zero-slop

zero-slop is a skill for Claude Code from manavmishra/ZeroSlop. It costs 125 tokens per session (13,642 once invoked), scanned A, a copy of zero-slop, MIT.

An accessibility review skill for one Figma component or component set. Accessibility means making an interface usable by people with disabilities, including those using keyboards or screen readers.

In plain words
What is it for?
Use it to assess a single interactive component's accessibility, compare its states, simulate common color-vision differences, and prioritize fixes.
Why use it?
It checks interaction states and visual details that are easy to miss in a general design review, such as focus indicators, contrast, non-color cues, target size, and missing annotations.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents; positional $N argument.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 scripts/learn.py --reflect --produced out.md --shipped final.md \.

Part of the zero-slop plugin — 2 skills, 1 MCP server shipped together

Good fit Use it to assess a single interactive component's accessibility, compare its states, simulate common color-vision differences, and prioritize fixes.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/manavmishra/ZeroSlop
agentmods
npx agentmods add skills/manavmishra/zeroslop/zero-slop

Made for: Claude Code.

Or install zero-slop, the plugin that ships this one along with the rest of its 2 skills, 1 MCP server.

Wrote 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.

agentmods badge for zero-slop

README.md
[![agentmods](https://agentmods.dev/badge/skills/manavmishra/zeroslop/zero-slop/github.svg)](https://agentmods.dev/skills/manavmishra/zeroslop/zero-slop)
Your own site
<a href="https://agentmods.dev/skills/manavmishra/zeroslop/zero-slop"><img src="https://agentmods.dev/badge/skills/manavmishra/zeroslop/zero-slop/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.

agentmods 80×15 button for zero-slop

Your own site · 80×15
<a href="https://agentmods.dev/skills/manavmishra/zeroslop/zero-slop"><img src="https://agentmods.dev/badge/skills/manavmishra/zeroslop/zero-slop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 13,642 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00125 $0.13642
Opus 5 $0.00063 $0.06821
Sonnet 5 $0.00025 $0.02728
Haiku 4.5 $0.00013 $0.01364

Measured today against content hash 110b520c1d93, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

zero-slop 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 today.

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.

Origin

This is a copy

100% identical to zero-slop — 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.

skills/zero-slop/SKILL.md · 1,020 lines

How it starts

The opening of the file, as written. The whole thing — 1,020 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Zero Slop

A linter for the AI accent. The things that make prose read as machine-written are measurable, so measure them, fix them, and show the numbers.

Zero Slop is a skill, not an AI model. The user's existing AI assistant, powered by Claude, GPT, or another compatible model, reads the draft, understands its context, and performs the editorial work. The bundled local tools handle repeatable checks. They do not replace the assistant, and no separate Zero Slop model or service receives the draft.

The separately invoked npm zero-slop deslop command and hosted MCP/REST endpoints send a draft to Zero Slop's remote service. They are opt-in alternatives, not local checks in this workflow. Do not invoke them as part of an offline skill run without the user's request. The npm score command continues to run locally.

The science in one paragraph: detectors (and readers) key on the post-training register — text that sits at the most-probable phrasing, with uniform sentence rhythm, a few hundred over-represented style words, tidy template structure, and relentless even polish. These signals live in the surface realization of the text and can usually be revised without changing the meaning; the fidelity and semantic checks below enforce that boundary. references/evidence.md has the citations, and the ladder below orders the signals by measured strength.

Hard rules (non-negotiable)

  1. Fidelity. Meaning, claims, and facts survive exactly. Never invent a number, name, anecdote, or experience — and experiential/interior claims count ("by test day it felt familiar", "I was terrified"): if the author didn't say it, it's fabrication, even when it would make the piece land better. Preserve the underlying emotion or position when the author states one. A generic promotional intensifier may be reduced only when it is a named delivery defect and the underlying claim remains ("incredibly excited" may become "excited"). A hedge, scope limit, caveat, factual degree, or change of speaker is not promotional padding and must keep its strength. Specificity without source grounding is fabrication — worse than the slop it replaces.
  2. Flag hollow spans, don't fill them. Prose that makes no claim cannot be rescued by rewording. Flag it and ask for the missing substance.
  3. No over-correction. Trading AI-slop for edgy-slop (forced hot takes, fake first person, performed candor, staccato drama) is failure. Read references/overcorrection.md before heavy rewrites.
  4. Idempotence. Text that already reads human returns unchanged. "Reads human" is a two-channel finding, never a score: a draft returns unchanged only after the scorer is clean and the step 2 performed-register pass has run on it and reported zero findings. The best edit is often small.
  5. Honest use. This skill improves writing quality and voice. Refuse requests to defeat AI-disclosure requirements (schools, journals, employers that require disclosure) or to impersonate a named individual.
  6. Speak to the writer, not the scoring code. User-facing reports must use ordinary editorial language. Say "writing score," "flagged phrases," "sentence variety," "readability," "facts preserved," and "final checks." Never expose internal labels such as "surface score," "weighted tells," "tell density," "burstiness," "followability," "fidelity gate," "scorecard," "heatmap," "artifact," "candidate," or "overlay." Keep internal field names only in machine-readable JSON or maintainer notes.
  7. Tell the writer who did what. Zero Slop is the skill and set of local tools; the AI assistant running it performs the contextual reading and editing. In every standalone report, name the current assistant or model only when the environment makes that identity certain. Say "Claude," "GPT," or the accurate product name when known; otherwise say "your AI assistant." Never guess. Do not imply that a separate Zero Slop model or service received, read, or rewrote the draft.
  8. A clean score is not a completed review. The scorer sees only the lexically anchored subset of the tells. Every draft gets the performed-register pass in step 2 regardless of what the meter says, and that pass reports its counts — including zero — in the step 9 summary. A score in the "clear" band is a reason to look harder at register, not permission to stop: the tell families the meter cannot see are exactly the ones still standing when it comes back empty.

Read the full file on GitHub · 1,020 lines

Files

What ships with it

60 files 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.

Changes

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.

  1. today Changed 110b520c1d93
  2. yesterday Changed · +5 lines 5b2248d1af8b
  3. 3d ago Changed · +15 lines f44a8b7d7cfe
  4. 4d ago Changed · +14 lines f173654982a1
  5. 5d ago Changed · +21 lines 4ea685fd5f53
  6. 9d ago First seen · 965 lines · 125 tokens per session scan A 885edc0f14d4

Subscribe to this mod's changes

zero-slop is a skill published in the GitHub repository manavmishra/ZeroSlop (114 stars, last pushed today), licensed MIT. It adds 125 tokens to every session and 13,642 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to zero-slop, differing in 0 lines, and is treated as a copy.

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