pm-resource

pm-resource is a skill for Claude Code, Codex from konglong87/superPM. It costs 53 tokens per session (3,559 once invoked), scanned A, original, MIT.

A process for dividing engineering and other team capacity among multiple products or projects.

In plain words
What is it for?
Use it to plan staffing, allocate development time, handle resource conflicts, and decide which projects should receive priority.
Why use it?
It helps resolve competing priorities, compare expected return on investment, and expose limits in team size or specialist roles.

Skill for Claude CodeCodex

Part of the super-pm plugin — 55 skills, 1 hook shipped together

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.

agentmods
npx agentmods add skills/konglong87/superpm/pm-resource
Any agent
npx skills add konglong87/superPM --skill pm-resource
Clone the repo
git clone --depth 1 https://github.com/konglong87/superPM

Made for: Claude Code, Codex.

Or install super-pm, the plugin that ships this one along with the rest of its 55 skills, 1 hook.

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 pm-resource

README.md
[![agentmods](https://agentmods.dev/badge/skills/konglong87/superpm/pm-resource.svg)](https://agentmods.dev/skills/konglong87/superpm/pm-resource)
Your own site
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-resource"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-resource.svg" alt="Measured on agentmods" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,559 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00053 $0.03559
Opus 5 $0.00026 $0.01780
Sonnet 5 $0.00011 $0.00712
Haiku 4.5 $0.00005 $0.00356

Measured 5d ago against content hash c160cfd0ec6c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pm-resource 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 5d 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/05-product-strategy/pm-resource/SKILL.md · 473 lines

How it starts

The opening of the file, as written. The whole thing — 473 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Preamble (run first)

bash "$(dirname "${BASH_SOURCE[0]}")/../../check-update.sh" 2>/dev/null || true
# 创建目录
mkdir -p docs/05-产品战略

# 检查是否有产品组合战略作为输入
if [ -f "docs/05-产品战略/产品组合战略.md" ]; then
  echo "✅ 检测到产品组合战略文档,将基于此进行资源分配"
else
  echo "⚠️  未检测到产品组合战略文档,建议先执行 /pm-portfolio"
fi

跨 Agent 交互规则

当流程要求与用户交互时:

  1. 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
  2. 如果当前环境不支持 AskUserQuestion,必须用普通聊天消息提出同样问题。
  3. 一次只问一个问题。
  4. 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
  5. 不得在用户回答前生成文档、写入 docs。
  6. 已有 docs 文件不能替代本轮用户回答。

执行流程

步骤 1: 读取前置数据

优先读取

  • docs/05-产品战略/产品组合战略.md

如果文件不存在,提示用户:

⚠️ 未找到产品组合战略文档

您可以选择: A) 先执行 /pm-portfolio 进行产品组合分析 B) 手动输入产品列表(快速模式)


步骤 2: 盘点可用资源

询问方式:使用 AskUserQuestion 逐个询问


问题 1: 研发团队规模

使用 AskUserQuestion 询问:

请问研发团队总人数?

A) 10人以下(小团队) B) 10-30人(中小团队) C) 30-50人(中型团队) D) 50-100人(大型团队) E) 100人以上(超大型团队) F) 其他(请手动输入)


问题 2: 可投入资源比例

本季度可投入新产品/项目的研发资源比例?

A) <20%(主要维护现有产品) B) 20-40%(小部分资源可投入新项目) C) 40-60%(资源较充足) D) 60-80%(重点投入新项目) E) >80%(全力投入新产品)


问题 3: 关键角色配置

团队中关键角色的配置情况?

逐个询问:

  • 产品经理人数:[数字]
  • 前端工程师人数:[数字]
  • 后端工程师人数:[数字]
  • 测试工程师人数:[数字]
  • 运维工程师人数:[数字]
  • 设计师人数:[数字]

问题 4: 资源约束条件

当前资源分配面临的主要约束是?(可多选)

A) 人力不足 B) 预算限制 C) 时间紧迫 D) 技术能力不足 E) 其他(请手动输入)


步骤 3: 产品资源需求评估

根据步骤1读取的产品列表,逐个评估资源需求:


针对每个产品询问

评估项 1: 资源需求量

{产品名称}本季度需要的研发人力?

A) 极高(需要团队主力投入) B) 高(需要大量人力) C) 中(需要适中人力) D) 低(少量人力即可) E) 维护模式(仅需维护人力)

评估项 2: ROI预期

{产品名称}投入产出比(ROI)预期如何?

A) 极高ROI(投入1元,产出>10元) B) 高ROI(投入1元,产出5-10元) C) 中ROI(投入1元,产出2-5元) D) 低ROI(投入1元,产出<2元) E) 暂无ROI(战略投资期)

评估项 3: 时间紧迫性

{产品名称}的上线时间紧迫性?

A) 极紧迫(必须本季度上线) B) 紧迫(下季度必须上线) C) 适中(半年内上线即可) D) 不紧迫(一年内上线即可) E) 无明确时间要求

评估项 4: 风险等级

{产品名称}的开发风险等级?

A) 高风险(技术难度大、不确定性高) B) 中风险(有一定技术挑战) C) 低风险(技术成熟、风险可控) D) 极低风险(常规开发)


步骤 4: 资源分配决策

决策逻辑

  1. 优先级评分模型

每个产品计算优先级得分:

优先级得分 = ROI得分 × 0.4 + 时间紧迫性得分 × 0.3 + 战略重要性得分 × 0.3 - 风险扣分
  1. 资源分配矩阵
优先级 资源占比 说明
P0(最高) 30-40% 必须完成的核心项目
P1(高) 20-30% 重要项目,保证交付
P2(中) 10-20% 次要项目,按资源情况调整
P3(低) <10% 探索性项目,资源充足时执行

Read the full file on GitHub · 473 lines

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. 5d ago First seen · 473 lines · 53 tokens per session scan A c160cfd0ec6c

Subscribe to this mod's changes

pm-resource is a skill published in the GitHub repository konglong87/superPM (61 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 3,559 once invoked, about $0.0003 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 2026-08-30.

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