Borrowing it
Nothing to install: this file belongs to Sma1lboy/rove. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Sma1lboy/rove/main/.agents/skills/pstack/skills/unslop/SKILL.mdgit clone --depth 1 https://github.com/Sma1lboy/roveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sma1lboy/rove/unslop)<a href="https://agentmods.dev/skills/sma1lboy/rove/unslop"><img src="https://agentmods.dev/badge/skills/sma1lboy/rove/unslop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sma1lboy/rove/unslop"><img src="https://agentmods.dev/badge/skills/sma1lboy/rove/unslop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.01619 |
| Opus 5 | $0.00007 | $0.00809 |
| Sonnet 5 | $0.00003 | $0.00324 |
| Haiku 4.5 | $0.00001 | $0.00162 |
Grade A, and why
unslop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to unslop — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unslop
Edit text to remove AI patterns and add human voice.
Process
- Scan for the patterns below.
- Rewrite. Preserve meaning, match intended tone.
- Add soul (see next section).
- Self-audit: "What makes this obviously AI generated?" Fix remaining tells.
Adding soul
Removing patterns is half the job. Sterile, voiceless writing is just as obvious.
- Have opinions. React to facts instead of neutrally listing pros and cons.
- Vary rhythm. Short sentences. Then longer ones that take their time. Mix it up.
- Acknowledge complexity. "Impressive but also kind of unsettling" beats "impressive."
- Use "I" when it fits. First person isn't unprofessional.
- Let some mess in. Perfect structure looks machine-made.
- Be specific. Not "this is concerning" but "there's something unsettling about agents churning away at 3am."
Patterns to detect and fix
Content
- Puffery. "pivotal moment", "testament to", "evolving landscape", "setting the stage for", "indelible mark", "deeply rooted". Cut puffery, state what happened.
- Name-dropping. Listing media outlets without context. Pick one, say what was said.
- Superficial -ing phrases. "highlighting...", "ensuring...", "reflecting...", "showcasing...", "fostering...". Delete or expand with real sources.
- Promotional language. "nestled", "vibrant", "breathtaking", "groundbreaking", "renowned", "stunning", "must-visit". Use neutral descriptions.
- Vague attributions. "Experts believe", "Industry reports suggest", "Some critics argue". Name the source or delete.
- Formulaic challenges. "Despite challenges... continues to thrive." Replace with specific facts.
Language
- AI vocabulary. Additionally, crucial, delve, enduring, enhance, fostering, garner, interplay, intricate, landscape (abstract), pivotal, showcase, tapestry (abstract), testament, underscore, vibrant. Replace with plain words.
- Fancy ways to say "is". "serves as", "stands as", "boasts", "features". Just say "is" or "has".
- "Not just X, but Y." State the point directly instead.
- Rule of three. Forcing ideas into groups of three. Use the natural number.
- Synonym cycling. Protagonist, main character, central figure, hero all in one paragraph. Pick one, repeat it.
- False ranges. "from X to Y" where X and Y aren't on a meaningful scale. List topics directly.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 81 lines · 14 tokens per session scan A 181883e539ca
unslop is a skill published in the GitHub repository Sma1lboy/rove (122 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 1,619 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to unslop, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
Read, create and manipulate PDF files — extract text and tables, merge, split, rotate, reorder and delete pages, read and fill AcroForm fields, add or strip metadata, encrypt and decrypt, and generate new PDFs from HTML or from scratch. Also covers rasterising pages to images so a PDF can actually be looked at, and…
fable-artifact
Design and author structured technical proposals, responsive artifacts, architecture diagrams, Mermaid charts, and interactive components. Use when creating standalone markdown reports, architectural specifications, Mermaid diagrams, or interactive artifact widgets — even if the user does not explicitly say…
fable-cowork
Execute autonomous multi-step cowork sessions with silent tool chaining, outcome-first progress reporting, and strict safety boundary enforcement. Use when executing complex background tasks autonomously, running multi-tool refactoring workflows without conversational noise, or performing deep automated passes — even…
fable-dataviz
Design and generate accessible, cohesive data visualizations, SVG charts, metric cards, and dashboard tiles with theme-adaptive styling and verified viewports. Use when creating SVG charts, rendering metrics plots, designing dashboard visuals, or visualizing performance trends — even if the user does not explicitly…
fable-eval
Evaluate changes to agent prompts, skills, routing policies, and harnesses against reproducible baselines, held-out suites, and regression benchmarks. Use when optimizing agent system prompts, measuring skill triggering accuracy, evaluating routing changes, or running benchmark regressions — even if the user does not…
fable-execute
Implement one accepted, bounded work card with immediate local verification, invariant preservation, and zero scope drift. Use when executing a planned work card, applying a well-defined code change, implementing an isolated function, or performing targeted single-scope edits — even if the user does not explicitly say…