make-skill

make-skill is a skill for Claude Code from LIUTod/scream-code. It costs 20 tokens per session (1,943 once invoked), scanned A, original, MIT.

A guided workflow for turning a conversation's solution into a reusable skill for coding agents.

In plain words
What is it for?
Use it to design, confirm, and install a new agent skill from an existing working method.
Why use it?
It helps capture the task type, name, purpose, focus areas, and final draft through several questions.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: names the AskUserQuestion tool.

Good fit Use it to design, confirm, and install a new agent skill from an existing working method.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/liutod/scream-code/make
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 LIUTod/scream-code --skill make
Clone the repo
git clone --depth 1 https://github.com/LIUTod/scream-code

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 make-skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/liutod/scream-code/make/github.svg)](https://agentmods.dev/skills/liutod/scream-code/make)
Your own site
<a href="https://agentmods.dev/skills/liutod/scream-code/make"><img src="https://agentmods.dev/badge/skills/liutod/scream-code/make/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 make-skill

Your own site · 80×15
<a href="https://agentmods.dev/skills/liutod/scream-code/make"><img src="https://agentmods.dev/badge/skills/liutod/scream-code/make.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,943 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 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 $0.00020 $0.01943
Opus 5.5 $0.00008 $0.00777
Sonnet 5.5 $0.00004 $0.00389
Haiku 4.5 $0.00002 $0.00194

Measured 1mo ago against content hash 39b71a67a39d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

make-skill 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 1mo 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.

packages/agent-core/src/skill/builtin/make-skill.md · 195 lines

How it starts

The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Make Skill: 从上下文提炼 Skill

用户调用了 /make-skill。你的任务是通过对话引导用户,把当前会话中解决问题的方式沉淀为一个可复用的 Scream Code Skill,并安装到插件中心。

激活参数

本次激活的参数:

$ARGUMENTS
  • initialRequest:用户输入 /make-skill 时附带的一句话描述,可能为空。

工作方式

这不是一次性任务。你需要通过多轮对话澄清以下信息,每一轮只问一个问题,等用户回答后再进入下一阶段:

  1. Skill 类型(workflow / code-pattern / troubleshooting / tool-chain / custom)
  2. Skill 名称(kebab-case)
  3. 这个 Skill 主要解决什么问题
  4. 希望重点关注哪些内容
  5. 生成草案并确认安装

阶段判断

根据当前对话历史判断你处于哪个阶段:

  • 阶段 0:本 Skill 刚激活,还没有问过任何问题。先分析会话上下文和 initialRequest,然后用 AskUserQuestion 询问 Skill 类型。
  • 阶段 1:已经确定了 Skill 类型,但还没有确定名称。根据类型和上下文建议一个 kebab-case 名称,用 AskUserQuestion 询问用户是否接受或修改。
  • 阶段 2:已经确定了名称,但还没有明确解决的问题。根据上下文总结一句话描述,用 AskUserQuestion 询问用户是否接受或修改。
  • 阶段 3:已经确定了问题,但还没有明确关注重点。给出 2-4 个关注重点建议,用 AskUserQuestion 让用户选择或输入。
  • 阶段 4:类型、名称、问题、重点都已确定。调用 MakeSkillPlanTool 生成草案,展示给用户,并用 AskUserQuestion 询问是否确认安装。
  • 阶段 5:用户已确认安装。调用 MakeSkillApplyTool 写入插件中心。

如何判断“已确定”:历史消息中已经有你提出的 AskUserQuestion 以及用户给出的明确回答(或选择了你建议的选项)。

每轮提问规范

除非处于阶段 4/5,否则每轮必须也只允许使用一次 AskUserQuestion。不要直接用普通文本提问,这样无法给用户结构化选项。

阶段 0:询问 Skill 类型

先分析当前会话上下文,判断最可能想沉淀什么。给出 2-4 个建议选项,不要列出全部 5 种。把最相关的放在最前面,并标记 (Recommended)。

示例问题:

{
  "questions": [
    {
      "question": "根据刚才的会话,你想把什么沉淀成 Skill?",
      "header": "类型",
      "options": [
        { "label": "Code pattern (Recommended)", "description": "把 React 表单验证的代码模式提炼为可复用模板" },
        { "label": "Workflow", "description": "把解决表单验证问题的步骤沉淀为流程" },
        { "label": "Troubleshooting", "description": "把常见验证错误排查过程沉淀为诊断指南" }
      ],
      "multi_select": false
    }
  ]
}

注意:系统会自动添加 "Other" 选项,不要自己添加。如果用户想选未列出的类型(如 tool-chain 或 custom),他们会通过 Other 输入。

阶段 1:询问 Skill 名称

根据已确定的类型和上下文,建议一个 kebab-case 名称。用 AskUserQuestion 让用户接受、修改或自己输入。

示例:

{
  "questions": [
    {
      "question": "建议把这个 Skill 命名为 react-form-validate,是否接受?",
      "header": "名称",
      "options": [
        { "label": "使用 react-form-validate (Recommended)", "description": "简洁直观,符合 kebab-case" },
        { "label": "换一个名称", "description": "我给出其他建议" },
        { "label": "我自己输入", "description": "手动指定名称" }
      ],
      "multi_select": false
    }
  ]
}

Read the full file on GitHub · 195 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. 1mo ago First seen · 195 lines · 20 tokens per session scan A 39b71a67a39d

Subscribe to this mod's changes

make-skill is a skill published in the GitHub repository LIUTod/scream-code (140 stars, last pushed 2d ago), licensed MIT. It adds 20 tokens to every session and 1,943 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.

Related

Other skills, from other repositories

session-sync

Syncs Claude Code project data — memories, session transcripts, settings, agent definitions, remember-logs, and handoff documents — between the same project on a remote workstation and the local machine over SSH, so local work can resume exactly where a remote session left off. Use when starting on a new machine…

HelixDevelopment/code · 82 tokens

persistent-notes

Save notes locally to /mnt/workspace/notes.json file. Use when user wants to "save a note" or "remember something".

awslabs/agentcore-samples · 32 tokens

llm-wiki

Build, ingest, query, and lint a persistent Markdown knowledge base using the Karpathy LLM Wiki pattern. Use for adding sources, maintaining interlinked wiki pages, answering from the knowledge base, filing durable research, or checking contradictions and index health. Do not use it to persist ordinary chat or…

TencentCloud/Octop · 69 tokens

awm-rsi-toolkit

Run AWM (Agent Workflow Memory, Wang et al. 2024) on an agent - induce reusable workflows from its successful trajectories, online over a training stream or offline from supplied experiences, then freeze the workflow memory and measure it on a published Benchmark.

Prism-Shadow/penguin-harness · 58 tokens

computer-history

Answer questions about what the user recently did on their computer — who contacted them, what they were working on, where they left off — by reading the local Computer History summaries and raw event streams.

MemTensor/memmy-agent · 42 tokens

docx

Create, read, edit, review, redline, comment on, merge, audit, render, and verify .docx Word documents, including layout-sensitive and OOXML-level work.

MemTensor/memmy-agent · 40 tokens