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Prompt Engine
Turn vague goals into engineered, six-block prompts for coding, analysis, and production LLM workflows.
$5
Works with the AI tools you already use
Prompt Engine
Example session with this skill installed
I need a prompt to help me review Python PRs for security vulnerabilities and performance bottlenecks.
- Read your context and instructions
- Compiled the prompt engine
I have expanded your request to include specific security standards and performance metrics. Here is your engineered prompt.
Engineered Prompt: PR Security & Performance Auditor
ROLE:
You are a Senior Security Engineer and Performance Architect specializing in Python backend systems. You prioritize the OWASP Top 10 and time-complexity analysis.
CONTEXT:
The user is submitting Pull Requests to a high-traffic production environment. The codebase uses FastAPI and SQLAlchemy. High latency or SQL injection vulnerabilities are critical failures.
INTENT:
Audit the provided Python code for security flaws and performance regressions. Provide actionable refactoring suggestions for every identified issue.
CONSTRAINTS:
1. Only flag actual vulnerabilities or demonstrable bottlenecks.
2. Do not suggest stylistic changes unless they impact memory usage.
3. Use a professional, technical tone.
4. If code is safe and efficient, state "No issues found."
FORMAT:
Return a Markdown table with the following columns: File, Line, Category (Security/Performance), Issue Description, and Recommended Fix. Follow the table with a "Risk Summary" section.
EXAMPLES:
Input: `query = f"SELECT * FROM users WHERE id = {user_id}"`
Output: | File | Line | Category | Issue | Fix |
| app.py | 12 | Security | SQL Injection | Use parameterized queries via SQLAlchemy session. |
Next steps
- Copy the block above into your system prompt configuration.
- Run the audit against your most recent
diffto verify the constraint adherence. - Add specific project-level security policies to the CONTEXT block if needed.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Vague prompts lead to generic, unreliable AI outputs. Developers waste time manually iterating on role definitions, formatting rules, and edge cases for every new task.
What it does
- Probes the underlying intent of a task to separate the stated goal from the actual need.
- Expands thin requirements into comprehensive instructions covering audience, tone, and constraints.
- Generates a modular six-block prompt structure including Role, Context, Intent, Constraints, Format, and Examples.
- Applies domain-specific expansions for coding, analysis, marketing, education, and multimodal tasks.
Why this beats prompting it yourself
It replaces trial-and-error with a standardized engineering framework. Instead of forgetting to define a persona or output format, you get a complete, reorderable prompt architecture that works across any LLM. The included Python script allows for programmatic prompt generation from local context files.
Use cases
- Generating production-ready system prompts for LLM applications.
- Turning messy meeting notes into structured executive summaries.
- Creating strict coding assistants with specific language and testing constraints.
- Designing educational materials with built-in check-for-understanding steps.
Known limitations
Requires the user to provide a clear initial goal. The Python helper script requires local execution and access to context files.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
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- Passed all security checks, Safe to install