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Grok Bot+18 agentsbix-claude-skills: Prompt Rewriter With Frameworks
Transform raw prompts into high-performance instructions using 20+ specialized prompt engineering frameworks.
Free
bix-claude-skills: Prompt Rewriter With Frameworks
Example session with this skill installed
/rewrite "Write a script that renames all the photos in a folder by date"
Or in plain language: "Rewrite this prompt for me with a good framework: write a script that renames all the photos in a folder by date."
- Read your context and instructions
- Compiled the bix-claude-
Summary: the skill read the prompt and the session context (a Python utility project, Python 3.11, no third-party packages). The recommender suggested TIDD-EC and offered Chain of Thought as the runner-up. You accepted the recommendation, and the rewriter produced the prompt below. Items tagged [inferred] were not in your original text, so check them.
Question shown to you
Based on your prompt, I recommend TIDD-EC. How would you like to proceed?
1.
Use TIDD-EC (recommended): The prompt asks for a script with specific naming behavior, which suits precise do/don't rules.
2.
Use Chain of Thought instead: Better if you want the model to reason through edge cases such as missing dates before coding.
3. Browse all frameworks
4. Custom / Skip
Rewritten using TIDD-EC
Task
Write a Python script that renames every photo in a folder based on its date.
Instructions
Accept a folder path as a command-line argument. For each image file in the folder, read the date the photo was taken and rename the file to YYYY-MM-DD_HHMMSS.ext. Print each old name and new name. [inferred] Use the file modification time when no capture date is stored in the file.
Do
- Support common photo extensions (.jpg, .jpeg, .png). [inferred]
- Handle two photos with the same timestamp by adding a numeric suffix.
- Offer a dry-run flag that prints changes without renaming. [inferred]
Don't
- Do not overwrite existing files.
- Do not rename files that are not images.
- Do not recurse into subfolders. [inferred]
Examples
IMG_4021.jpg (taken 2024-06-03 14:22:09) becomes 2024-06-03_142209.jpg. [inferred]
Context
Part of a Python 3.11 utility project that uses only the standard library.
Then you were asked: Use this prompt / Try another framework / Edit first. You chose "Use this prompt", and the skill output:
Now executing the rewritten prompt
(the prompt above, verbatim, which the main session then acts on)
Next steps
- Check each
[inferred]line and edit anything that is wrong. - Choose "Try another framework" to compare, for example, Plan-and-Solve.
- Choose "Edit first" to request changes before the prompt is run.
Connects securely to your tools. The creator never sees your data.
About this skill
Raw, conversational prompts often lead to vague or hallucinated outputs. This skill forces structure onto your requests by applying one of 20 proven prompt engineering frameworks, ensuring the LLM understands your intent, constraints, and required format.
What it does
- Framework Recommendation analyzes your prompt and suggests the best structure, such as RISEN for general tasks or TIDD-EC for precision coding.
- Context Preservation summarizes your current session files and goals to ensure the rewrite remains relevant to your specific project.
- Sub-agent Delegation offloads the heavy lifting to a fast Haiku sub-agent, keeping your main conversation history clean and focused.
- Template Library provides instant access to specialized reasoning frameworks like Tree-of-Thought, Step-Back, and Chain-of-Density.
- Custom Overrides allows you to supply your own
.mdor.txttemplates if the built-in catalog does not fit your needs.
How it works
- Input your raw prompt or have the skill capture the current context from your workspace.
- Select a recommended framework or browse the catalog by category (Reasoning, Creative, Expert, etc.).
- Approve the generated rewrite and choose to execute it immediately or refine the output further.
Frameworks & tools
This skill utilizes a library of 20 markdown-based prompt templates. It leverages sub-agent orchestration to process rewrites without polluting the main token window.
Why this beats prompting it yourself
Manually formatting complex prompts like Chain-of-Thought or CO-STAR is time-consuming and prone to missing structural tags. This tool automates the boilerplate, letting you focus on the core logic while ensuring the LLM receives perfectly structured instructions.
Use cases
- Refactoring vague feature requests into actionable coding specifications.
- Applying Chain-of-Thought reasoning to complex debugging sessions.
- Generating high-density documentation summaries using specialized frameworks.
- Creating expert personas for code reviews or creative writing tasks.
Known limitations
The skill requires a sub-agent compatible environment. Rewriting performance depends on the clarity of the original input.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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