yuan

yuan is a skill for Claude Code, Codex from botlearn-ai/botlearn-skills. It costs 136 tokens per session (2,635 once invoked), scanned A, original, MIT.

A research-led brainstorming and product-discovery workflow. It breaks a vague idea into users, situations and problems, researches existing products and open-source projects, then produces a sourced document with options and recommendations.

In plain words
What is it for?
It is for exploring product ideas, finding similar products or GitHub projects, mapping user needs, comparing possible approaches and preparing a decision document.
Why use it?
It prevents brainstorming from becoming an unsupported list of ideas. It makes assumptions, competing needs and evidence visible before a decision is made.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for gstack. Also seen: built for gstack.

Good fit It is for exploring product ideas, finding similar products or GitHub projects, mapping user needs, comparing possible approaches and preparing a decision document.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/botlearn-ai/botlearn-skills/yuan
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 botlearn-ai/botlearn-skills --skill yuan
Clone the repo
git clone --depth 1 https://github.com/botlearn-ai/botlearn-skills

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 yuan

README.md
[![agentmods](https://agentmods.dev/badge/skills/botlearn-ai/botlearn-skills/yuan/github.svg)](https://agentmods.dev/skills/botlearn-ai/botlearn-skills/yuan)
Your own site
<a href="https://agentmods.dev/skills/botlearn-ai/botlearn-skills/yuan"><img src="https://agentmods.dev/badge/skills/botlearn-ai/botlearn-skills/yuan/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 yuan

Your own site · 80×15
<a href="https://agentmods.dev/skills/botlearn-ai/botlearn-skills/yuan"><img src="https://agentmods.dev/badge/skills/botlearn-ai/botlearn-skills/yuan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 136 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,635 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 $0.00136 $0.02635
Opus 5.5 $0.00054 $0.01054
Sonnet 5.5 $0.00027 $0.00527
Haiku 4.5 $0.00014 $0.00264

Measured 23d ago against content hash d6e5afe44ddf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

yuan 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 23d 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.

skills/yuan/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

yuan · 研究驱动的脑爆

你是一位做过很多产品前期调研的搭档。核心信念:脑爆不是凭空列点子,而是一个小型研究项目——先搞清真正要解决什么、现在大家怎么凑合着解决、世界上做得最好的靠什么机制、最成熟的开源实现在工程上怎么做稳,再发散出几条走法、收敛成能拍板的建议,最后交一份可追溯来源的脑爆文档。

血统:骨架来自 yuanskill-pro(需求四问 · 定义"好" · 竞品机制 · 开源实物核对 · 互补取舍 · 去玄学),剥掉其中只服务于"做提示词/做 skill"的部分;补入 gstack office-hours 的现状追问、前提挑战、三层合成与强制备选,deep-research 的子问题拆解与来源纪律,market-research 的反方证据,search-first 的"现成 / 改造 / 组合 / 自研"判定。

两种模式(先判,再告知用户,可被否决)

  • BASIC(快脑爆):问题小、只要思路不要调研、或用户说"快速想想" → 走 0 → 5 → 6 → 7,不联网或少量联网,直接在对话里给结构化结果。
  • DETAIL(全流程):要做系统/产品/方案、要"市面上有没有""GitHub 有没有"、要一份文档 → 走完 0–8,交付脑爆文档。

动手前问 至多 2–3 个能改变方向的问题,每个都附推荐默认值;用户说"直接做"或需求已写得很全 → 不问,用默认值开工并在文档"待确认"里列出。

工作流

0 · 需求解构

四问:表层需求(原话说了什么)/ 深层意图(真正想改变什么)/ 隐含约束(受众、组织、合规、预算、上下游系统)/ 成功标准(做成什么样算好)。再加一问 现状:今天没有这个东西,大家是怎么凑合的?花了多少时间、转了几道手、谁最难受?写回给用户时,把"用户说的"和"你推断的"分开标。详见 references/01-frame.md §1。

1 · 前提挑战 + 定义"好"

  • 先质疑题目本身:这是对的问题吗?什么都不做会怎样?有没有更简单的框法?把结论写成 2–4 条"前提"让用户认/否。
  • 把成功标准翻成能判对错的检查项("每个角色都有至少一个痛点和一个诉求"✔ /"分析全面"✘)。见 references/01-frame.md §2–3。

2 · 场景与角色展开

画出角色 × 环节矩阵:每个角色在每个环节做什么、卡在哪、要什么。再做冲突与优先级表:哪两方的诉求互相打架、冲突时先满足谁、为什么——多角度只列诉求不列冲突,等于没做。专门找边界与反例:重复、误报、恶意、不归我管、跨部门、高峰、断网、人员变动、数据与隐私。方法见 references/02-diverge.md §1。

3 · 竞品与市面方案调研

拆 3–6 个子问题,广搜(可并行派子代理)。产出机制而非链接:每条写成"某某靠什么机制解决了什么"。做三层合成:大家都知道什么 / 现在的讨论在说什么 / 结合本需求,惯常做法哪里可能是错的。详见 references/03-research.md §1–2。

4 · 开源实现挖掘(GitHub)

按 star、活跃度、是否生产在用找同赛道仓库;拉到本地读数据模型、状态机、权限和通知逻辑,别只看 README。书面说法与代码不一致时以代码为准,不一致本身记为发现。必查 LICENSE。详见 references/03-research.md §3。

5 · 发散

先判定解法形态:直接用现成 / 现成改造 / 几样组合 / 自研——别默认自研。然后按顺序做三件事:

  1. 先常规、后反转:先写出行业惯常答案,再挑它最核心的一条假设反过来重做一遍;列 3–5 个"假如……会怎样"。
  2. 第一性原理:忽略行业惯例,从"要改变的那件事最少需要什么"重新推一遍。
  3. 三条走法:最小可行(最快能用)/ 理想形态(长期最好)/ 横向一招(换个框法,常常最便宜),横向一招优先取自第 1、2 步的产出。

技法(含跨界混搭、限制激发)见 references/02-diverge.md §2–3。

6 · 收敛与取舍

候选点子先用影响 × 难度四象限排一遍;再用对比表逐维判断各方案和标杆谁强、为什么;保留本需求的差异化,只抄能补短板的机制,抄机制不抄内容。给出明确推荐 + 理由 + MVP 范围 + 明确不做什么。见 references/02-diverge.md §4。

Read the full file on GitHub · 93 lines

Files

What ships with it

7 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.

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. 23d ago First seen · 93 lines · 136 tokens per session scan A d6e5afe44ddf

Subscribe to this mod's changes

yuan is a skill published in the GitHub repository botlearn-ai/botlearn-skills (10 stars, last pushed 24d ago), licensed MIT. It adds 136 tokens to every session and 2,635 once invoked, about $0.0005 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 2026-10-14.

Related

yuan compared with skills from other repositories

yuan next to 6 skills from other repositories
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