Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/olelehmann1337/workshop-plugin/antislop-workshop-v2npx skills add olelehmann1337/workshop-plugin --skill antislop-workshop-v2git clone --depth 1 https://github.com/olelehmann1337/workshop-pluginWrote 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/olelehmann1337/workshop-plugin/antislop-workshop-v2)<a href="https://agentmods.dev/skills/olelehmann1337/workshop-plugin/antislop-workshop-v2"><img src="https://agentmods.dev/badge/skills/olelehmann1337/workshop-plugin/antislop-workshop-v2.svg" alt="Measured on agentmods" 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 | $0.00116 | $0.02516 |
| Opus 5 | $0.00058 | $0.01258 |
| Sonnet 5 | $0.00023 | $0.00503 |
| Haiku 4.5 | $0.00012 | $0.00252 |
Grade A, and why
antislop-workshop-v2 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anti-Slop
People can spot AI writing in about two seconds. Not because they're experts — because the patterns are that predictable. Same sentence structures, same filler phrases, same fake enthusiasm. Every AI tool produces the same voice.
This skill runs content through 5 editing passes that catch every common AI pattern and replace them with something a human would actually write. Not just "don't do X" — specific fixes for each problem.
the core job
Take any AI-generated or AI-assisted content and strip the patterns that make it sound robotic.
Output: Cleaned version of the content + a changelog of what was fixed, grouped by pass.
before starting: gather context
STOP. Do not edit until you have the content and know the context. Ask for any missing field before proceeding.
- The content — What are you cleaning? (paste it or point to a file)
- The tone — What voice are you going for? (casual, professional, technical, conversational)
- The format — What type of content is this? (email, social post, landing page, article, newsletter)
Tone matters because some fixes depend on context. An em dash in a casual LinkedIn post is different from one in a technical doc. A rhetorical question in a sales page may stay. In a technical explainer it goes.
the process
Run content through these 5 passes in order. Each pass catches a different category of AI pattern.
STRUCTURE → PHRASES → VERBS → FORMATTING → CONTENT
Don't try to fix everything at once. One pass at a time.
Full detail for every pass — scan patterns, before/after examples, and specific fixes — lives in references/passes.md. Read it before editing.
Pass 1: Structure
Catches repeated sentence structures: binary opposites ("it's not X, it's Y"), forced threes, infomercial questions ("Want to know the secret?"), "No X. No Y. Just Z." openers.
Pass 2: Phrases
Catches filler phrases: robot buzzwords (game-changer, supercharge, leverage), throat-clearing hedges, thesaurus syndrome (utilize → use), melodramatic realizations, "Enter: [Thing]," "If you're serious about," "To your success," "Not because of X. But because of Y."
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 246 lines · 116 tokens per session scan A 28f9f8545aeb
antislop-workshop-v2 is a skill published in the GitHub repository olelehmann1337/workshop-plugin (7 stars, last pushed 4mo ago), licensed MIT. It adds 116 tokens to every session and 2,516 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…