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avoid-feature-creep避免特征蔓延

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avoid-feature-creep 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:avoid-feature-creep(避免特征蔓延)
来源仓库:https://github.com/waynesutton/convexskills
仓库路径:skills/avoid-feature-creep
安装命令:
npx skills add https://github.com/waynesutton/convexskills --skill avoid-feature-creep
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/waynesutton/convexskills --skill avoid-feature-creep

简介

通过严格的范围管理,专注于核心问题、加快交付速度并避免产品臃肿。

  • 提供五步决策框架(验证问题、检查一致性、衡量影响、评估复杂性、内部检查),以在构建之前评估每个功能请求
  • 包括范围管理规则:预先定义 MVP、跟踪代码等范围更改、对新请求应用 48 小时等待期,并使用基于预算的权衡来防止添加
  • 提供向利益相关者、高管、用户和AI Agent说“不”的模板,以及记录所有功能选择的范围决策日志
  • 解决人工智能特定的风险:验证人工智能功能是否可以解决实际任务,测量实际使用情况,并将代理建议视为利益相关者的请求,并受到同样严格的要求
  • 包括针对已经臃肿的产品的恢复策略以及用于人工智能辅助开发的会话纪律检查,以防止构建中期范围蔓延

SKILL.md

Avoid Feature Creep for Agents

Stop building features nobody needs. This skill helps you ship products that solve real problems without drowning in unnecessary complexity.

Feature creep kills products. It delays launches, burns budgets, exhausts teams, and creates software nobody wants to use. The most successful products do fewer things well.

The Core Problem

Feature creep is the gradual accumulation of features beyond what your product needs to deliver value. It happens slowly, then all at once.

Warning signs you're in trouble:

  • Release scope keeps growing without clear user value
  • You're copying competitor features without validating need
  • Stakeholders keep adding "just one more thing"
  • The codebase is getting harder to maintain
  • Users complain the product is confusing or bloated
  • You haven't shipped in months

What it costs:

  • Development time on features 80% of users never touch
  • Increased bug surface area
  • Team burnout and context switching
  • Delayed time-to-market
  • Technical debt that compounds
  • User confusion and abandonment

Decision Framework

Before adding ANY feature, run through this checklist:

1. VALIDATE THE PROBLEM
   □ Does this solve a real, validated user pain point?
   □ Have we talked to actual users about this need?
   □ What evidence supports building this?

2. CHECK ALIGNMENT
   □ Does this support the core product vision?
   □ Would this delay our current release?
   □ What are we NOT building if we build this?

3. MEASURE IMPACT
   □ How will we know if this feature succeeds?
   □ What KPIs will change?
   □ Can we quantify the value (time saved, revenue, retention)?

4. ASSESS COMPLEXITY
   □ What's the true cost (build + test + maintain + document)?
   □ Does this add dependencies or technical debt?
   □ Can we ship a simpler version first?

5. FINAL GUT CHECK
   □ Would we delay launch by a month for this feature?
   □ Is this a differentiator or just table stakes?
   □ Would removing this harm the core experience?

If you can't answer YES to questions 1-3 with evidence, do not build the feature.

Scope Management Rules

Rule 1: Define and Defend Your MVP

Write down exactly what "done" means before you start. Document what you're NOT building. Reference this constantly.

## MVP Scope Document Template

### Core Problem
[One sentence describing the user problem]

### Success Criteria
[How we know we've solved it]

### In Scope (v1)
- Feature A: [brief description]
- Feature B: [brief description]

### Explicitly Out of Scope
- Feature X: Deferred to v2
- Feature Y: Will not build unless [condition]
- Feature Z: Not our problem to solve

### Non-Negotiables
- Ship by [date]
- Budget: [hours/dollars]
- Core user: [specific persona]

Rule 2: Use Version Control for Scope

Treat scope like code. Track changes. Require approval for additions.

# Create a scope document and track it
git add SCOPE.md
git commit -m "Initial MVP scope definition"

# Any scope changes require explicit commits
git commit -m "SCOPE CHANGE: Added feature X - approved by [stakeholder] - impact: +2 weeks"

Rule 3: The 48-Hour Rule

When someone requests a new feature, wait 48 hours before adding it to the backlog. Most "urgent" requests feel less urgent after reflection.

Rule 4: Budget-Based Scoping

Every feature has a cost. When something new comes in, something else must go out.

"Yes, we can add that. Which of these three features should we cut to make room?"

Saying No

Saying no to features is a skill. Here are templates:

To stakeholders:

"That's an interesting idea. Based on our user research, it doesn't solve our core user's top three problems. Let's add it to the v2 consideration list and revisit after we validate the MVP."

To executives:

"I understand the value this could bring. If we add this, we'll delay launch by [X weeks] and deprioritize [Y feature]. Here are the trade-offs - which path should we take?"

To users:

"Thanks for the feedback. We're focused on [core problem] right now. I've logged this for future consideration. Can you tell me more about why this would be valuable?"

To yourself:

"Is this scratching my own itch or solving a real user problem? Would I bet the release date on this?"

To AI agents (Claude, Opus, Codex, Ralph, Cursor):

"Stop. Before we add this feature, answer: Does this solve the core user problem we defined at the start of this session? If not, add it to a DEFERRED.md file and stay focused on the current scope."

When working with AI coding agents:

  • State your scope constraints at the start of every session
  • Agents will suggest improvements. Most are out of scope.
  • Treat agent suggestions like stakeholder requests: apply the 48-hour rule
  • If an agent keeps pushing a feature, ask "Why?" three times to find the real need

AI-Specific Guidelines

When building AI-powered products, feature creep has extra risks:

AI Feature Creep Red Flags:

  • Adding AI because "everyone else is"
  • Building AI summaries without validating users want them
  • Multiple AI features without clear differentiation
  • AI capabilities that don't connect to core user workflows

AI Feature Discipline:

  1. One AI feature at a time
  2. Validate the use case with users first
  3. Measure actual usage, not just availability
  4. Question: "Does the AI make the core task faster or better?"

Before adding any AI feature, answer:

  • What specific task does this automate?
  • How is this better than the non-AI alternative?
  • What happens when the AI is wrong?
  • Can we ship without this AI feature?

Backlog Hygiene

A messy backlog enables feature creep. Clean it ruthlessly.

Monthly Backlog Audit:

For each item older than 30 days:
1. Has anyone asked about this since it was added?
2. Does it still align with current product vision?
3. If we never built this, would anyone notice?

If the answer to all three is "no" → Delete it.

Priority Framework (MoSCoW):

  • Must Have: Product doesn't work without it
  • Should Have: Important but not critical for launch
  • Could Have: Nice but can wait
  • Won't Have: Explicitly out of scope

Be honest: Most "Should Haves" are actually "Could Haves" in disguise.

AI Session Discipline

Session Start Check: Before coding with any AI assistant (Claude, Cursor, OpenCode), state:

  • What specific feature you're building
  • What's explicitly out of scope for this session
  • When you'll stop and ship

Mid-Session Check: Every 30-60 minutes, ask your AI: "Are we building the right thing today, or are we adding scope?"

If the answer is "adding scope," stop. Commit what you have. Start fresh.

Session End Check: Before closing an AI coding session:

  • What did we actually build vs. what we planned?
  • Did scope expand? Why?
  • What should we defer to the next session?

Daily AI Check: At the end of each day working with AI assistants:

1. Features completed today: [list]
2. Scope additions today: [list]
3. Was each addition validated? [yes/no]
4. Tomorrow's focus: [single item]

Sprint Planning Guard Rails:

  • No new features mid-sprint without removing something
  • Capacity for bug fixes and debt paydown (20% minimum)
  • Clear definition of done for each item

Stakeholder Management: Create a single source of truth for scope decisions:

## Scope Decision Log

| Date | Request | Source | Decision | Rationale | Trade-off |
|------|---------|--------|----------|-----------|-----------|
| 2025-01-15 | Add export to PDF | PM | Deferred v2 | Not core to MVP | Would delay launch 2 weeks |
| 2025-01-16 | Dark mode | User feedback | Approved | User research shows demand | Removed social sharing |
| 2025-01-17 | Add caching layer | Claude | Deferred | Premature optimization | Stay focused on core feature |
| 2025-01-17 | Refactor to hooks | Cursor | Rejected | Works fine as is | Technical scope creep |

Agents as Stakeholders: AI coding agents are now stakeholders in your project. They have opinions. They make suggestions. Treat them like any other stakeholder:

  • Log agent suggestions in your scope decision log with the agent name as source
  • Apply the same rigor you would to a PM or executive request
  • Agents optimize for different things (code quality, patterns, completeness) than you might need right now
  • "The agent suggested it" is not a valid reason to add a feature

Common agent-driven scope creep patterns:

  • "Let me also add error handling for edge cases you haven't hit yet"
  • "This would be cleaner with a refactor"
  • "You should probably add tests for this"
  • "Let me add TypeScript types for these additional scenarios"

Each of these might be good ideas. None of them are your current scope unless you decide they are.

Recovery: You're Already Bloated

If feature creep has already happened:

Step 1: Audit Current Features

  • List every feature
  • Check usage data for each
  • Identify features with <5% engagement

Step 2: Categorize

  • Core: Users can't accomplish their goal without it
  • Supporting: Makes core better
  • Peripheral: Nice but not necessary
  • Bloat: Nobody uses it

Step 3: Remove or Hide

  • Deprecate bloat with warning period
  • Move peripheral features behind advanced settings
  • Communicate changes clearly to users

Step 4: Prevent Recurrence

  • Add feature creep checks to your PR/code review process
  • Require usage metrics before features graduate from beta
  • Build feature flags so you can easily remove experiments

Quick Reference Commands

When reviewing any feature request, ask:

1. "What user problem does this solve?"
2. "What's the smallest version we could ship?"
3. "What are we NOT building to make room for this?"
4. "How will we measure success?"
5. "What happens if we never build this?"

If you can't answer these clearly → Do not proceed.

The Golden Rule

Ship something small that works. Then iterate based on real usage data.

Users don't remember features. They remember whether your product solved their problem.

Every feature you don't build is:

  • Time you get back
  • Bugs you don't have to fix
  • Documentation you don't have to write
  • Code you don't have to maintain
  • Confusion you don't add

The best products aren't the ones with the most features. They're the ones that do the right things exceptionally well.


*"Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away."* - Antoine de Saint-Exupéry

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