prompt-optimizer

affaan-m/everything-claude-code · updated Jun 1, 2026

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$npx skills add https://github.com/affaan-m/everything-claude-code --skill prompt-optimizer
0 commentsdiscussion
summary

Analyze a draft prompt, critique it, match it to ECC ecosystem components,

  • and output a complete optimized prompt the user can paste and run.
skill.md

Prompt Optimizer

Analyze a draft prompt, critique it, match it to ECC ecosystem components, and output a complete optimized prompt the user can paste and run.

When to Use

  • User says "optimize this prompt", "improve my prompt", "rewrite this prompt"
  • User says "help me write a better prompt for..."
  • User says "what's the best way to ask Claude Code to..."
  • User says "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令"
  • User pastes a draft prompt and asks for feedback or enhancement
  • User says "I don't know how to prompt for this"
  • User says "how should I use ECC for..."
  • User explicitly invokes /prompt-optimize

Do Not Use When

  • User wants the task done directly (just execute it)
  • User says "优化代码", "优化性能", "optimize this code", "optimize performance" — these are refactoring tasks, not prompt optimization
  • User is asking about ECC configuration (use configure-ecc instead)
  • User wants a skill inventory (use skill-stocktake instead)
  • User says "just do it" or "直接做"

How It Works

Advisory only — do not execute the user's task.

Do NOT write code, create files, run commands, or take any implementation action. Your ONLY output is an analysis plus an optimized prompt.

If the user says "just do it", "直接做", or "don't optimize, just execute", do not switch into implementation mode inside this skill. Tell the user this skill only produces optimized prompts, and instruct them to make a normal task request if they want execution instead.

Run this 6-phase pipeline sequentially. Present results using the Output Format below.

Analysis Pipeline

Phase 0: Project Detection

Before analyzing the prompt, detect the current project context:

  1. Check if a CLAUDE.md exists in the working directory — read it for project conventions
  2. Detect tech stack from project files:
    • package.json → Node.js / TypeScript / React / Next.js
    • go.mod → Go
    • pyproject.toml / requirements.txt → Python
    • Cargo.toml → Rust
    • build.gradle / pom.xml → Java / Kotlin / Spring Boot
    • Package.swift → Swift
    • Gemfile → Ruby
    • composer.json → PHP
    • *.csproj / *.sln → .NET
    • Makefile / CMakeLists.txt → C / C++
    • cpanfile / Makefile.PL → Perl
  3. Note detected tech stack for use in Phase 3 and Phase 4

If no project files are found (e.g., the prompt is abstract or for a new project), skip detection and flag "tech stack unknown" in Phase 4.

Phase 1: Intent Detection

Classify the user's task into one or more categories:

Category Signal Words Example
New Feature build, create, add, implement, 创建, 实现, 添加 "Build a login page"
Bug Fix fix, broken, not working, error, 修复, 报错 "Fix the auth flow"
Refactor refactor, clean up, restructure, 重构, 整理 "Refactor the API layer"
Research how to, what is, explore, investigate, 怎么, 如何 "How to add SSO"
Testing test, coverage, verify, 测试, 覆盖率 "Add tests for the cart"
Review review, audit, check, 审查, 检查 "Review my PR"
Documentation document, update docs, 文档 "Update the API docs"
Infrastructure deploy, CI, docker, database, 部署, 数据库 "Set up CI/CD pipeline"
Design design, architecture, plan, 设计, 架构 "Design the data model"

Phase 2: Scope Assessment

If Phase 0 detected a project, use codebase size as a signal. Otherwise, estimate from the prompt description alone and mark the estimate as uncertain.

Scope Heuristic Orchestration
TRIVIAL Single file, < 50 lines Direct execution
LOW Single component or module Single command or skill
MEDIUM Multiple components, same domain Command chain + /verify
HIGH Cross-domain, 5+ files /plan first, then phased execution
EPIC Multi-session, multi-PR, architectural shift Use blueprint skill for multi-session plan

Phase 3: ECC Component Matching

Map intent + scope + tech stack (from Phase 0) to specific ECC components.

By Intent Type

Intent Commands Skills Agents
New Feature /plan, /tdd, /code-review, /verify tdd-workflow, verification-loop planner, tdd-guide, code-reviewer
Bug Fix /tdd, /build-fix, /verify tdd-workflow tdd-guide, build-error-resolver
Refactor /refactor-clean, /code-review, /verify verification-loop refactor-cleaner, code-reviewer
Research /plan search-first, iterative-retrieval
Testing /tdd, /e2e, /test-coverage tdd-workflow, e2e-testing tdd-guide, e2e-runner
Review /code-review security-review code-reviewer, security-reviewer
Documentation /update-docs, /update-codemaps doc-updater
Infrastructure /plan, /verify docker-patterns, deployment-patterns, database-migrations architect
Design (MEDIUM-HIGH) /plan planner, architect
Design (EPIC) blueprint (invoke as skill) planner, architect

By Tech Stack

Tech Stack Skills to Add Agent
Python / Django django-patterns, django-tdd, django-security, django-verification, python-patterns, python-testing python-reviewer
Go golang-patterns, golang-testing go-reviewer, go-build-resolver
Spring Boot / Java springboot-patterns, springboot-tdd, springboot-security, springboot-verification, java-coding-standards, jpa-patterns code-reviewer
Kotlin / Android kotlin-coroutines-flows, compose-multiplatform-patterns, android-clean-architecture kotlin-reviewer
TypeScript / React frontend-patterns, backend-patterns, coding-standards code-reviewer
Swift / iOS swiftui-patterns, swift-concurrency-6-2, swift-actor-persistence, swift-protocol-di-testing code-reviewer
PostgreSQL postgres-patterns, database-migrations database-reviewer
Perl perl-patterns, perl-testing, perl-security code-reviewer
C++ cpp-coding-standards, cpp-testing code-reviewer
Other / Unlisted coding-standards (universal) code-reviewer

Phase 4: Missing Context Detection

Scan the prompt for missing critical information. Check each item and mark whether Phase 0 auto-detected it or the user must supply it:

  • Tech stack — Detected in Phase 0, or must user specify?
  • Target scope — Files, directories, or modules mentioned?
  • Acceptance criteria — How to know the task is done?
  • Error handling — Edge cases and failure modes addressed?
  • Security requirements — Auth, input validation, secrets?
  • Testing expectations — Unit, integration, E2E?
  • Performance constraints — Load, latency, resource limits?
  • UI/UX requirements — Design specs, responsive, a11y? (if frontend)
  • Database changes — Schema, migrations, indexes? (if data layer)
  • Existing patterns — Reference files or conventions to follow?
  • Scope boundaries — What NOT to do?

If 3+ critical items are missing, ask the user up to 3 clarification questions before generating the optimized prompt. Then incorporate the answers into the optimized prompt.

Phase 5: Workflow & Model Recommendation

Determine where this prompt sits in the development lifecycle:

Research → Plan → Implement (TDD) → Review → Verify → Commit

For MEDIUM+ tasks, always start with /plan. For EPIC tasks, use blueprint skill.

Model recommendation (include in output):

Scope Recommended Model Rationale
TRIVIAL-LOW Sonnet 4.6 Fast, cost-efficient for simple tasks
MEDIUM Sonnet 4.6 Best coding model for standard work
HIGH Sonnet 4.6 (main) + Opus 4.6 (planning) Opus for architecture, Sonnet for implementation
EPIC Opus 4.6 (blueprint) + Sonnet 4.6 (execution) Deep reasoning for multi-session planning

Multi-prompt splitting (for HIGH/EPIC scope):

For tasks that exceed a single session, split into sequential prompts:

  • Prompt 1: Research + Plan (use search-first skill, then /plan)
  • Prompt 2-N: Implement one phase per prompt (each ends with /verify)
  • Final Prompt: Integration test + /code-review across all phases
  • Use /save-session and /resume-session to preserve context between sessions

Output Format

Present your analysis in this exact structure. Respond in the same language as the user's input.

Section 1: Prompt Diagnosis

Strengths: List what the original prompt does well.

Issues:

Issue Impact Suggested Fix
(problem) (consequence) (how to fix)

Needs Clarification: Numbered list of questions the user should answer. If Phase 0 auto-detected the answer, state it instead of asking.

Section 2: Recommended ECC Components

Type Component Purpose
Command /plan Plan architecture before coding
Skill tdd-workflow TDD methodology guidance
Agent code-reviewer Post-implementation review
Model Sonnet 4.6 Recommended for this scope

Section 3: Optimized Prompt — Full Version

Present the complete optimized prompt inside a single fenced code block. The prompt must be self-contained and ready to copy-paste. Include:

  • Clear task description with context
  • Tech stack (detected or specified)
  • /command invocations at the right workflow stages
  • Acceptance criteria
  • Verification steps
  • Scope boundaries (what NOT to do)

For items that reference blueprint, write: "Use the blueprint skill to..." (not /blueprint, since blueprint is a skill, not a command).

Section 4: Optimized Prompt — Quick Version

A compact version for experienced ECC users. Vary by intent type:

Intent Quick Pattern
New Feature /plan [feature]. /tdd to implement. /code-review. /verify.
Bug Fix /tdd — write failing test for [bug]. Fix to green. /verify.
Refactor /refactor-clean [scope]. /code-review. /verify.
Research Use search-first skill for [topic]. /plan based on findings.
Testing /tdd [module]. /e2e for critical flows. /test-coverage.
Review /code-review. Then use security-reviewer agent.
Docs /update-docs. /update-codemaps.
EPIC Use blueprint skill for "[objective]". Execute phases with /verify gates.

Section 5: Enhancement Rationale

Enhancement Reason
(what was added) (why it matters)

Footer

Not what you need? Tell me what to adjust, or make a normal task request if you want execution instead of prompt optimization.


Examples

Trigger Examples

  • "Optimize this prompt for ECC"
  • "Rewrite this prompt so Claude Code uses the right commands"
  • "帮我优化这个指令"
  • "How should I prompt ECC for this task?"

Example 1: Vague Chinese Prompt (Project Detected)

User input:

帮我写一个用户登录页面

Phase 0 detects: package.json with Next.js 15, TypeScript, Tailwind CSS

Optimized Prompt (Full):

使用项目现有技术栈(Next.js 15 + TypeScript + Tailwind CSS)实现用户登录页面。

技术要求:
- 沿用项目现有的组件结构和路由约定
- 表单验证使用项目中已有的验证方案(检查是否已用 Zod/Yup/其他)
- 认证方式:沿用项目现有认证方案(如无,默认 JWT)
- 包含:邮箱/密码登录表单、表单验证、错误提示、加载状态、响应式布局

工作流:
1. /plan 先规划组件结构和认证流程,参考现有页面的模式
2. /tdd 测试先行:编写登录表单的单元测试和认证流程的集成测试
3. 实现登录页面和认证逻辑
4. /code-review 审查实现
5. /verify 验证所有测试通过且页面正常渲染

安全要求:
- 密码不明文传输
- 防止暴力破解(rate limiting)
- XSS 防护
- CSRF token

验收标准:
- 所有测试通过,覆盖率 80%+
- 页面在移动端和桌面端正常渲染
- 登录成功跳转到 dashboard,失败显示错误信息

不要做:
- 不要实现注册页面
- 不要实现忘记密码功能
- 不要修改现有的路由结构

Example 2: Moderate English Prompt

User input:

Add a REST API endpoint for user profile updates with validation

Phase 0 detects: go.mod with Go 1.22, Chi router

Optimized Prompt (Full):

Add a REST API endpoint for user profile updates (PATCH /api/users/:id).

Tech stack: Go 1.22 + Chi router (detected from project)

Requirements:
- PATCH /api/users/:id — partial update of user profile
- Input validation for fields: name, email, avatar_url, bio
- Auth: require valid token, users can only update own profile
- Return 200 with updated user on success
- Return 400 with validation errors on invalid input
- Return 401/403 for auth failures
- Follow existing API patterns in the codebase

Workflow:
1. /plan the endpoint structure, middleware chain, and validation logic
2. /tdd — write table-driven tests for success, validation failure, auth failure, not-found
3. Implement following existing handler patterns
4. /go-review
5. /verify — run full test suite, confirm no regressions

Do not:
- Modify existing endpoints
- Change the database schema (use existing user table)
- Add new dependencies without checking existing ones first (use search-first skill)

Example 3: EPIC Project

User input:

Migrate our monolith to microservices

Optimized Prompt (Full):

Use the blueprint skill to plan: "Migrate monolith to microservices architecture"

Before executing, answer these questions in the blueprint:
1. Which domain boundaries exist in the current monolith?
2. Which service should be extracted first (lowest coupling)?
3. Communication pattern: REST APIs, gRPC, or event-driven (Kafka/RabbitMQ)?
4. Database strategy: shared DB initially or database-per-service from start?
5. Deployment target: Kubernetes, Docker Compose, or serverless?

The blueprint should produce phases like:
- Phase 1: Identify service boundaries and create domain map
- Phase 2: Set up infrastructure (API gateway, service mesh, CI/CD per service)
- Phase 3: Extract first service (strangler fig pattern)
- Phase 4: Verify with integration tests, then extract next service
- Phase N: Decommission monolith

Each phase = 1 PR, with /verify gates between phases.
Use /save-session between phases. Use /resume-session to continue.
Use git worktrees for parallel service extraction when dependencies allow.

Recommended: Opus 4.6 for blueprint planning, Sonnet 4.6 for phase execution.

Related Components

Component When to Reference
configure-ecc User hasn't set up ECC yet
skill-stocktake Audit which components are installed (use instead of hardcoded catalog)
search-first Research phase in optimized prompts
blueprint EPIC-scope optimized prompts (invoke as skill, not command)
strategic-compact Long session context management
cost-aware-llm-pipeline Token optimization recommendations
how to use prompt-optimizer

How to use prompt-optimizer on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add prompt-optimizer
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/affaan-m/everything-claude-code --skill prompt-optimizer

The skills CLI fetches prompt-optimizer from GitHub repository affaan-m/everything-claude-code and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/prompt-optimizer

Reload or restart Cursor to activate prompt-optimizer. Access the skill through slash commands (e.g., /prompt-optimizer) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.867 reviews
  • Ganesh Mohane· Dec 20, 2024

    Solid pick for teams standardizing on skills: prompt-optimizer is focused, and the summary matches what you get after install.

  • Sakura Brown· Dec 20, 2024

    Useful defaults in prompt-optimizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kaira Kapoor· Dec 16, 2024

    prompt-optimizer has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kofi Thomas· Dec 12, 2024

    I recommend prompt-optimizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Xiao Lopez· Dec 12, 2024

    Keeps context tight: prompt-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Advait Malhotra· Dec 12, 2024

    prompt-optimizer reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Mia Martin· Dec 8, 2024

    Registry listing for prompt-optimizer matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Advait Martin· Nov 27, 2024

    Useful defaults in prompt-optimizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Michael Martin· Nov 15, 2024

    prompt-optimizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Rahul Santra· Nov 11, 2024

    We added prompt-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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