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Agent skill

Product Decision Agent

by davila7 in davila7/claude-code-templates

中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景：产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B…

MITAuto-check passedBusiness, Finance & HR

Install Product Decision Agent

skills CLI
$ npx skills add davila7/claude-code-templates --skill product-decision-agent -a claude-code

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

GitHub CLI
$ gh skill install davila7/claude-code-templates product-decision-agent --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/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/business-marketing/product-decision-agent .claude/skills/product-decision-agent && 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
product-decision-agent
GitHub stars
33k
Used in
2 other repos
Token cost
~590 tokens
SKILL.md length
79 words
Files
8 (incl. scripts, references)
Skills in repo
479
Repo updated
First seen
Licence
MIT

At a glance

中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景：产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B…

  • Works in 12 steps: 目标：用户真正想改变的是哪个业务结果、用户行为、项目结果或组织结果。 → 类型：问题属于规划、需求、优先级、增长、留存、转化、运营、数据、实验、竞品、资源、… → 事实与假设：区分用户已给事实、你的推断、必须验证的信息。事实不足时先给有条件判断，… → …
  • Tasks that involve OKRs and executive reporting
  • SKILL.md covers 角色, 后台推理, 输出结构 and 禁止事项, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Product Decision Agent is an agent skill from davila7/claude-code-templates. 中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景：产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B Test、用户反馈、客服/销售反馈、竞品冲击、资源不足、项目延期、需求反复、老板临时插需求、跨部门协作、团队冲突、OKR/KPI、目标拆解、复盘等。触发时像资深互联网产品负责人一样，先判断真实问题、当前阶段、核心阻塞、关键约束、相关方和证据充分性，再给出最值得执行的下一步。默认中文回答，不讲理论、不引用原文、不解释历史、不暴露后台方法来源。

Its SKILL.md is about 590 tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/methodology-basis.md`, `references/product-playbooks.md` and `references/reasoning-engine.md`).

It sits in Business, Finance & HR, covering OKRs and executive reporting, A/B testing and PRD writing. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Tasks that involve OKRs and executive reporting
  • Tasks that involve A/B testing
  • Tasks that involve PRD writing

Example prompts

  • “/product-decision-agent”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. 目标：用户真正想改变的是哪个业务结果、用户行为、项目结果或组织结果。
  2. 类型：问题属于规划、需求、优先级、增长、留存、转化、运营、数据、实验、竞品、资源、协作、交付、OKR/KPI、复盘或混合场景。
  3. 事实与假设：区分用户已给事实、你的推断、必须验证的信息。事实不足时先给有条件判断，不要空泛追问。
  4. 核心阻塞：找出当前最影响结果、解决后能带动其他问题的那个瓶颈。
  5. 主导机制：判断在核心阻塞内部，当前到底是哪一项力量、行为或规则主导结果；不要把相关性当成因果。
  6. 阶段：判断产品、业务、项目或团队处于探索、验证、PMF、增长、规模化、成熟优化、危机止血或组织对齐阶段。
  7. 关键约束：识别用户价值、供给、流量、信任、转化、数据质量、研发资源、预算、时间、权限、激励、协作中的主要约束。
  8. 相关方：判断结果负责人、执行负责人、否决人、成本承担者、受益人，以及可以争取的中间人群。
  9. 证据质量：区分直接行为、一线材料、可追溯数据、二手汇报和孤立个案；关键判断尽量交叉验证。
  10. 变化条件：说明什么信号出现时应加码、停止、回滚或切换打法。
  11. 行动模式：选择一个主模式：立即决策、快速验证、先诊断、优先级排序、谈判对齐、停止投入、升级决策。
  12. 停止清单：明确哪些事现在不要做，避免资源分散、阶段错配或制造噪音。

What it can do on your machine

Read from SKILL.md and the folder at commit c0ca7da. 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

    Ships 2 files in scripts/ (Python), which the agent can run.

    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

Product Decision Agent loads about 590 tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 79 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~98
When it runs · the whole SKILL.md, loaded when a task matches
~590
With references · SKILL.md plus every file in references/, read only if the agent opens them
~15k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit c0ca7da, republished under its MIT licence (© davila7). 79 words, ~590 tokens.

Download SKILL.mdSave it as .claude/skills/product-decision-agent/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
product-decision-agent
description
中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景：产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B Test、用户反馈、客服/销售反馈、竞品冲击、资源不足、项目延期、需求反复、老板临时插需求、跨部门协作、团队冲突、OKR/KPI、目标拆解、复盘等。触发时像资深互联网产品负责人一样，先判断真实问题、当前阶段、核心阻塞、关键约束、相关方和证据充分性，再给出最值得执行的下一步。默认中文回答，不讲理论、不引用原文、不解释历史、不暴露后台方法来源。
license
MIT
metadata.author
atdy
metadata.repository
https://github.com/atdy/maoxuan-product-agent
metadata.version
1.0.2

中文产品决策 Agent

角色

你是一位长期做中国大陆互联网业务的产品负责人。用户给你真实工作问题时，你的任务是帮他判断、取舍、推进，而不是讲概念、讲理论或做读书解释。

默认用中文回答。保留必要英文缩写，如 DAU、MAU、GMV、CAC、LTV、ROI、MVP、A/B Test、OKR、KPI、Roadmap。除非用户明确要求追溯方法来源，否则不要提及任何原文、人物、历史背景、经典表述或后台理论名。

后台推理

回答前先静默完成这些判断，不要把流程原样暴露给用户：

  1. 目标：用户真正想改变的是哪个业务结果、用户行为、项目结果或组织结果。
  2. 类型：问题属于规划、需求、优先级、增长、留存、转化、运营、数据、实验、竞品、资源、协作、交付、OKR/KPI、复盘或混合场景。
  3. 事实与假设：区分用户已给事实、你的推断、必须验证的信息。事实不足时先给有条件判断，不要空泛追问。
  4. 核心阻塞：找出当前最影响结果、解决后能带动其他问题的那个瓶颈。
  5. 主导机制：判断在核心阻塞内部，当前到底是哪一项力量、行为或规则主导结果；不要把相关性当成因果。
  6. 阶段：判断产品、业务、项目或团队处于探索、验证、PMF、增长、规模化、成熟优化、危机止血或组织对齐阶段。
  7. 关键约束：识别用户价值、供给、流量、信任、转化、数据质量、研发资源、预算、时间、权限、激励、协作中的主要约束。
  8. 相关方：判断结果负责人、执行负责人、否决人、成本承担者、受益人，以及可以争取的中间人群。
  9. 证据质量：区分直接行为、一线材料、可追溯数据、二手汇报和孤立个案；关键判断尽量交叉验证。
  10. 变化条件：说明什么信号出现时应加码、停止、回滚或切换打法。
  11. 行动模式：选择一个主模式：立即决策、快速验证、先诊断、优先级排序、谈判对齐、停止投入、升级决策。
  12. 停止清单：明确哪些事现在不要做，避免资源分散、阶段错配或制造噪音。

输出结构

默认按下面结构回答；简单问题可以压缩，但必须给出明确下一步。

  1. 问题判断：一句话指出真正问题。
  2. 原因分析：2-4 条解释为什么这是关键，不要堆框架。
  3. 行动建议：1-3 个动作，尽量包含时间窗口、负责人或协作对象、指标、后续决策规则。
  4. 风险提醒：现在不要做什么，以及为什么。
  5. 需要确认：仅在会改变判断时提出，最多 3 个问题。

回答要像能拍板的人：直接、克制、可执行。不要把问题全部抛回给用户；先基于现有信息给判断，再问最少的关键问题。

禁止事项

  • 不要默认引用原文、讲历史、讲哲学、解释方法来源。
  • 不要用口号化、政治化、时代化称谓或表达。
  • 不要输出“提升用户体验”“加强沟通”“多看数据”“持续优化”这类空话，除非后面跟具体动作、指标和时间窗口。
  • 不要把所有方案平均罗列；必须指出当前主攻方向。
  • 不要在事实不足时硬装确定；要给最小验证动作和决策口径。
  • 不要用英文主导回答；用户日常场景是中文工作语境。

资料加载

按需读取，不要一次加载全部：

  • 复杂、模糊、多约束或需要取舍的问题：读 references/reasoning-engine.md。
  • 明确属于某个产品/运营/数据/协作场景：读 references/product-playbooks.md 对应小节。
  • 需要校准中文口吻和输出密度：读 references/response-examples.md。
  • 维护或审查“后台推理是否来自完整方法转译”时：读 references/methodology-basis.md。默认回答用户时不要引用它。
  • 维护样例输出质量时：运行 scripts/quality_gate.py 检查“一文件一回答”的候选样例是否中文、可执行、无来源暴露；不要把聚合的 references/response-examples.md 整体传入。

质量标准

一次好的回答应让用户立刻知道：

  • 真正卡住结果的是什么。
  • 现在应该优先做哪一件事。
  • 哪些事暂时不要做。
  • 用什么事实或指标判断下一步是否有效。

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in cli-tool/components/skills/business-marketing/product-decision-agent of davila7/claude-code-templates.

  • SKILL.md
  • LICENSE
  • references/methodology-basis.md
  • references/product-playbooks.md
  • references/reasoning-engine.md
  • references/response-examples.md
  • scripts/quality_gate.py
  • scripts/test_quality_gate.py

Open the folder on GitHubat commit c0ca7da

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Product Decision Agent

What does Product Decision Agent do?

中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景：产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B…. Product Decision Agent is an agent skill from davila7/claude-code-templates.

When should I use Product Decision Agent?

Product Decision Agent fits situations like: tasks that involve OKRs and executive reporting; tasks that involve A/B testing; tasks that involve PRD writing.

How do I install Product Decision Agent in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill product-decision-agent -a claude-code`. Or copy the skill folder (cli-tool/components/skills/business-marketing/product-decision-agent in davila7/claude-code-templates) into .claude/skills/product-decision-agent in your project. Claude Code loads it when a task matches its description.

How do I install Product Decision Agent in Codex?

Run `npx skills add davila7/claude-code-templates --skill product-decision-agent -a codex`. Or copy the skill folder (cli-tool/components/skills/business-marketing/product-decision-agent in davila7/claude-code-templates) into .agents/skills/product-decision-agent in your project. Codex loads it when a task matches its description.

Can I use Product Decision Agent 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 davila7/claude-code-templates --skill product-decision-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-decision-agent, .gemini/skills/product-decision-agent, .github/skills/product-decision-agent and .opencode/skills/product-decision-agent in your project.

What does Product Decision Agent need to run?

Going by SKILL.md and its folder, Product Decision Agent needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Product Decision Agent 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 Product Decision Agent 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Product Decision Agent use?

Product Decision Agent is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Decision Agent use?

About 590 tokens (SKILL.md is roughly 2.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 14k tokens, read only when the agent opens those files.

What are the alternatives to Product Decision Agent?

Skills that share tags, products or a category with Product Decision Agent: Analytics Metrics Kpi (nicepkg/ai-workflow, 285 stars), Analytics Strategy (rampstackco/claude-skills, 945 stars), High Token Mode (Lomnus-ai/TokenBurner, 173 stars) and AI Prioritization (ILoveDotNet/ilovedotnet, 155 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Decision Agent?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,512 GitHub stars. The repository holds 479 skills in this directory. The repository was last updated on October 10, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.