Claude Code Skills & Agents is a collection of reusable skills, sub-agents, and configuration templates that extend Claude Code with workflows such as research, image generation, translation, and GitHub automation. It is for Claude Code users who want to install these capabilities individually or as a marketplace plugin. The catalogue entries are components from this collection.
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/feiskyer/claude-code-settings/grill-menpx skills add feiskyer/claude-code-settings --skill grill-megit clone --depth 1 https://github.com/feiskyer/claude-code-settingsWrote 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/feiskyer/claude-code-settings/grill-me)<a href="https://agentmods.dev/skills/feiskyer/claude-code-settings/grill-me"><img src="https://agentmods.dev/badge/skills/feiskyer/claude-code-settings/grill-me.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 | $0.00058 | $0.02008 |
| Opus 5 | $0.00029 | $0.01004 |
| Sonnet 5 | $0.00012 | $0.00402 |
| Haiku 4.5 | $0.00006 | $0.00201 |
Grade A, and why
grill-me 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- grill-me — 86% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
追问会话
启动步骤
触发后,先执行以下检查:
- 检查项目根目录是否已有
CONTEXT.md或CONTEXT-MAP.md,有则读取 - 检查
docs/adr/是否已有 ADR 记录,有则了解已有决策 - 然后开始追问
核心规则
针对方案的每一个方面进行不留死角的追问,直到我们达成共识。沿着设计决策树逐一走下去,逐条解决决策之间的依赖关系。每个问题给出你的推荐答案。
一次只问一个问题,等我回复后再继续下一个。一次抛出多个问题会让人无所适从。
如果某个事实可以通过探索代码库获得,就直接查找,不要问我。但决策权在我——每个决策都提给我,等我回答。
在我明确确认达成共识之前,不要开始执行方案。
领域建模(内置流程)
追问过程中,一旦有决策结晶,就立即建立和完善项目的领域模型——挑战术语、构造边界场景、在第一时间写下术语表和决策记录。
文件结构
大多数仓库只有单一上下文:
/
├── CONTEXT.md
├── docs/
│ └── adr/
│ ├── 0001-event-sourced-orders.md
│ └── 0002-postgres-for-write-model.md
└── src/
如果根目录存在 CONTEXT-MAP.md,则表示仓库有多个上下文,地图指向各自位置:
/
├── CONTEXT-MAP.md
├── docs/
│ └── adr/ ← 系统级决策
├── src/
│ ├── ordering/
│ │ ├── CONTEXT.md
│ │ └── docs/adr/ ← 上下文专属决策
│ └── billing/
│ ├── CONTEXT.md
│ └── docs/adr/
懒创建——只在有内容可写时才创建文件。如果 CONTEXT.md 不存在,在第一个术语确定时创建它。如果 docs/adr/ 不存在,在第一个 ADR 需要时创建它。
追问过程中的行为
对照术语表质疑
当用户使用的术语与 CONTEXT.md 中已有定义冲突时,立即指出:"你的术语表把'取消'定义为 X,但你现在似乎是指 Y——到底是哪个?"
磨尖模糊表达
当用户使用含糊或多义的词汇时,提出精确的规范术语:"你说的'账户'——是指 Customer 还是 User?这是两个不同概念。"
讨论具体场景
讨论领域关系时,用具体场景压力测试。构造探索边界条件的场景,迫使用户精确界定概念之间的边界。
与代码交叉验证
当用户陈述某物如何运作时,检查代码是否一致。如果发现矛盾,立即暴露:"你的代码取消的是整个 Order,但你刚说可以部分取消——哪个是对的?"
即时更新 CONTEXT.md
术语一旦敲定,立即更新 CONTEXT.md,不要攒着批量处理。格式参见下方「CONTEXT.md 格式」章节。
CONTEXT.md 必须完全不含实现细节。不要把它当规格文档、草稿本或实现决策仓库。它只是术语表。
审慎提供 ADR
仅当以下三条全部成立时才创建 ADR:
- 难以逆转 — 将来改变主意的代价可观
- 缺乏上下文则令人费解 — 未来读者会困惑"为什么这样做?"
- 确实是权衡的结果 — 存在真正的替代方案,且你基于具体理由选了其一
参考:CONTEXT.md 格式
结构
# {上下文名称}
{一两句话描述这个上下文是什么、为什么存在。}
## 语言
**订单(Order)**:
{一两句话定义该术语}
_避免使用_: Purchase, transaction
**发票(Invoice)**:
向客户发送的交付后付款请求。
_避免使用_: Bill, payment request
**客户(Customer)**:
下订单的个人或组织。
_避免使用_: Client, buyer, account
注:示例语言应跟随项目主语言。中文项目用中文术语,英文项目用英文术语。
规则
- 有主张。 当多个词指代同一概念时,选最好的那个,其余列入
_避免使用_。 - 定义简洁。 最多两句话。定义它是什么,而非它做什么。
- 只收录项目上下文专属术语。 通用编程概念不属于这里。
- 出现自然聚类时分组。 若所有术语属于同一领域,平铺即可。
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 · 200 lines · 58 tokens per session scan A edaa68d8c1a1
grill-me is a skill published in the GitHub repository feiskyer/claude-code-settings (1,646 stars, last pushed 22d ago), licensed MIT. It adds 58 tokens to every session and 2,008 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
github-review-pr
Review GitHub pull requests with evidence-backed, multi-perspective analysis and false-positive filtering. Use when the user asks to review, inspect, or check a GitHub pull request by number or URL. Default to reporting findings locally; publish comments, submit reviews, or approve only when the user explicitly…
brainstorming
在构建新功能、创建新组件或设计新系统之前使用。通过协作对话探索用户意图、需求和设计方案,再进入实现阶段。当用户描述想要构建的东西且涉及设计决策时触发——不用于 bug 修复、配置变更或实现路径显而易见的任务。.
claude-skill
Use when work should be delegated to Claude Code CLI, especially headless claude -p runs, automation scripts, CI jobs, resumable sessions, or requests to use Claude/Claude Code for a task.
nanobanana-skill
Generate, remix, or edit images with Nanobanana / Nano Banana 2 through the bundled Gemini CLI wrapper. Use this whenever the user wants AI image generation or editing, especially for reference-image composition, character consistency, grounded visuals that may need live web search, style transfer, marketing graphics…
gpt-image-skill
Generate or edit images using OpenAI GPT Image API (gpt-image-2, gpt-image-1, etc). Triggers: "gpt image", "openai image", "generate image with openai", "draw image", "create image", "image generation", "AI drawing", "图片生成", "AI绘图", "生成图片", "画图". Use this skill whenever the user wants to generate or edit images and…
deep-research
深度调研的多实例(多 Agent)编排工作流:把一个调研目标拆成可并行子目标,用 Codex CLI 子进程采集和分析证据,再聚合、核验并精修为完整报告。用于系统性网页或资料调研、竞品与行业分析、批量链接或数据集分片、长文证据整合,以及用户提及深度调研、Deep Research、Wide Research、多 Agent 并行调研或多进程调研的场景。.