Prompt Improver
severity1/claude-code-prompt-improver
This skill enriches vague prompts with targeted research and clarification before execution.
Analyze draft prompts, detect intent and missing context, match ECC commands, skills, and agents, and output a ready-to-paste optimized prompt with diagnosis and rationale — advisory only, never…
Install Prompt Optimizer
$ npx skills add affaan-m/ECC --skill prompt-optimizer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-optimizer .claude/skills/prompt-optimizer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/prompt-optimizer into .claude/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/affaan-m/ECC/tree/main/skills/prompt-optimizerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add affaan-m/ECC --skill prompt-optimizer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/prompt-optimizer .agents/skills/prompt-optimizer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/prompt-optimizer into .agents/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill prompt-optimizer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/prompt-optimizer .cursor/skills/prompt-optimizer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/prompt-optimizer into .cursor/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/affaan-m/ECC.git --path skills/prompt-optimizer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add affaan-m/ECC --skill prompt-optimizer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/prompt-optimizer .gemini/skills/prompt-optimizer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/prompt-optimizer into .gemini/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install affaan-m/ECC prompt-optimizerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add affaan-m/ECC --skill prompt-optimizer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/prompt-optimizer .github/skills/prompt-optimizer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/prompt-optimizer into .github/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill prompt-optimizer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/prompt-optimizer .opencode/skills/prompt-optimizer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/prompt-optimizer into .opencode/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
prompt-optimizer- GitHub stars
- 275k
- Used in
- 5 other repos
- Token cost
- ~3.8k tokens
- SKILL.md length
- 1,404 words
- Files
- 1
- Skills in repo
- 645
- Repo updated
- First seen
- Licence
- MIT
At a glance
Analyze draft prompts, detect intent and missing context, match ECC commands, skills, and agents, and output a ready-to-paste optimized prompt with diagnosis and rationale — advisory only, never…
- Works in 6 steps: Project Detection → Intent Detection → Scope Assessment → …
- The user says optimize prompt
- SKILL.md covers When to Use, How It Works, Output Format and Examples, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Prompt Optimizer is an agent skill from affaan-m/ECC. Analyze draft prompts, detect intent and missing context, match ECC commands, skills, and agents, and output a ready-to-paste optimized prompt with diagnosis and rationale — advisory only, never executes the task. Use when the user says 'optimize prompt', 'improve my prompt', 'rewrite this prompt', 'help me prompt', 优化prompt, 改进prompt, 怎么写prompt, or 帮我优化这个指令; not for requests to optimize code or performance.
Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.
When your agent uses it
- The user says optimize prompt
- Improve my prompt
- Rewrite this prompt
- Not for requests to optimize code
Example prompts
- “optimize prompt”
- “improve my prompt”
- “rewrite this prompt”
- “/prompt-optimizer”
Requirements
- Python 3
- Node.js
- Docker
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Prompt Optimizer loads about 3.8k tokens when it runs. Until then it costs about 107 tokens; SKILL.md has 1,404 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 1,404 words, ~3,796 tokens.
Download SKILL.mdSave it as
.claude/skills/prompt-optimizer/SKILL.md (or your agent's skills folder).- name
- prompt-optimizer
- description
- Analyze draft prompts, detect intent and missing context, match ECC commands, skills, and agents, and output a ready-to-paste optimized prompt with diagnosis and rationale — advisory only, never executes the task. Use when the user says 'optimize prompt', 'improve my prompt', 'rewrite this prompt', 'help me prompt', 优化prompt, 改进prompt, 怎么写prompt, or 帮我优化这个指令; not for requests to optimize code or performance.
- metadata.origin
- community
- metadata.author
- YannJY02
- metadata.version
- 1.0.0
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-eccinstead) - User wants a skill inventory (use
skill-stocktakeinstead) - 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:
- Check if a
CLAUDE.mdexists in the working directory — read it for project conventions - Detect tech stack from project files:
package.json→ Node.js / TypeScript / React / Next.jsgo.mod→ Gopyproject.toml/requirements.txt→ PythonCargo.toml→ Rustbuild.gradle/pom.xml→ Java / Kotlin (then check forquarkusin build file → Quarkus, orspring-boot→ Spring Boot)Package.swift→ SwiftGemfile→ Rubycomposer.json→ PHP*.csproj/*.sln→ .NETMakefile/CMakeLists.txt→ C / C++cpanfile/Makefile.PL→ Perl
- 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 | java-reviewer |
| Quarkus / Java | quarkus-patterns, quarkus-tdd, quarkus-security, quarkus-verification, java-coding-standards, jpa-patterns | java-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.
Show full SKILL.md (542 more words)Show less
Phase 5: Workflow & Model Recommendation
Determine where this prompt sits in the development lifecycle:
Research → Plan → Implement (TDD) → Review → Verify → CommitFor MEDIUM+ tasks, always start with /plan. For EPIC tasks, use blueprint skill.
Model recommendation (include in output):
| Scope | Recommended Model | Rationale |
|---|---|---|
| TRIVIAL-LOW | Sonnet 5 | Fast, cost-efficient for simple tasks |
| MEDIUM | Sonnet 5 | Best coding model for standard work |
| HIGH | Sonnet 5 (main) + Opus 5 (planning) | Opus for architecture, Sonnet for implementation |
| EPIC | Opus 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:
| 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 5 | 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 validationPhase 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 microservicesOptimized 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
| 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 |
© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/prompt-optimizer of affaan-m/ECC.
Open the folder on GitHubat commit ef648e0
Used in 5 other repositories
We found 10 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 5 other GitHub owners. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.
- loulanyue/awesome-claude-notesskills/prompt-optimizerdocs/tr/skills/prompt-optimizer+4 more
- hashgraph-online/awesome-codex-pluginsplugins/Colin4k1024/tsp/skills/prompt-optimizer
- tpavanipradeep/everything-claude-codeskills/prompt-optimizer
- Sidiora-Labs/centra-llm-agentsprotocol/skills/prompt-optimizer
- templetongroup/radiantskills/prompt-optimizer
Compare with similar skills
Prompt Optimizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Prompt Optimizer this skillaffaan-m/ECC | 275k | 5 repos | ~3.8k | Automated safety check: Pass | MIT | |
| Prompt Improverseverity1/claude-code-prompt-improver | 1.9k | 2 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Prompt Engineering Patternsynulihao/AgentSkillOS | 617 | 15 repos | ~1.7k | Automated safety check: Pass | None | |
| Patch CreationPiebald-AI/tweakcc | 2.5k | — | ~1.6k | Automated safety check: Pass | MIT | |
| LLM Application DevMoizIbnYousaf/ai-agent-skills | 1.1k | 2 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit | 260 | 4 repos | ~1.4k | Automated safety check: Pass | Custom licence |
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Related
Categories
Questions about Prompt Optimizer
What does Prompt Optimizer do?
Analyze draft prompts, detect intent and missing context, match ECC commands, skills, and agents, and output a ready-to-paste optimized prompt with diagnosis and rationale — advisory only, never…. Prompt Optimizer is an agent skill from affaan-m/ECC. Analyze draft prompts, detect intent and missing context, match ECC commands, skills, and agents, and output a ready-to-paste optimized prompt with diagnosis and rationale — advisory only, never executes the task.
When should I use Prompt Optimizer?
Prompt Optimizer fits situations like: the user says optimize prompt; improve my prompt; rewrite this prompt; not for requests to optimize code.
How do I install Prompt Optimizer in Claude Code?
Run `npx skills add affaan-m/ECC --skill prompt-optimizer -a claude-code`. Or copy the skill folder (skills/prompt-optimizer in affaan-m/ECC) into .claude/skills/prompt-optimizer in your project. Claude Code loads it when a task matches its description.
How do I install Prompt Optimizer in Codex?
Run `npx skills add affaan-m/ECC --skill prompt-optimizer -a codex`. Or copy the skill folder (skills/prompt-optimizer in affaan-m/ECC) into .agents/skills/prompt-optimizer in your project. Codex loads it when a task matches its description.
Can I use Prompt Optimizer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill prompt-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-optimizer, .gemini/skills/prompt-optimizer, .github/skills/prompt-optimizer and .opencode/skills/prompt-optimizer in your project.
What does Prompt Optimizer need to run?
SKILL.md names no scripts, command-line tools or credentials: Prompt Optimizer is instructions for the agent only. Our summary lists: Python 3; Node.js; Docker.
Does Prompt Optimizer access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Prompt Optimizer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Prompt Optimizer use?
Prompt Optimizer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Prompt Optimizer use?
About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Prompt Optimizer?
Skills that share tags, products or a category with Prompt Optimizer: Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars), Prompt Engineering Patterns (ynulihao/AgentSkillOS, 617 stars), Patch Creation (Piebald-AI/tweakcc, 2.5k stars) and LLM Application Dev (MoizIbnYousaf/ai-agent-skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Prompt Optimizer?
affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 275,023 GitHub stars. The repository holds 645 skills in this directory. The repository was last updated on October 5, 2026.
Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.