Automatically enhances prompts using AI learning patterns
Intelligent Prompt Optimizer is an advanced meta-prompt engineering and alignment optimization skill on EasyClaw. It automates the process of refining, testing, and standardizing AI prompts. It applies established structural frameworks to convert vague drafts into high-performance, structured templates, while incorporating active user-rating feedback loops to help the skill self-improve over repeated runs.
The skill is built for developers building AI-driven integrations, creators standardizing their writing templates, business analysts automating data reporting pipelines, and power users looking to maximize language model reasoning and formatting accuracy.
The expected outcome is a structured, production-ready Prompt Template Brief: featuring clear system roles, dynamic variable inputs, explicit output formatting constraints, and self-improving parameter notes based on past run ratings.
1. Analyze draft prompt and context. Paste your rough prompt draft (e.g., "help me write an email") or describe your target automation task.
2. Structural prompt engineering. The skill restructures your draft: defining clear System Roles, establishing detailed context anchors, injecting dynamic input variables (using double-curly brackets like `{{variable_name}}`), and setting strict output constraints (e.g., JSON schema limits).
3. Template compile and delivery. It delivers a modular, reusable prompt template, accompanied by a brief explanation of what improvements were made to the logic, ready to copy into your scripts.
4. Rating and feedback log. To run the self-improving loop, submit a performance score (between 0 and 1, such as "feedback: 0.8") after running the template. The skill writes the rating, your feedback note, and the target task type directly to its local database history.
5. Dynamic calibration. Over repeated runs, the optimizer cross-references its feedback history for similar tasks, adjusting its generation parameters (like temperature or structuring rules) to continuously raise output quality.
- Meta-Prompt refiner: Converts vague, 1-sentence inputs into robust, structured system prompts.
- Dynamic template builder: Structures reusable templates with clear variables and rules.
- Self-improving feedback loop: Logs user ratings (0 to 1) to calibrate future generations automatically.
- Strict output constraint setup: Enforces rigid JSON, Markdown, or XML output formats.
- Prompt hygiene auditing: Identifies and resolves common prompt flaws (such as ambiguity or role-conflicts).
- Offline database writes: Logs prompt histories and ratings locally to protect data security.
1. Refining a vague email prompt for B2B sales
A user wants a prompt to write cold emails. Their draft is: "write a cold email for a lead gen tool." The skill refines the draft into a professional system prompt: defining a B2B SaaS copywriter persona, structuring the input variables (prospect company, pain point), setting a strict length limit (under 150 words), and applying the PAS (Problem-Agitation-Solution) copywriting framework, raising lead response rates.
2. Building a reusable weekly data report template
An analyst wants to automate weekly database reports. The skill structures a robust, reusable prompt template: outlining exactly where to paste raw SQL tables, setting up custom markdown table structures, defining how to isolate statistical outliers, and mandating a 3-line executive summary at the top, ensuring consistent, high-yield reports weekly.
3. Calibrating prompt parameters via user feedback
A developer runs an automated code-refining prompt. The output is solid but slightly too verbose. They type: "feedback: 0.7 - too wordy." The skill logs the score and note in its local database. The next time the developer requests a code-refining template, the optimizer automatically adjusts its guidelines, mandating shorter, direct code comments, self-improving to a 0.9 score.
4. Creating a highly structured JSON extractor prompt
A software team wants to extract user details from raw text emails and require a strict, parsable JSON format. The skill designs a JSON-enforcer prompt: specifying the exact JSON keys, including a few-shot example, and applying strict system constraints forbidding markdown backticks or conversational filler text, preventing API parsing crashes.
5. Correcting conflicting roles in complex prompts
A writer has a complex prompt that frequently fails because the AI gets confused between different instructions. The optimizer audits the draft, resolves the role conflicts, and reorganizes the instructions into a clear, hierarchical Markdown structure with explicit priority rankings, stabilizing the model's output.
A developer wants to optimize a prompt to generate API documentation.
1. They open EasyClaw and activate Intelligent Prompt Optimizer.
2. They run: *"Refine Prompt: Write me a prompt to document Node.js Express APIs."*
3. The skill restructures the draft, applies professional system-prompt templates, and writes the brief.
4. It outputs the structured Prompt Template:
- System Role: Senior Node.js API Architect.
- Context & Constraints: Document endpoints using standard JSDoc formatting, include raw request/response JSON schemas, prohibit introductory text.
- Variables: `{{route_code}}`, `{{method}}`, `{{description}}`.
5. The developer copies the structured prompt template into their integration files.
Prompt template designed and compiled in under 30 seconds.
Auto — Interception - Intelligently detects when prompt optimization is needed
Learning — Based Improvement - Uses embedding-indexed history for continuous enhancement
Multi — Pattern Support - Applies APE, OPRO, DSPy optimization techniques
Performance Tracking — Records feedback to improve future optimizations
Interception - Intelligently detects when prompt optimization is needed
Based Improvement - Uses embedding-indexed history for continuous enhancement
Pattern Support - Applies APE, OPRO, DSPy optimization techniques
Records feedback to improve future optimizations
Meta-prompting is using a language model to design, structure, and optimize another prompt. It applies established computational and linguistic rules to convert vague human intent into highly precise system instructions that models understand best.
The skill writes your performance ratings (0 to 1) and qualitative notes directly to a local JSON database. Over repeated runs, the optimization engine analyzes this feedback history to identify what parameters (such as temperature, role, or formatting rules) deliver the highest scores, automatically adjusting future templates.
Yes. The generated prompts can include rigid JSON schema definitions and few-shot formatting examples, programmatically forcing the model to return only valid, parsable JSON.
No. The core prompt refining, template building, and feedback logging are executed entirely locally in your workspace container, requiring no external paid API subscriptions.
Yes. The skill supports multilingual prompt engineering. You can input drafts in Chinese or English and request the final system prompts and variable templates delivered in either language.
Few-shot prompting is a technique where you provide the model with a few examples of target input and output directly in the prompt text, which is highly effective at teaching complex formatting or reasoning rules.
Paste your failing prompt and the incorrect output. The skill will run a diagnostic audit — identifying role conflicts, data-mapping gaps, or formatting leaks — and output a rewritten, stabilized template.
Yes. All prompt engineering, database logging, and file writes are executed entirely locally in your workspace session, ensuring your proprietary prompts and automation rules remain strictly private.
Prompts and templates are written directly as clean Markdown (`.md`) files in your workspace exports folder, ready to copy directly into your local scripts or codebases.
Yes. You can specify: "optimize this prompt for Claude 3.5 Sonnet" or "optimize for GPT-4o," and the skill will apply model-specific formatting guidelines (such as system-message styling or XML tag structures) to maximize output quality.
Browse more in General Tools or all skills.
Get EasyClaw, add this skill, and start building AI agent workflows in minutes.
Get EasyClaw Free →