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 skills add TulanCN/vibe-noveling --skill fuck-itgit clone --depth 1 https://github.com/TulanCN/vibe-novelingWrote 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/tulancn/vibe-noveling/fuck-it)<a href="https://agentmods.dev/skills/tulancn/vibe-noveling/fuck-it"><img src="https://agentmods.dev/badge/skills/tulancn/vibe-noveling/fuck-it/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/tulancn/vibe-noveling/fuck-it"><img src="https://agentmods.dev/badge/skills/tulancn/vibe-noveling/fuck-it.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 | $0.00050 | $0.00703 |
| Opus 5 | $0.00025 | $0.00351 |
| Sonnet 5 | $0.00010 | $0.00141 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
fuck-it 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 12d 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.
What it actually says
单章加戏器
不动章末目标，只放大过程阻力、烈度、压迫和场面感。
硬约束
- 先锁死章末目标再加戏：输出前用一句话复述当前章既定收尾，所有方案共享这个目标
- 只炸过程，不炸终点：允许升级外部压迫、对抗烈度、公开场面、群体围观、误会试探、身份暴露风险、时间压力、身体代价、情绪爆发；不允许改本章谁赢谁输、主角拿没拿到结果、收尾落在哪个节点
加戏方向（15 种，SSoT 独立选 3 种）
在心里生成随机字符串（≥20 字符），拆成三段，分别对 15 取模，确保三个方向不重复：
压迫升级 / 公开处刑 / 表演反杀 / 身份掀牌 / 误会爆燃 / 失控连锁 / 倒计时逼杀 / 围观哗然 / 赌注加码 / 资源断供 / 旧账追魂 / 错位登场 / 规则翻脸 / 诱饵钓杀 / 带伤硬撑
漫画感要求
每套方案必须有可视化、可表演、可放大的瞬间，让读者感到"这一幕被故意抬高了"。允许夸张的公开场合、强烈的视觉反差、突然掀开的身份压差、群体围观、连锁误判、带有镜头感的动作/台词/停顿/表情。
三个方案的差别在于着力点不同（偏压迫/偏掀牌/偏失控等），不允许其中一套写成只有结构加强没有场面感的平推版。
上下文读取
只读最小锚点：本章困局和希望放大的点、已确认的任务卡/收尾点、上一章收尾动作。缺少章末目标时允许追问 1 个问题："这章原本必须落到哪个结果？"
输出
🎭 方案 A｜标题
- 所选方向：
- 本章结束目标（固定）：
- 加戏核心：
- 冲突升级链：
- 角色表现力放大点：
- 漫画感/场面感装置：
- 局部代价或挂彩：
- 为什么终点没变但过程更炸：
🎭 方案 B｜标题
...
🎭 方案 C｜标题
...
落地
用户确认后交给 novel-discuss 落事件卡。
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.
- 12d ago First seen · 56 lines · 50 tokens per session scan A 02ab4893ae24
fuck-it is a skill published in the GitHub repository TulanCN/vibe-noveling (23 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 703 once invoked, about $0.0003 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
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…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
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…