Agent skill

One Shot Product Bet Discovery

by Peiiii in Peiiii/nextclaw

当创始人明确只有一次 all in 商业机会、给出最低成功率,并已有经过高召回搜索和批量筛选的决赛候选时使用;候选不限于软件产品,普通头脑风暴、宽搜、功能优先级或投资人点评不使用。

MITAuto-check passed

Install One Shot Product Bet Discovery

skills CLI
$ npx skills add Peiiii/nextclaw --skill one-shot-product-bet-discovery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Peiiii/nextclaw one-shot-product-bet-discovery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Peiiii/nextclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/wiki/skills/strategy/one-shot-product-bet-discovery .claude/skills/one-shot-product-bet-discovery && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
one-shot-product-bet-discovery
GitHub stars
260
Token cost
~738 tokens
SKILL.md length
94 words
Files
1
Skills in repo
70
Repo updated
First seen
Licence
MIT

At a glance

当创始人明确只有一次 all in 商业机会、给出最低成功率,并已有经过高召回搜索和批量筛选的决赛候选时使用;候选不限于软件产品,普通头脑风暴、宽搜、功能优先级或投资人点评不使用。

  • SKILL.md covers 目标, 进入条件, 决赛比较与证伪 and 信息差资格门, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

One Shot Product Bet Discovery is an agent skill from Peiiii/nextclaw. 当创始人明确只有一次 all in 商业机会、给出最低成功率,并已有经过高召回搜索和批量筛选的决赛候选时使用;候选不限于软件产品,普通头脑风暴、宽搜、功能优先级或投资人点评不使用。

Its SKILL.md is about 740 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: A human-centered long-term AI partner—not a task-centered assistant. The licence is MIT.

Example prompts

  • “/one-shot-product-bet-discovery”

What it can do on your machine

Read from SKILL.md and the folder at commit 4d9d500. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

One Shot Product Bet Discovery loads about 738 tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 94 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~738

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Peiiii/nextclaw at commit 4d9d500, republished under its MIT licence (© Peiiii). 94 words, ~738 tokens.

Download SKILL.mdSave it as .claude/skills/one-shot-product-bet-discovery/SKILL.md (or your agent's skills folder).
name
one-shot-product-bet-discovery
description
当创始人明确只有一次 all in 商业机会、给出最低成功率,并已有经过高召回搜索和批量筛选的决赛候选时使用;候选不限于软件产品,普通头脑风暴、宽搜、功能优先级或投资人点评不使用。

单次重注商业机会判断

目标

寻找在当前证据下能够越过用户最低胜率的商业机会,而不是为既定愿景或产品形态补理由。候选可以是软件、软硬结合、产品化服务、交易或网络、资产收购、运营型生意及其组合;AI 也可以只存在于供给侧。找不到时明确报告“尚未找到”,不得降低门槛、偷换成功定义或把未来实验成功后的条件概率当成当前概率。若门槛显著高于充分竞争市场的基准,必须找到并验证能够解释超额概率的信息差;不能只在市场已知 idea 中挑相对最好的一项。

这里的概率是用于决策的保守区间,不是假装精确的统计结果。愿景吸引力、技术或交付可行性和商业成功率必须分开判断。

进入条件

开始时冻结本轮成功合同:

  • 成功结果与时间范围;
  • 最低可接受概率;
  • 创始人的资金、时间、能力、分发与机会成本约束;
  • 允许验证到什么程度,以及什么损失不可接受;
  • 品类级胜利、可持续垂直生意、现金流资产经营和技术原型中,本轮究竟判断哪一个。
  • 相邻模式的基准成功率,以及候选需要用什么信息优势解释从基准到目标门槛的提升。

同时确认候选搜索 owner 已形成可审阅账本:主要结构视角已覆盖,原始池经过批量证据和统一过滤,留下不超过三个结构不同的决赛候选。若当前只有创始人的初始想法、零散竞品或第一个看起来合理的方向,不进入本 Skill,也不提前做真实试用。

上下文已有答案时直接复述并继续;只有缺失项会实质改变判断时才询问。事实、外部基准、推断和待验证假设分开记录。

决赛比较与证伪

对每个候选回答:谁会持续付费、为什么现在必须解决、为什么创始人能做到、第一批客户或可收购资产从哪里来、平台或既有供给为何不能轻易抹平、交付成本与信任风险是否可承受。写出关键成立条件、最强反方解释、单点致命假设,以及能够最大幅度改变候选排序的一组证据。

真实世界验证只用于区分桌面证据无法区分的决赛候选。访谈只能验证语言;真实委托、订单或收购谈判验证行为;续约、复购或持续交易验证重复价值;付费验证优先级;交付毛利验证经济性;扩权验证信任;可重复获客或可重复并购验证生意。

给出当前概率区间与依据,并把“验证后的条件概率”单列;区间跨过门槛、或仍依赖未验证的致命假设,都不算达标。每轮在同一探索账本中记录候选因什么证据升降级。已经被证伪的方向没有新证据不得换名字重新进入。

信息差资格门

当目标胜率高于相邻行业或商业结构基准时,信息差是必要而非充分条件。它可以是公开事实推导出的少数派因果判断、未被价格反映的时间窗口,或独有数据、渠道和资产入口,但必须写清:具体预测与期限、市场共识、未被套利原因、领先指标、创始人捕获路径和窗口失效方式。

“市场很大”“别人没做好”“我更努力或更会用 AI”“竞品暂时少”都不构成信息差。没有合格信息差时,保守区间上界不得越过用户门槛;返回未来判断搜索,不能调整权重制造达标结论。

创始人的 AI 局部峰值

当胜出理由包含“更充分地使用 AI”时,必须说清比较对象与局部结果;区分 Token、Agent 数和真实的问题分解、验证、纠偏与持续完成;用交付周期、完成率、人工救火或用户结果证明优势;并说明它如何在别人获得同类模型前沉淀为付费、系统、数据、分发、合同或所有权。只靠创始人长期超负荷操作不能提高商业成功率。

小团队更可能在工作数字化、反馈短、结果可验证、失败可回退且大组织存在协调或利益冲突的区域形成峰值;否则 AI 杠杆不得作为主要胜出理由。

机会应把模型进步作为生产资料和顺风,而不是把当前模型缺陷当作护城河。自动完成某个边界清楚的认知或执行任务、比通用模型多一点提示技巧,通常会被模型能力吞没;除非它同时积累独立、持久且可捕获的资产,否则不能作为 all-in 方向。重运营或线下交付不自动出局,但必须证明毛利、质量控制、区域密度或资产复用能够让小团队长期承受。

All-in 资格门

只有同时满足以下条件,方向才能标记为 all-in qualified:

  • 成功合同没有在评估中被悄悄缩小;
  • 保守概率区间整体越过用户门槛,而不只是乐观端越过;
  • 真实付费、续约或复购、核心结果、可重复触达和可承受单位经济已有行为证据;
  • 候选宣称的核心差异真实创造价值,不是展示功能;
  • 创始人优势能够转化为产品、交付系统、分发、合同、网络或资产所有权优势,并适合长期投入;
  • 不存在一个尚未验证、失败即推翻整个方向的致命假设;
  • 超过市场基准的概率提升有一条已获得行为或领先指标支持的信息差,且创始人能在窗口关闭前捕获;
  • 用户理解剩余风险后仍认可这个方向。

未通过时只允许标记为 探索中、值得验证 或 已淘汰。没有合格方向也是有效结论。

常见偷换

偷换正确处理
“实验成功后可能超过 50%”当前仍未达标,单列条件概率
“我们能做出来或交付一次”只证明技术或单次交付可行,不证明续约、毛利、付费或分发
“市场很大、趋势正确”继续回答为什么这一小群用户会现在选择我们
“巨头不够专注”说明可持续的结构性约束,不把意愿猜测当护城河
“下载、注册或赞美很多”寻找续约、复购、持续交易、付费、扩权与替代成本证据
“只有一次机会,所以必须现在选”稀缺性提高证据门槛,不提高方向本身的胜率
“我比别人更会用 AI”定义局部基准、外部结果和优势捕获路径
“这是现有选项里概率最高的一个”最高不等于越过门槛;缺少信息差时回到未来判断搜索
“竞品还不多”区分市场不存在、需求不存在和认知领先;写出共识基线、未套利原因与窗口

每轮输出

先给当前结论,再用紧凑表格呈现:候选机会、基准成功率、信息差及其证据、成功机制、商业结构、致命未知、保守概率区间、验证通过后的条件概率和下一组决定性证据。最后说明这组证据将如何改变候选排序或下注决定。

试用与复核

本 Skill 当前处于试用状态。首次真实使用暴露了三项缺口:原判断给出了品类冠军的低概率,却没有先围绕创始人的单次重注约束寻找可越过门槛的方向;随后又把宽搜、收敛和最终验证混在同一入口中,退化成低覆盖率的局部爬山;再次纠偏后发现“产品方向”本身仍会把 Agent、软件和从零创建产品误当作默认边界。宽搜已由独立 owner 负责,本 Skill 只保留成功合同、决赛比较和 all-in 资格门,并允许不同商业结构进入同一决赛。连续用于三次真实方向决策,或当前方向完成收敛后复核;若它能减少概率偷换且边界清晰则保留,否则继续合并或收窄。

© Peiiii, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/wiki/skills/strategy/one-shot-product-bet-discovery of Peiiii/nextclaw.

Open the folder on GitHubat commit 4d9d500

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Questions about One Shot Product Bet Discovery

What does One Shot Product Bet Discovery do?

当创始人明确只有一次 all in 商业机会、给出最低成功率,并已有经过高召回搜索和批量筛选的决赛候选时使用;候选不限于软件产品,普通头脑风暴、宽搜、功能优先级或投资人点评不使用。. One Shot Product Bet Discovery is an agent skill from Peiiii/nextclaw.

How do I install One Shot Product Bet Discovery in Claude Code?

Run `npx skills add Peiiii/nextclaw --skill one-shot-product-bet-discovery -a claude-code`. Or copy the skill folder (.agents/wiki/skills/strategy/one-shot-product-bet-discovery in Peiiii/nextclaw) into .claude/skills/one-shot-product-bet-discovery in your project. Claude Code loads it when a task matches its description.

How do I install One Shot Product Bet Discovery in Codex?

Run `npx skills add Peiiii/nextclaw --skill one-shot-product-bet-discovery -a codex`. Or copy the skill folder (.agents/wiki/skills/strategy/one-shot-product-bet-discovery in Peiiii/nextclaw) into .agents/skills/one-shot-product-bet-discovery in your project. Codex loads it when a task matches its description.

Can I use One Shot Product Bet Discovery in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Peiiii/nextclaw --skill one-shot-product-bet-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/one-shot-product-bet-discovery, .gemini/skills/one-shot-product-bet-discovery, .github/skills/one-shot-product-bet-discovery and .opencode/skills/one-shot-product-bet-discovery in your project.

What does One Shot Product Bet Discovery need to run?

SKILL.md names no scripts, command-line tools or credentials: One Shot Product Bet Discovery is instructions for the agent only.

Does One Shot Product Bet Discovery access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is One Shot Product Bet Discovery safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does One Shot Product Bet Discovery use?

One Shot Product Bet Discovery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does One Shot Product Bet Discovery use?

About 738 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to One Shot Product Bet Discovery?

Skills that share tags, products or a category with One Shot Product Bet Discovery: PUA Shot Agent Persona (tanweai/pua, 20k stars), Shots (hashgraph-online/awesome-codex-plugins, 1.3k stars), Readme Shot Demo (MaaAssistantArknights/MaaAssistantArknights, 24k stars) and Sports Betting (LeoYeAI/openclaw-master-skills, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains One Shot Product Bet Discovery?

Peiiii (a GitHub user) maintains it in Peiiii/nextclaw, which has 260 GitHub stars. The repository holds 70 skills in this directory. The repository was last updated on October 9, 2026.

Source: Peiiii/nextclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.