smashup-faction-implementation

smashup-faction-implementation is a skill for Claude Code, Codex from zhuanggenhua/BoardGame. It costs 32 tokens per session (3,522 once invoked), scanned A, original, MIT.

A workflow for implementing and reviewing new Smash Up factions, where a faction is a themed group of cards in the game. It covers gameplay code, tests, end-to-end checks, and evidence, one faction at a time.

In plain words
What is it for?
Use it to add faction abilities, triggers, interactions, and base abilities; reuse shared game mechanisms; test real entry points; and document completion.
Why use it?
It prevents implementation from starting with unresolved source data and helps ensure that passing tests or connected assets represent working gameplay.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to add faction abilities, triggers, interactions, and base abilities; reuse shared game mechanisms; test real entry points; and document completion.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zhuanggenhua/boardgame/smashup-faction-implementation
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 zhuanggenhua/BoardGame --skill smashup-faction-implementation
Clone the repo
git clone --depth 1 https://github.com/zhuanggenhua/BoardGame

Made for: Claude Code, Codex.

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 smashup-faction-implementation

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhuanggenhua/boardgame/smashup-faction-implementation/github.svg)](https://agentmods.dev/skills/zhuanggenhua/boardgame/smashup-faction-implementation)
Your own site
<a href="https://agentmods.dev/skills/zhuanggenhua/boardgame/smashup-faction-implementation"><img src="https://agentmods.dev/badge/skills/zhuanggenhua/boardgame/smashup-faction-implementation/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 smashup-faction-implementation

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhuanggenhua/boardgame/smashup-faction-implementation"><img src="https://agentmods.dev/badge/skills/zhuanggenhua/boardgame/smashup-faction-implementation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,522 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original 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 $0.00032 $0.03522
Opus 5 $0.00016 $0.01761
Sonnet 5 $0.00006 $0.00704
Haiku 4.5 $0.00003 $0.00352

Measured 12d ago against content hash 20ae6bf69cd9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

smashup-faction-implementation 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.

.spec/skills/smashup-faction-implementation/SKILL.md · 174 lines

How it starts

The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Smash Up 派系玩法实施工作流

适用范围

用于 Smash Up 新派系在 intake 完成后的正式玩法实施:运行时接入、ability / trigger / interaction / base ability 实现、共享机制复用、测试、E2E、evidence 和批量收口。本流程不重新裁定图片、atlas、资源真相源;这些属于 smashup-faction-intake

进入 implementation 前必须已有 intake 合同和 handoff 包,至少包含主真相源、atlas 几何、row-major 索引、基地元信息、faction 清单、card/base canonical 名称、仍待裁定项和复用风险。intake 未收口时必须停下,不能边猜边做。

执行原则

  • 连续推进:用户给出“长期任务 / 继续 / 不要停 / 最后再总结”时,默认清空当前 scope;硬阻塞才停,并写清剩余对象和卡在哪层。
  • 一次一个派系:批量任务也按单派系闭环推进:实现、测试、E2E、evidence,再进入下一个派系;禁止多个派系同时改一半。
  • 资源完成不等于玩法完成:atlas、locale、选派系入口、静态数据或单测通过,只能证明结构或展示接通;玩法完成必须包含玩法实现、测试、真实入口和 evidence。
  • 先复用共享机制:优先查 src/games/smashup/domain/src/games/smashup/abilities/src/games/smashup/__tests__/ 是否已有 bury / uncover、ongoing modifier、movement、destroy、duel、before/after scoring、response window 等机制;确实缺共享抽象时再扩展共享层。
  • 共享重构默认允许:确认共享抽象缺口后可直接做必要扩展,前提是服务当前和后续派系、不引入一次性硬编码、同步测试与 evidence,并说明影响范围。分支 / worktree / tag、删除本地数据等高风险动作仍需用户确认。
  • 新派系 AI 走 outcome 合同:新增行动牌、可发动能力或其它会消耗手牌 / 次数 / 资源的对象时,AI 收益判断必须复用 game-ai-strategy-design 的共享 outcome 合同和 smashUpAiRuntime.projectActionOutcome;旧派系可按触碰范围逐步迁移,但新派系不得再为单卡补“零收益 / 无目标”特例。

批量重审模式

当任务是整批重审、重录或补证时:

  1. 建立批次对象清单,粒度到单卡、单基地或单 effect atom。
  2. 每项标注证据轴:未核图 / 已核图 / 已修数据 / 已过最终状态 / 已过真实入口 / 已过生命周期
  3. 用户说“继续”默认推进下一个未完成对象,不重复汇报上一个对象。
  4. 抽样发现 HIGH / CRITICAL、语义反转、ID 漂移、注册不触发或旧测试沿错语义时,旧“全面 / 收口”结论失效,必须回到全量矩阵。
  5. 批次清单仍有未完成对象时,只能写“仍有残余范围”。

effect atom 是重审最小单位。一张卡包含多个动作时,按目标选择、摧毁、额外打出、洗牌 / 重排等自然语义拆开;每个 atom 必须追到描述限定词、静态字段、command / validator、handler / reducer / trigger、UI / 交互出口、测试和证据。若测试断言沿错语义,测试本身就是 finding。

结论等级

对外汇报必须选择当前证据能支撑的等级:

  • 结构审计通过:静态接入、注册、targetTypedefId 或结构测试通过;不证明玩法。
  • 代表性玩法已验证:有行为级测试和至少一条真实规则链路,但不是全对象覆盖。
  • 当前发布口径已收口:当前计划发布范围内,结构、行为、真实入口、生命周期和残余范围声明都闭合。
  • 仍有残余范围:仍有未审家族、共享根因、交互链或验证缺口。
  • 旧结论失效:旧 audit / rollup / closeout 被新证据推翻,必须回写原文档。

只说“审计 / 重审 / 收口审计”时,默认是对已锁定对象范围做对象级审计,不自动扩大到整批派系。只有所有对象都有对象级结论,或明确登记为共享链路完全同构、仅配置不同,才允许写“当前发布口径已收口”。

Read the full file on GitHub · 174 lines

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. 12d ago First seen · 174 lines · 32 tokens per session scan A 20ae6bf69cd9

Subscribe to this mod's changes

smashup-faction-implementation is a skill published in the GitHub repository zhuanggenhua/BoardGame (23 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 3,522 once invoked, about $0.0002 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

develop-web-game

Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with rendergametotext.

netease-youdao/LobsterAI · 64 tokens

threejs-qa-release

Verify and release Three.js browser games: playtest QA, automated bot playtests, mobile and responsive checks, production builds, static-hosting base paths, debug gating, bundle review, screenshots, visual regression baselines, canvas-pixel inspection with measured metrics, and release risk reports.

majidmanzarpour/threejs-game-skills · 63 tokens

godot-e2e

Write and run E2E (end-to-end) game tests using the godot-e2e framework. Python controls a live Godot game over TCP — Locator-based semantic queries, expect() auto-retry assertions, and engine log capture make failures self-diagnosing. Use this skill whenever you need to: Test actual gameplay: player movement…

valkor-ai/loom · 189 tokens

test-playable-web-games

Test a playable browser game end to end with deterministic fixtures and real browser evidence. Use for gameplay QA, regression testing, controls, accessibility, responsive/mobile testing, save flows, console checks, performance smoke tests, and release verification.

MengTo/Skills · 52 tokens

visual-qa

Inspect Godot screenshots or short frame sequences for acceptance issues. Use after capturing a scene, when checking camera framing, missing assets, UI overlap, collision visualization, animation, or a black/empty viewport.

valkor-ai/loom · 45 tokens

pie-testing

Start, stop, and query Play-In-Editor (PIE) sessions for runtime testing of Blueprints, gameplay logic, widgets, AI, and any in-game behavior. Use when the user asks you to "play", "test", "run", "PIE", "start/stop the game", or otherwise needs a live game world to validate changes.

kevinpbuckley/VibeUE · 76 tokens