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/yunyoujun/advjs/adv-adaptnpx skills add YunYouJun/advjs --skill adv-adaptgit clone --depth 1 https://github.com/YunYouJun/advjsWrote 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/yunyoujun/advjs/adv-adapt)<a href="https://agentmods.dev/skills/yunyoujun/advjs/adv-adapt"><img src="https://agentmods.dev/badge/skills/yunyoujun/advjs/adv-adapt.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 | $0.00072 | $0.01058 |
| Opus 5 | $0.00036 | $0.00529 |
| Sonnet 5 | $0.00014 | $0.00212 |
| Haiku 4.5 | $0.00007 | $0.00106 |
Grade A, and why
adv-adapt 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADV Source Adaptation
Build an adaptation whose structural coverage can be checked without pretending that automation can judge semantic fidelity.
Workflow
1. Establish the source contract
Record every source work before writing scripts:
- canonical title, author, stable URL, local snapshot or revision, and license;
- explicit order when several works form a sequence;
- heading-level section inventory with stable lowercase IDs;
- requested fidelity mode: complete canonical, faithful compression, or free reinterpretation.
Separate story prose from author metadata at this point. Forewords, postscripts, contest notes, revision notes, source-order explanations, and license/attribution text belong in the manifest or About page unless the user explicitly wants them dramatized. Record exclusions with required: false and an excludedReason; do not silently delete them.
Stop and resolve missing rights or contradictory source versions before copying text or publishing assets.
2. Create the adaptation manifest
Read references/adaptation-manifest.md and create adv/adaptation.json. Include all source sections, a complete cast, scenes, terminology, and the boundary between canonical and newly authored content.
For complete-canonical adaptations, mark every prose section that must survive as required: true. Do not mark forewords or postscripts optional merely because they are not dramatic scenes unless the user explicitly excludes them.
3. Build the authoring inventories
Before drafting chapters:
- create or update every
.character.mdfile, including aliases, voice, relationships, first appearance, appearance, andimagePrompt; - create every scene entry with time variants, atmosphere, reusable background needs, and
imagePrompt; - create an outline that maps source sections to ADV chapters and names all added branches;
- distinguish source-derived dialogue, staging adaptations, and wholly new material.
What ships with it
3 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.
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 · 94 lines · 72 tokens per session scan A cf7df93e8020
adv-adapt is a skill published in the GitHub repository YunYouJun/advjs (325 stars, last pushed 16d ago), licensed MPL-2.0. It adds 72 tokens to every session and 1,058 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.
Other skills, from other repositories
anti-cheat-systems
Guide for modern game anti-cheat architecture, Windows kernel monitoring, and detection tradeoffs. Use this skill when analyzing EAC, BattlEye, Vanguard, FACEIT AC, kernel callbacks, handle protection, manual-map detection, boot-start drivers, BYOVD, DMA threats, or behavioral telemetry in game security research.
windows-kernel-security
Guide for Windows kernel internals and security mechanisms used in game protection and low-level research. Use this skill when working with drivers, IRQL-sensitive callbacks, EPROCESS, ETHREAD, MMVAD internals, IOCTL paths, DSE, PatchGuard, HVCI, PiDDBCache, MmUnloadedDrivers, or kernel memory inspection.
game-hacking-techniques
Guide for game-hacking technique taxonomy and threat modeling relevant to game security. Use this skill when researching memory access, code injection, overlays, input simulation, engine-specific attack surfaces, or how modern anti-cheat systems constrain user-mode, kernel-mode, hypervisor, and DMA-based cheat…
reverse-engineering-tools
Guide for reverse engineering protected games and anti-cheat components across user mode, kernel mode, and hypervisor-aware environments. Use this skill when analyzing drivers, IOCTL protocols, callback registration, injected-code artifacts, integrity checks, protected binaries, or debugging security-sensitive game…
graphics-api-hooking
Guide for graphics API interception, overlay rendering, and render-pipeline analysis across DirectX, OpenGL, and Vulkan. Use this skill when working with Present or SwapBuffers hooks, DXGI swap chains, shader or draw-call interception, screenshot-sensitive overlays, or graphics debugging in game security research.
game-engine-resources
Guide for game-engine internals, source trees, plugins, and engine-specific security research. Use this skill when researching Unreal, Unity, Source, Godot, custom engines, engine detectors, engine explorers, or engine protection patterns relevant to modding, reverse engineering, and anti-cheat.