cheat-on-content

cheat-on-content is a skill for Claude Code from Dirtytrii/codex-skills. It costs 77 tokens per session (4,335 once invoked), scanned A, original, no licence file.

A workflow for scoring, predicting, reviewing, and improving judgments about measurable content such as videos, articles, and podcasts.

In plain words
What is it for?
Initialising the system, scoring content, making blind predictions, reviewing published results, finding topics, tracking trends, and checking status.
Why use it?
It creates a repeatable way to compare content decisions with later results and refine the scoring rules.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Initialising the system, scoring content, making blind predictions, reviewing published results, finding topics, tracking trends, and checking status.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dirtytrii/codex-skills/cheat-on-content
Install

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.

Any agent
npx skills add Dirtytrii/codex-skills --skill cheat-on-content
Clone the repo
git clone --depth 1 https://github.com/Dirtytrii/codex-skills

Made for: Claude Code.

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 cheat-on-content

README.md
[![agentmods](https://agentmods.dev/badge/skills/dirtytrii/codex-skills/cheat-on-content.svg)](https://agentmods.dev/skills/dirtytrii/codex-skills/cheat-on-content)
Your own site
<a href="https://agentmods.dev/skills/dirtytrii/codex-skills/cheat-on-content"><img src="https://agentmods.dev/badge/skills/dirtytrii/codex-skills/cheat-on-content.svg" alt="Measured on agentmods" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,335 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 unknown No closer match found 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 $0.00077 $0.04335
Opus 5 $0.00039 $0.02167
Sonnet 5 $0.00015 $0.00867
Haiku 4.5 $0.00008 $0.00434

Measured 8d ago against content hash d503e8e5a1e5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

cheat-on-content 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 8d ago.

The scan reads SKILL.md. This mod also ships 20 executable files (adapters/perf-data/bilibili-stat/crawler.py, adapters/perf-data/bilibili-stat/paths.py, adapters/perf-data/bilibili-stat/renderer.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/codex-skills-content/skills/cheat-on-content/SKILL.md · 224 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

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. 8d ago First seen · 224 lines · 77 tokens per session scan A d503e8e5a1e5

Subscribe to this mod's changes

cheat-on-content is a skill published in the GitHub repository Dirtytrii/codex-skills (11 stars, last pushed 13d ago), with no licence file. It adds 77 tokens to every session and 4,335 once invoked, about $0.0004 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 2026-08-30.

Related

Other skills, from other repositories

skill-sanitizer

Pre-share leak scanner for Claude Code skills (or any folder). Scans BEFORE you publish for secrets (API keys, tokens, private keys, .env values, high-entropy strings), PII (emails/phones), local machine paths (C:\Users\ , /home/ , /Users/ , UNC, internal hostnames), and client/proper-noun names from a LOCAL private…

animaresearch/skills · 193 tokens

public-skill-launcher

Package an AI skill, prompt workflow, or internal operating habit into a public-ready release with a catchy hook, safe redaction, useful examples, demo tasks, README copy, and launch messaging. Use when preparing a skill for GitHub, social sharing, marketplace submission, documentation, or community feedback.

animaresearch/skills · 66 tokens

knowledge-gravity-lab

Analyze folders of Markdown/text research notes, Obsidian vaults, paper cards, invention notes, or memory exports as a practical knowledge map. Use when asked to find center topics, noisy or low-signal notes, possible contamination, overgrown topic groups, orphan notes, cleanup actions, or a useful public…

animaresearch/skills · 74 tokens

super-lab-lite

Lightweight multi-agent research orchestration using one coordinator, three domain leads, and three lightweight research agents. Use for medium-size research, market scans, competitor comparisons, prior-art style exploration, report planning, and any task that benefits from parallel domain decomposition without…

animaresearch/skills · 64 tokens

dirty-skill

A deliberately leaky demo skill used to show what skill-sanitizer catches before you publish. Do not ship this.

animaresearch/skills · 28 tokens

cangjie-skill

A process for turning a book, course, podcast, interview, long video, or other long material into reusable instructions for an AI agent. It extracts methods and principles, checks them, and packages them as skills.

kangarooking/cangjie-skill · 143 tokens