Agent skill

Prompt Optimizer

by aAAaqwq in aAAaqwq/AGI-Super-Team

Evaluate, optimize, and enhance prompts using 58 proven prompting techniques.

MITAuto-check passedAI & LLM Engineering

Install Prompt Optimizer

skills CLI
$ npx skills add aAAaqwq/AGI-Super-Team --skill prompt-optimizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aAAaqwq/AGI-Super-Team prompt-optimizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-optimizer .claude/skills/prompt-optimizer && rm -rf skills-src

Use ~/.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/

Facts

Skill name
prompt-optimizer
GitHub stars
105
Used in
2 other repos
Token cost
~1.7k tokens
SKILL.md length
736 words
Files
5 (incl. references)
Skills in repo
161
Repo updated
First seen
Licence
MIT

At a glance

Evaluate, optimize, and enhance prompts using 58 proven prompting techniques.

  • Works in 5 steps: Load Quality Framework → Perform Quality Assessment → Identify Applicable Techniques → …
  • User asks to improve
  • SKILL.md covers Overview, Quick Start, Evaluation Workflow and Optimization Patterns, plus 4 more sections
  • Calls python3

What it does

Prompt Optimizer is an agent skill from aAAaqwq/AGI-Super-Team. Evaluate, optimize, and enhance prompts using 58 proven prompting techniques. Use when user asks to improve, optimize, or analyze a prompt; when a prompt needs better clarity, specificity, or structure; or when generating prompt variations for different use cases. Covers quality assessment, targeted improvements, and automatic optimization across techniques like CoT, few-shot learning, role-play, and 50+ more.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `.clawhub/origin.json`, `_meta.json` and `references/prompt-techniques.md`).

It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: An installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters. The licence is MIT.

When your agent uses it

  • User asks to improve
  • Analyze a prompt
  • A prompt needs better clarity
  • Generating prompt variations for different use cases

Example prompts

  • “/prompt-optimizer”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Load Quality Framework
  2. Perform Quality Assessment
  3. Identify Applicable Techniques
  4. Generate Optimization Plan
  5. Generate Optimized Prompt

What it can do on your machine

Read from SKILL.md and the folder at commit 331ecd3. 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

    Shell commands in SKILL.md call:

    • python3

    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 1.7k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 736 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~108
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

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 aAAaqwq/AGI-Super-Team at commit 331ecd3, republished under its MIT licence (© aAAaqwq). 736 words, ~1,737 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
Evaluate, optimize, and enhance prompts using 58 proven prompting techniques. Use when user asks to improve, optimize, or analyze a prompt; when a prompt needs better clarity, specificity, or structure; or when generating prompt variations for different use cases. Covers quality assessment, targeted improvements, and automatic optimization across techniques like CoT, few-shot learning, role-play, and 50+ more.

Prompt Optimizer

Overview

Evaluate prompt quality, provide targeted improvement suggestions, and generate optimized versions using 58 proven prompting techniques. This skill systematically analyzes prompts across multiple quality dimensions and applies evidence-based optimization patterns.

Quick Start

For most optimization tasks, follow this workflow:

  1. Analyze the current prompt - Read and understand what the user wants to achieve
  2. Evaluate quality - Assess across clarity, specificity, structure, completeness
  3. Load relevant techniques - Read references/prompt-techniques.md for applicable methods
  4. Generate suggestions - Use evaluation results and techniques to propose improvements
  5. Create optimized version - Apply chosen techniques to produce an enhanced prompt

Evaluation Workflow

When a user asks to optimize or evaluate a prompt:

Step 1: Load Quality Framework

Read references/quality-framework.md to understand evaluation dimensions:

  • Clarity - Is the prompt unambiguous and easy to understand?
  • Specificity - Are requirements and constraints clearly defined?
  • Structure - Does it follow logical organization?
  • Completeness - Does it include all necessary context and instructions?
  • Tone - Is the voice appropriate for the task?
  • Constraints - Are boundaries and limitations clear?
Step 2: Perform Quality Assessment

Evaluate the prompt against each dimension:

For each quality dimension:
1. Identify strengths (what works well)
2. Identify weaknesses (what's missing or unclear)
3. Rate quality (Poor/Fair/Good/Excellent)
4. Note specific improvement opportunities
Step 3: Identify Applicable Techniques

Load references/prompt-techniques.md and identify techniques that address the identified weaknesses.

Example mapping:

  • Weak: "Be creative" → Apply: Role-play or Creative Persona
  • Weak: "Write an essay" → Apply: Chain of Thought or Step-by-Step
  • Weak: "Summarize this" → Apply: Few-shot Learning with examples
Step 4: Generate Optimization Plan

Create a structured optimization plan:

  1. Priority improvements - High-impact changes that address multiple weaknesses
  2. Optional enhancements - Nice-to-have techniques that boost performance
  3. Technique combinations - Suggest technique pairings for specific use cases
Step 5: Generate Optimized Prompt

Apply the selected techniques to create an improved version:

  • Preserve original intent and requirements
  • Add structure and clarity where missing
  • Embed examples, constraints, or guidance as needed
  • Maintain appropriate tone and voice

Optimization Patterns

For common optimization scenarios, use these proven patterns:

Ambiguous Requests → Structured Breakdown

When prompt lacks clarity:

  1. Add explicit task definition
  2. Break into sub-tasks with numbered steps
  3. Include output format specification
  4. Add completion criteria
Generic Tasks → Technique Enhancement

When prompt is too broad:

  1. Apply relevant technique from references/prompt-techniques.md
  2. Add examples (few-shot) or reasoning steps (CoT)
  3. Include role or persona guidance
  4. Specify evaluation criteria
Missing Context → Scenario Framing

When prompt lacks background:

  1. Add user intent/goal statement
  2. Include target audience specification
  3. Define success metrics
  4. Add relevant constraints or boundaries
Weak Instructions → Actionable Steps

When prompt provides vague guidance:

  1. Convert abstract concepts to concrete actions
  2. Add step-by-step instructions
  3. Include quality checkpoints
  4. Specify expected output format

Script Usage

Quality Evaluation

For consistent, repeatable evaluation:

bash
python3 scripts/evaluate.py "Your prompt here"

This provides:

  • Dimension scores (clarity, specificity, structure, completeness)
  • Overall quality rating
  • Detailed weakness analysis
  • Suggested improvement areas
Show full SKILL.md (289 more words)Show less
Prompt Optimization

For automatic optimization generation:

bash
python3 scripts/optimize.py "Your prompt here" --techniques "few-shot,coT"

This generates:

  • Multiple optimized prompt versions
  • Explanation of applied techniques
  • Comparison with original prompt

Note: Scripts should be used for automation or when you need deterministic results. For complex optimization tasks, use the manual workflow for more nuanced analysis.

Reference Files

references/prompt-techniques.md

Complete catalog of 58 prompting techniques including:

  • Reasoning techniques (CoT, Tree of Thoughts, Decomposition)
  • Context techniques (Few-shot, Self-Consistency, Reflection)
  • Creative techniques (Role-play, Scenario, Persona)
  • Structural techniques (Template, Framework, Checklists)
  • And 50+ more with usage examples

Load this when you need to identify applicable techniques for a specific optimization task.

references/quality-framework.md

Detailed evaluation framework with:

  • Dimension-specific criteria and rubrics
  • Scoring guidelines
  • Common anti-patterns to avoid
  • Quality benchmarks for different prompt types

Load this before any evaluation task to ensure consistent assessment.

references/optimization-patterns.md

Collection of proven optimization patterns including:

  • Pattern → Technique mappings
  • Before/after examples
  • Technique combination guidelines
  • Use-case specific templates

Load this when optimizing common prompt types (essays, code generation, analysis, etc.).

Best Practices

  1. Preserve user intent - Never change what the user wants, only how they ask for it
  2. Add incrementally - Apply one technique at a time and evaluate impact
  3. Test iteratively - After optimization, test the prompt and refine further if needed
  4. Document choices - Explain which techniques you applied and why
  5. Provide options - Offer multiple optimization versions when appropriate

When This Skill Should Trigger

This skill should be activated when:

  • User explicitly asks to "optimize," "improve," or "evaluate" a prompt
  • User asks if a prompt is "good" or "clear"
  • User wants to "fix" or "enhance" a prompt that isn't working well
  • User requests "better versions" of a prompt
  • User asks about prompt engineering techniques or best practices
  • User wants to analyze why a prompt is producing poor results

© aAAaqwq, 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 skills/prompt-optimizer of aAAaqwq/AGI-Super-Team.

  • SKILL.md
  • .clawhub/origin.json
  • _meta.json
  • references/prompt-techniques.md
  • references/quality-framework.md

Open the folder on GitHubat commit 331ecd3

Used in 2 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in aAAaqwq/AGI-Super-Team, which our catalogue first saw on October 7, 2026.

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.

Prompt Optimizer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Prompt Optimizer this skillaAAaqwq/AGI-Super-Team1052 repos~1.7kAutomated safety check: PassMIT
Prompt Improverseverity1/claude-code-prompt-improver1.9k2 repos~1.7kAutomated safety check: PassMIT
Prompt Engineering Patternsynulihao/AgentSkillOS61714 repos~1.7kAutomated safety check: PassNone
Patch CreationPiebald-AI/tweakcc2.5k—~1.6kAutomated safety check: PassMIT
LLM Application DevMoizIbnYousaf/ai-agent-skills1.1k2 repos~1.3kAutomated safety check: PassMIT
Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit2594 repos~1.4kAutomated safety check: PassCustom licence

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Questions about Prompt Optimizer

What does Prompt Optimizer do?

Evaluate, optimize, and enhance prompts using 58 proven prompting techniques. Prompt Optimizer is an agent skill from aAAaqwq/AGI-Super-Team. Evaluate, optimize, and enhance prompts using 58 proven prompting techniques.

When should I use Prompt Optimizer?

Prompt Optimizer fits situations like: user asks to improve; analyze a prompt; A prompt needs better clarity; generating prompt variations for different use cases.

How do I install Prompt Optimizer in Claude Code?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill prompt-optimizer -a claude-code`. Or copy the skill folder (skills/prompt-optimizer in aAAaqwq/AGI-Super-Team) 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 aAAaqwq/AGI-Super-Team --skill prompt-optimizer -a codex`. Or copy the skill folder (skills/prompt-optimizer in aAAaqwq/AGI-Super-Team) 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 aAAaqwq/AGI-Super-Team --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?

Going by SKILL.md and its folder, Prompt Optimizer needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

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 1.7k tokens (SKILL.md is roughly 6.9k 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 8.9k 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?

aAAaqwq (a GitHub user) maintains it in aAAaqwq/AGI-Super-Team, which has 105 GitHub stars. The repository holds 161 skills in this directory. The repository was last updated on September 27, 2026.

Source: aAAaqwq/AGI-Super-Team on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.