stylework-requirement-planning

stylework-requirement-planning is a skill for Codex from PANGKAIFENG/ai-product-manager-skills. It costs 154 tokens per session (1,537 once invoked), scanned A, original, MIT.

A planning skill for reviewing a batch of product or business requests from sources such as Excel, CSV files, screenshots, or work-management exports. It groups requests by theme and records duplicates, dependencies, unclear items, and planning assumptions.

In plain words
What is it for?
Use it to cluster requests, identify possible duplicates and prerequisite work, flag missing information, and discuss iteration priorities using business goals, customer commitments, technical difficulty, and team capacity.
Why use it?
It helps teams understand what a group of requests is trying to solve before deciding priorities, without presenting guesses as confirmed facts or promising delivery dates.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to cluster requests, identify possible duplicates and prerequisite work, flag missing information, and discuss iteration priorities using business goals, customer commitments, technical difficulty, and team capacity.

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Install with agentmods
npx agentmods add skills/pangkaifeng/ai-product-manager-skills/stylework-requirement-planning
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 PANGKAIFENG/ai-product-manager-skills --skill stylework-requirement-planning
Clone the repo
git clone --depth 1 https://github.com/PANGKAIFENG/ai-product-manager-skills

Made for: 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 stylework-requirement-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/stylework-requirement-planning/github.svg)](https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/stylework-requirement-planning)
Your own site
<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/stylework-requirement-planning"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/stylework-requirement-planning/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 stylework-requirement-planning

Your own site · 80×15
<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/stylework-requirement-planning"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/stylework-requirement-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 154 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,537 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 $0.00154 $0.01537
Opus 5 $0.00077 $0.00768
Sonnet 5 $0.00031 $0.00307
Haiku 4.5 $0.00015 $0.00154

Measured 12d ago against content hash 8701a113df86, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

stylework-requirement-planning 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 12d 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/stylework-requirement-planning/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

StyleWork 需求排期共创

Overview

先把一批需求解释成人能讨论的主题地图,回答“这一批在解决什么”,再与用户共创优先级和迭代建议。Skill 只读分析,不修改云效,不修改钉钉,也不修改用户提供的源 Excel;用户最终手工调整。

Core Boundaries

  • 不调用 stylework-yunxiao-requirement-sync,不导出、不新建 Sheet、不写回任何系统。
  • 不把标题推断写成已确认事实;事实、推断和建议必须分开。
  • 不要求批量补录描述、提出方、期望时间、收益数据或完整 PRD 才开始分析。
  • 信息不足时仍输出临时建议,并为每个关键判断标注理由、缺失信息、风险和置信度。
  • 不替负责人承诺交期,不把建议迭代描述成已经排定。

Minimum Input

接受 Excel、CSV、钉钉 Sheet 的只读内容、截图或用户粘贴的需求列表。优先使用已有字段:

标题、负责人、创建者、迭代、技术难度、优先级、客户名称、URL

描述是可选增强信息,不是启动门槛。若 URL 可用且少量关键需求仅凭标题无法判断,可在用户已经授权查看的前提下,用浏览器只读打开这些需求详情;不要批量修改、评论或改变状态。

Workflow

1. Establish the evidence ledger

记录:

  • 已确认字段与用户补充事实;
  • 从标题或分组推断的主题;
  • 缺失信息;
  • 冲突;
  • 当前排期假设。

标题只能支持初步分类。模糊标题不得被补造成具体业务目标、客户承诺或技术方案。

2. Summarize what the batch is solving

先完成主题聚类,再讨论逐条排期。输出每个主题:

  • 主题名;
  • “在解决什么”一句话;
  • 代表需求;
  • 数量与当前迭代分布;
  • 主题性质:用户体验、业务能力、平台基建、可靠性/治理、探索验证或其他;
  • 判断依据与置信度。

同时标记:重复/高度相似项、依赖、前置能力、可能的能力链和模糊项。不要因标题相似就自动合并,只输出合并候选和需要核对的差异。

3. Invite high-value clarification

在主题摘要后,向用户提出最多 1-3 个批次级高价值问题,优先确认:

  • 当前领导或阶段重点;
  • 已承诺客户、固定日期或必须上线事项;
  • 本月最需要形成的业务结果,或明确不能做的方向。

不要把每条需求的未知项变成几十个问题。若用户暂时不回答、明确要求直接给建议,或上下文已经足够,继续输出临时版,不暂停整个排期。

4. Apply the planning rubric

读取 references/planning-rubric.md。依次判断:

  1. 外部硬约束与紧迫性;
  2. 领导/阶段方向和业务结果;
  3. 客户影响范围;
  4. 是否是其他需求的前置能力或共同底座;
  5. 依赖顺序、技术难度、验证成本和交付风险;
  6. 需求清晰度与可执行性;
  7. 负责人和迭代负载是否存在明显集中。

技术难度影响拆分和排期顺序,不自动降低业务优先级。高价值高难度项可建议先做验证或拆分前置任务,而不是简单后移。

5. Produce a co-planning draft

references/output-contract.md 输出:

  1. 批次主题地图;
  2. 重复、依赖、前置能力和模糊项清单;
  3. 建议的迭代重点与负载观察;
  4. 逐需求当前/建议迭代与当前/建议优先级;
  5. 理由、依赖、风险、缺失信息和置信度;
  6. 本轮最值得用户调整或确认的 1-3 个决策。

26.8.1 这类迭代解释为 2026 年 8 月第 1 周。若迭代日历与此不同,采用用户给出的团队定义。

6. Revise with user direction

用户补充重点方向或澄清需求后:

  • 明确列出哪些建议发生变化及原因;
  • 保留未变化项,不整表重写得难以比较;
  • 更新置信度与剩余风险;
  • 输出新的建议草案,仍不执行外部写入。

Confidence Rules

  • 高:有明确描述、外部承诺或可验证依赖,且建议直接由证据支持。
  • 中:标题和现有字段较清楚,但业务收益、容量或依赖仍有一项关键假设。
  • 低:主要依赖模糊标题推断,或缺少会显著改变排期的事实。

低置信度不等于不建议;它表示用户应优先复核。

Definition of Done

  • 先解释整批需求在解决什么,再进入逐条排期。
  • 重复、依赖、前置能力和模糊项均已显式标记。
  • 问题不超过 1-3 个批次级高价值问题,没有要求团队批量补录大量字段。
  • 信息不足的项仍有临时建议,且理由、缺失信息、风险和置信度完整。
  • 当前值与建议值分开,事实与推断分开。
  • 全程只读,没有修改云效、钉钉或源文件。

Read the full file on GitHub · 123 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. 12d ago First seen · 123 lines · 154 tokens per session scan A 8701a113df86

Subscribe to this mod's changes

stylework-requirement-planning is a skill published in the GitHub repository PANGKAIFENG/ai-product-manager-skills (11 stars, last pushed 12d ago), licensed MIT. It adds 154 tokens to every session and 1,537 once invoked, about $0.0008 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 2026-08-31.