound more naturally human. Use ultrathink.
| Pattern | Problem | |---------|---------| | Emdash overuse | LLMs love emdashes—they use them constantly—even when other punctuation works better | | "It's not X, it's Y" | Formulaic contrast structure | | "Here's why" | Clickbait-style lead-in | | "Let's dive in" | Forced enthusiasm | | "At its core..." | Pseudo-profound opener | | "It's worth noting..." | Unnecessary hedge |
| Original | Alternative |
|----------|-------------|
| X—Y—Z | X; Y; Z or X, Y, Z |
| The tool—which is powerful—works | The tool, which is powerful, works |
| We built this—and it works | We built this, and it works |
Sometimes the best fix is to split into two sentences.
Before:
This tool—which we built from scratch—handles everything automatically—from parsing to output.
After:
This tool handles everything automatically, from parsing to output. We built it from scratch.
Before:
We chose Rust for this component. Here's why: performance matters.
After:
We chose Rust for this component because performance matters.
Before:
It's not just a linter—it's a complete code quality system.
After:
This complete code quality system goes beyond basic linting.
Before:
# Getting Started
Let's dive in! We're excited to help you get up and running.
After:
# Getting Started
Install the tool and run your first command in under a minute.
| Topic | Reference | |-------|-----------| | Complete phrase list | PATTERNS.md |
tools
Invokes Apple's macOS 27 fm command-line tool from a local Mac to use the on-device system model or Private Cloud Compute, including instructions, image prompts, schema-constrained JSON, and noninteractive automation. Use when the user asks to run Apple Foundation Models through fm, compare system versus pcc, generate structured output, or automate fm without Swift or an app.
development
Compile historical Codex sessions into governed counterfactual evidence, evaluate an existing owner-applied candidate through blinded paired HCTP trials, and fold observable evidence into RUN, OBSERVE, or STOP. Use for `$hylo`, CRF extraction, counterfactual replay, source-governed direct or historical trials, sealed evidence, paired baseline/candidate evaluation, causal frontiers, or evidence-governed improvement.
testing
Ensure a `ledger` command is available on PATH; materialize, validate, record, replay, and project requested Actuating artifacts without taking semantic or execution authority; coordinate the shared Learnings/Synesthesia/Negative Ledger lifecycle checkpoint and repo-local source-memory reconciliation; address Universalist plans and receipts; and perform pure artifact validation.
testing
Classify and quotient review findings, failing tests, incidents, bug reports, migration failures, and other witnessed falsifiers against accepted intent and the current Construction. Author counterexample-set/v1 without selecting repairs, counting review credit, or granting mutation.