Prompt Improver
severity1/claude-code-prompt-improver
This skill enriches vague prompts with targeted research and clarification before execution.
Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt).
Install Prompt Optimizer
$ npx skills add daymade/claude-code-skills --skill prompt-optimizer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install daymade/claude-code-skills 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/daymade/claude-code-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/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/daymade/claude-code-skills/tree/main/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/daymade/claude-code-skills/tree/main/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 daymade/claude-code-skills --skill prompt-optimizer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install daymade/claude-code-skills prompt-optimizer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/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/daymade/claude-code-skills/tree/main/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 daymade/claude-code-skills --skill prompt-optimizer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install daymade/claude-code-skills prompt-optimizer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/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/daymade/claude-code-skills/tree/main/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/daymade/claude-code-skills.git --path 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 daymade/claude-code-skills --skill prompt-optimizer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install daymade/claude-code-skills prompt-optimizer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/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/daymade/claude-code-skills/tree/main/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 daymade/claude-code-skills 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 daymade/claude-code-skills --skill prompt-optimizer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/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/daymade/claude-code-skills/tree/main/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 daymade/claude-code-skills --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 daymade/claude-code-skills prompt-optimizer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/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/daymade/claude-code-skills/tree/main/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
- 1.4k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 653 words
- Files
- 5 (incl. references)
- Skills in repo
- 103
- Repo updated
- First seen
- Licence
- MIT
At a glance
Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt).
- Works in 6 steps: Analyze Original Requirement → Apply EARS Transformation → Identify Domain Theories → …
- Requirements lack triggers
- SKILL.md covers Overview, When to Use, Six-Step Optimization Workflow and Advanced Techniques, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Prompt Optimizer is an agent skill from daymade/claude-code-skills. Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt). Use when requirements lack triggers or measurable outcomes, or the user asks to "optimize my prompt" / "improve this requirement" / "make this more specific".
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/advanced_techniques.md`, `references/domain_theories.md` and `references/ears_syntax.md`).
It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: Professional Claude Code skills marketplace featuring production-ready skills for enhanced development workflows. The licence is MIT.
When your agent uses it
- Requirements lack triggers
- Measurable outcomes
- The user asks to optimize my prompt / improve this requirement / make this more specific
Example prompts
- “optimize my prompt”
- “improve this requirement”
- “make this more specific”
- “/prompt-optimizer”
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 91bed2b. 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 (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
mp.weixin.qq.com
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 1.9k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 93 tokens; SKILL.md has 653 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 daymade/claude-code-skills at commit 91bed2b, republished under its MIT licence (© daymade). 653 words, ~1,948 tokens.
Download SKILL.mdSave it as
.claude/skills/prompt-optimizer/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- prompt-optimizer
- description
- Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt). Use when requirements lack triggers or measurable outcomes, or the user asks to "optimize my prompt" / "improve this requirement" / "make this more specific".
Prompt Optimizer
Overview
Optimize vague prompts into precise, actionable specifications using EARS (Easy Approach to Requirements Syntax) - a Rolls-Royce methodology for transforming natural language into structured, testable requirements.
Methodology inspired by: This skill's approach to combining EARS with domain theory grounding was inspired by 阿星AI工作室 (A-Xing AI Studio), which demonstrated practical EARS application for prompt enhancement.
Four-layer enhancement process:
- EARS syntax transformation - Convert descriptive language to normative specifications
- Domain theory grounding - Apply relevant industry frameworks (GTD, BJ Fogg, Gestalt, etc.)
- Example extraction - Surface concrete use cases with real data
- Structured prompt generation - Format using Role/Skills/Workflows/Examples/Formats framework
When to Use
Apply when:
- User provides vague feature requests ("build a dashboard", "create a reminder app")
- Requirements lack specific conditions, triggers, or measurable outcomes
- Natural language descriptions need conversion to testable specifications
- User explicitly requests prompt optimization or requirement refinement
Six-Step Optimization Workflow
Step 1: Analyze Original Requirement
Identify weaknesses:
- Overly broad - "Add user authentication" → Missing password requirements, session management
- Missing triggers - "Send notifications" → Missing when/why notifications trigger
- Ambiguous actions - "Make it user-friendly" → No measurable usability criteria
- No constraints - "Process payments" → Missing security, compliance requirements
Step 2: Apply EARS Transformation
Convert requirements to EARS patterns. See references/ears_syntax.md for complete syntax rules.
Five core patterns:
- Ubiquitous:
The system shall <action> - Event-driven:
When <trigger>, the system shall <action> - State-driven:
While <state>, the system shall <action> - Conditional:
If <condition>, the system shall <action> - Unwanted behavior:
If <condition>, the system shall prevent <unwanted action>
Quick example:
Before: "Create a reminder app with task management"
After (EARS):
1. When user creates a task, the system shall guide decomposition into executable sub-tasks
2. When task deadline is within 30 minutes AND user has not started, the system shall send notification with sound alert
3. When user completes a sub-task, the system shall update progress and provide positive feedbackTransformation checklist:
- Identify implicit conditions and make explicit
- Specify triggering events or states
- Use precise action verbs (shall, must, should)
- Add measurable criteria ("within 30 minutes", "at least 8 characters")
- Break compound requirements into atomic statements
- Remove ambiguous language ("user-friendly", "fast")
Step 3: Identify Domain Theories
Match requirements to established frameworks. See references/domain_theories.md for full catalog.
Common domain mappings:
- Productivity → GTD, Pomodoro, Eisenhower Matrix
- Behavior Change → BJ Fogg Model (B=MAT), Atomic Habits
- UX Design → Hick's Law, Fitts's Law, Gestalt Principles
- Security → Zero Trust, Defense in Depth, Privacy by Design
Selection process:
- Identify primary domain from requirement keywords
- Match to 2-4 complementary theories
- Apply theory principles to specific features
- Cite theories in enhanced prompt for credibility
Show full SKILL.md (286 more words)Show less
Step 4: Extract Concrete Examples
Generate specific examples with real data:
- User scenarios: "When user logs in on mobile device..."
- Data examples: "Product: 'Laptop', Price: $999, Stock: 15"
- Workflow examples: "Task: Write report → Sub-tasks: Research (2h), Draft (3h), Edit (1h)"
Examples must be realistic, specific, varied (success/error/edge cases), and testable.
Step 5: Generate Enhanced Prompt
Structure using the standard framework:
# Role
[Specific expert role with domain expertise]
## Skills
- [Core capability 1]
- [Core capability 2]
[List 5-8 skills aligned with domain theories]
## Workflows
1. [Phase 1] - [Key activities]
2. [Phase 2] - [Key activities]
[Complete step-by-step process]
## Examples
[Concrete examples with real data, not placeholders]
## Formats
[Precise output specifications:
- File types, structure requirements
- Design/styling expectations
- Technical constraints
- Deliverable checklist]Quality criteria:
- Role specificity: "Product designer specializing in time management apps" > "Designer"
- Theory grounding: Reference frameworks explicitly
- Actionable workflows: Clear inputs/outputs and decision points
- Concrete examples: Real data, not "Example 1", "Example 2"
- Measurable formats: Specific requirements, not "good design"
Step 6: Present Optimization Results
Output in structured format:
## Original Requirement
[User's vague requirement]
**Identified Issues:**
- [Issue 1: e.g., "Lacks specific trigger conditions"]
- [Issue 2: e.g., "No measurable success criteria"]
## EARS Transformation
[Numbered list of EARS-formatted requirements]
## Domain & Theories
**Primary Domain:** [e.g., Authentication Security]
**Applicable Theories:**
- **[Theory 1]** - [Brief relevance]
- **[Theory 2]** - [Brief relevance]
## Enhanced Prompt
[Complete Role/Skills/Workflows/Examples/Formats prompt]
---
**How to use:**
[Brief guidance on applying the prompt]Advanced Techniques
For complex scenarios, see references/advanced_techniques.md:
- Multi-stakeholder requirements - EARS statements for each user type
- Non-functional requirements - Performance, security, scalability with quantified thresholds
- Complex conditional logic - Nested conditions with boolean operators
Quick Reference
Do's: ✅ Break down compound requirements (one EARS statement per requirement) ✅ Specify measurable criteria (numbers, timeframes, percentages) ✅ Include error/edge cases ✅ Ground in established theories ✅ Use concrete examples with real data
Don'ts: ❌ Avoid vague language ("fast", "user-friendly") ❌ Don't assume implicit knowledge ❌ Don't mix multiple actions in one statement ❌ Don't use placeholders in examples
Resources
Load these reference files as needed:
references/ears_syntax.md- Complete EARS syntax rules, all 5 patterns, transformation guidelines, benefitsreferences/domain_theories.md- 40+ theories mapped to 10 domains (productivity, UX, gamification, learning, e-commerce, security, etc.)references/examples.md- Four complete transformation examples (procrastination app, e-commerce product page, learning dashboard, password reset security) with before/after comparisons and reusable templatereferences/advanced_techniques.md- Multi-stakeholder requirements, non-functional specs, complex conditional logic patterns
When to load references:
- EARS syntax clarification needed →
ears_syntax.md - Domain theory selection requires extensive options →
domain_theories.md - User requests multiple optimization examples →
examples.md - Complex requirements with multiple stakeholders or non-functional specs →
advanced_techniques.md
© daymade, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (references) in prompt-optimizer of daymade/claude-code-skills.
- SKILL.md
- references/advanced_techniques.md
- references/domain_theories.md
- references/ears_syntax.md
- references/examples.md
Open the folder on GitHubat commit 91bed2b
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 skilldaymade/claude-code-skills | 1.4k | — | ~1.9k | 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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Questions about Prompt Optimizer
What does Prompt Optimizer do?
Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt). Prompt Optimizer is an agent skill from daymade/claude-code-skills. Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt).
When should I use Prompt Optimizer?
Prompt Optimizer fits situations like: requirements lack triggers; measurable outcomes; the user asks to optimize my prompt / improve this requirement / make this more specific.
How do I install Prompt Optimizer in Claude Code?
Run `npx skills add daymade/claude-code-skills --skill prompt-optimizer -a claude-code`. Or copy the skill folder (prompt-optimizer in daymade/claude-code-skills) 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 daymade/claude-code-skills --skill prompt-optimizer -a codex`. Or copy the skill folder (prompt-optimizer in daymade/claude-code-skills) 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 daymade/claude-code-skills --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.
Does Prompt Optimizer access the network?
SKILL.md names 1 domain. As links in the text: mp.weixin.qq.com. 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 1.9k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.3k tokens, read only when the agent opens those files.
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?
daymade (a GitHub user) maintains it in daymade/claude-code-skills, which has 1,444 GitHub stars. The repository holds 103 skills in this directory. The repository was last updated on October 8, 2026.
Source: daymade/claude-code-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.