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prompt-optimizer

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prompt-optimizer 是什麼?

prompt-optimizer is a Claude Code agent skill that >-.

相容平台Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/prompt-optimizer

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說明文件

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 (then check for quarkus in build file → Quarkus, or spring-boot → 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:

CategorySignal WordsExample
New Featurebuild, create, add, implement, 创建, 实现, 添加"Build a login page"
Bug Fixfix, broken, not working, error, 修复, 报错"Fix the auth flow"
Refactorrefactor, clean up, restructure, 重构, 整理"Refactor the API layer"
Researchhow to, what is, explore, investigate, 怎么, 如何"How to add SSO"
Testingtest, coverage, verify, 测试, 覆盖率"Add tests for the cart"
Reviewreview, audit, check, 审查, 检查"Review my PR"
Documentationdocument, update docs, 文档"Update the API docs"
Infrastructuredeploy, CI, docker, database, 部署, 数据库"Set up CI/CD pipeline"
Designdesign, 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.

ScopeHeuristicOrchestration
TRIVIALSingle file, < 50 linesDirect execution
LOWSingle component or moduleSingle command or skill
MEDIUMMultiple components, same domainCommand chain + /verify
HIGHCross-domain, 5+ files/plan first, then phased execution
EPICMulti-session, multi-PR, architectural shiftUse 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

IntentCommandsSkillsAgents
New Feature/plan, /tdd, /code-review, /verifytdd-workflow, verification-loopplanner, tdd-guide, code-reviewer
Bug Fix/tdd, /build-fix, /verifytdd-workflowtdd-guide, build-error-resolver
Refactor/refactor-clean, /code-review, /verifyverification-looprefactor-cleaner, code-reviewer
Research/plansearch-first, iterative-retrieval
Testing/tdd, /e2e, /test-coveragetdd-workflow, e2e-testingtdd-guide, e2e-runner
Review/code-reviewsecurity-reviewcode-reviewer, security-reviewer
Documentation/update-docs, /update-codemapsdoc-updater
Infrastructure/plan, /verifydocker-patterns, deployment-patterns, database-migrationsarchitect
Design (MEDIUM-HIGH)/planplanner, architect
Design (EPIC)blueprint (invoke as skill)planner, architect

By Tech Stack

Tech StackSkills to AddAgent
Python / Djangodjango-patterns, django-tdd, django-security, django-verification, python-patterns, python-testingpython-reviewer
Gogolang-patterns, golang-testinggo-reviewer, go-build-resolver
Spring Boot / Javaspringboot-patterns, springboot-tdd, springboot-security, springboot-verification, java-coding-standards, jpa-patternsjava-reviewer
Quarkus / Javaquarkus-patterns, quarkus-tdd, quarkus-security, quarkus-verification, java-coding-standards, jpa-patternsjava-reviewer
Kotlin / Androidkotlin-coroutines-flows, compose-multiplatform-patterns, android-clean-architecturekotlin-reviewer
TypeScript / Reactfrontend-patterns, backend-patterns, coding-standardscode-reviewer
Swift / iOSswiftui-patterns, swift-concurrency-6-2, swift-actor-persistence, swift-protocol-di-testingcode-reviewer
PostgreSQLpostgres-patterns, database-migrationsdatabase-reviewer
Perlperl-patterns, perl-testing, perl-securitycode-reviewer
C++cpp-coding-standards, cpp-testingcode-reviewer
Other / Unlistedcoding-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):

ScopeRecommended ModelRationale
TRIVIAL-LOWSonnet 5Fast, cost-efficient for simple tasks
MEDIUMSonnet 5Best coding model for standard work
HIGHSonnet 5 (main) + Opus 5 (planning)Opus for architecture, Sonnet for implementation
EPICOpus 5 (blueprint) + Sonnet 5 (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:

IssueImpactSuggested 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

TypeComponentPurpose
Command/planPlan architecture before coding
Skilltdd-workflowTDD methodology guidance
Agentcode-reviewerPost-implementation review
ModelSonnet 5Recommended 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:

IntentQuick 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.
ResearchUse 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.
EPICUse blueprint skill for "[objective]". Execute phases with /verify gates.

Section 5: Enhancement Rationale

EnhancementReason
(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 5 for blueprint planning, Sonnet 5 for phase execution.

Related Components

ComponentWhen to Reference
configure-eccUser hasn't set up ECC yet
skill-stocktakeAudit which components are installed (use instead of hardcoded catalog)
search-firstResearch phase in optimized prompts
blueprintEPIC-scope optimized prompts (invoke as skill, not command)
strategic-compactLong session context management
cost-aware-llm-pipelineToken optimization recommendations

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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