product-evolution-planner

product-evolution-planner is a skill for Claude Code, Codex from TashanGKD/tashan-cursor-skills. It costs 149 tokens per session (2,682 once invoked), scanned A, original, MIT.

A product strategy review that compares a current product with stated design principles and proposes possible improvements.

In plain words
What is it for?
Use it to diagnose product direction, find missing capabilities, and create evidence-based evolution suggestions.
Why use it?
It identifies gaps between what the product currently does and the principles it is meant to follow.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to diagnose product direction, find missing capabilities, and create evidence-based evolution suggestions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tashangkd/tashan-cursor-skills/product-evolution-planner
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 TashanGKD/tashan-cursor-skills --skill product-evolution-planner
Clone the repo
git clone --depth 1 https://github.com/TashanGKD/tashan-cursor-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 product-evolution-planner

README.md
[![agentmods](https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/product-evolution-planner/github.svg)](https://agentmods.dev/skills/tashangkd/tashan-cursor-skills/product-evolution-planner)
Your own site
<a href="https://agentmods.dev/skills/tashangkd/tashan-cursor-skills/product-evolution-planner"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/product-evolution-planner/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 product-evolution-planner

Your own site · 80×15
<a href="https://agentmods.dev/skills/tashangkd/tashan-cursor-skills/product-evolution-planner"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/product-evolution-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 149 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,682 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 $0.00149 $0.02682
Opus 5 $0.00075 $0.01341
Sonnet 5 $0.00030 $0.00536
Haiku 4.5 $0.00015 $0.00268

Measured 8d ago against content hash 50c87f78380e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

product-evolution-planner 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 8d 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/product-evolution-planner/SKILL.md · 206 lines

How it starts

The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.

主动式产品演进规划(product-evolution-planner)

核心定位:AI 不等待指令,主动对照原则思考「这个产品应该往哪里演进」。 强绑定 Rule:R2 NO_FABRICATION / R3 READ_FIRST / R1 EVIDENCE_FIRST / R6 ARTIFACT_FIRST

与其他 Skill 的区别:

  • role-产品经理:执行具体的产品设计任务(闭环递进法、动线设计)

知识导航表(执行前必须理解的概念根)

层级 文档 需要理解的概念
D0 认知根(必读) _内部总控/认知结构/L1_系统性文档/产品理论维度/AI时代产品问题全景框架.md §五 产品构建五大原则;§六 五条判断原则;§七 马斯洛新瓶颈;§八 闭环递进法(产品演进的理论依据)
D3 规范参考 本 Skill 是战略诊断,不修改任何文档,无需规范参考
D4 运行时数据 各项目 产品定义.md + 开发计划.md 当前产品现状(被诊断的对象)

核心概念速查: ① 原则驱动 = 以L1产品理论为标准评估,不是凭感觉或竞品对标来建议改进 ② 战略诊断 ≠ 产品设计:本 Skill 只产出建议报告,不创建或修改产品文档 ③ 缺口 = 当前产品与产品原则之间的差距(证据必须引用具体章节)

  • architecture-gap-mapper:对比两个已有系统/方案的差距
  • product-evolution-planner:对照设计原则,主动诊断当前产品,生成战略级改进建议

激活后立即执行(顺序不可跳过)

Step 1  读取原则文档(R3 READ_FIRST:必须先读完再分析)
        用 explore 子智能体并行读取以下文档:
        
        【必读,不存在则中止】
        - _内部总控/产品定义/AI时代产品问题全景框架.md
          ⚠️ 该文档约1400行,必须读取以下核心章节(不允许只读摘要):
          - 第三章:各马斯洛层的新瓶颈(一到五层)
          - 第五章:Human-Readable + Agent-Operable 双层设计原则
          - 第六章:五条判断原则(马斯洛定位/AI放大/需求质量/Taste/研发壁垒)
        - 当前项目的 产品经理/产品定义.md
        
        【可选,不存在则跳过,不报错】
        - _内部总控/产品定义/体系架构原则.md(未来会建立)
        - 当前项目的 技术架构师/技术架构.md
        
        若「必读」文档不存在:
        → 若产品原则文档不存在:中止并提示「_内部总控/产品定义/ 目录下未找到框架文档」
        → 若产品定义不存在:提示「当前项目没有产品定义,建议先运行 role-产品经理 完成定义,再做演进分析」

Step 2  从每条原则出发,对照当前产品做原则检验
        
        对照 AI时代产品问题全景框架 的核心原则(R1 EVIDENCE_FIRST:每条必须引用原则文档具体论断):
        
        【原则一:马斯洛新瓶颈检验】
        → 这个产品服务的是哪一层的需求?
        → 它解决的是「AI时代的新瓶颈」,还是「旧瓶颈」(已被AI直接解决或即将解决的)?
        → 判据:AI越强,这个需求是更强烈还是消失?
        
        【原则二:AI放大检验】
        → AI能力再强10倍,这个产品的需求变大了还是消失了?
        → 若消失:本质是在填AI能力空缺,不是长期方向
        
        【原则三:Human-Readable + Agent-Operable 双层设计检验】
        → 人层:产品是否极简可读、体验先于功能?
        → 智能体层:所有功能是否有对应的API/接口,AI可以完整操作?
        → 有没有「只为人设计、AI无法操作」或「只有API、人不知道怎么用」的情况?
        
        【原则四:闭环完整性检验】
        → 主干用户动线是否有明确的入口和出口?
        → 有没有「用户走到一半找不到下一步」的断点?
        → 有没有功能「没有反馈分支」(不论用户做什么,系统反应一样)?
        
        【原则五:研发壁垒检验】
        → 6-12个月内,竞品是否可以完整复制这个产品?
        → 背后有没有持续生产新知识的研究方向?
        → 数据飞轮是否存在(越用越强)?
        
        【技术架构原则检验(若文档存在)】
        → 读取体系架构原则.md,逐条对照当前技术架构
        
        每项检验必须同时给出:
        - 当前产品的具体证据(引用产品定义中的具体描述)
        - 通过/部分通过/未通过的判断
        - 未通过时:具体体现在哪里

Step 3  生成原则缺口清单
        
        对每个「未通过」或「部分通过」的检验,生成一条缺口描述:
        
        格式:
        [优先级] 缺口:[一句话描述]
        原则依据:[引用框架文档的具体论断]
        当前产品的体现:[引用产品定义的具体描述]
        改进方向:[建议的方向,不是具体实现]
        类型:产品功能类 / 技术架构类

Step 4  优先级排序
        
        P0:违反核心原则(会让产品往错方向持续投入)
            → 对应:解决旧瓶颈 / AI越强需求消失 / 数据飞轮反转
        P1:明显改进机会(现有框架内可做,改了明显更好)
            → 对应:闭环有断点 / 双层设计缺失某一层 / 研发壁垒薄弱
        P2:中长期演进方向(需要新资源或新研究,当前不紧急)

Step 5  输出「产品演进建议报告」(R6 ARTIFACT_FIRST:必须写文件)
        
        写入:产品经理/产品演进建议_YYYYMMDD.md
        
        格式见下方「报告格式」

Step 6  路由询问
        
        「📊 产品演进分析完成。共发现 N 条改进建议(P0: N,P1: N,P2: N)。
        
          是否现在执行某项?
          - 产品功能类建议 → 加载 role-产品经理 继续设计
          - 技术架构类建议 → 加载 role-技术架构师 继续设计
          - [选择第几条执行] [先看报告,稍后决定]」
        
        若用户选择执行某条:
        → 将该条建议的完整内容(原则依据 + 当前问题 + 改进方向)作为上下文
        → 加载对应角色 Skill,将建议内容传入作为「本次任务背景」

Read the full file on GitHub · 206 lines

Files

What ships with it

2 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. 8d ago First seen · 206 lines · 149 tokens per session scan A 50c87f78380e

Subscribe to this mod's changes

product-evolution-planner is a skill published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 149 tokens to every session and 2,682 once invoked, about $0.0007 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 2026-09-03.

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