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    PromptDecoder Pro — AI Output to Prompt Converter

    1

    Paste any AI output. Get the production-ready prompt that made it.

    Free

    24 uses5.0 (1 review)
    PromptDecoder Pro — AI Output to Prompt Converter

    PromptDecoder Pro — AI Output to Prompt Converter

    Example session with this skill installed

    Reverse-engineer this response into a reusable prompt: "To optimize your React code, first identify unnecessary re-renders using Profiler, then implement useMemo for expensive calculations."

    • Read your context and instructions
    • Compiled the promptdecoder pro

    ###Role###
    You are a Senior Frontend Engineer and Performance Consultant...

    ###Task###
    Analyze the provided [code_snippet] to identify bottlenecks...

    ###Steps###

    1. Profile the execution...
    2. Identify re-render triggers...

    ###Format###
    Provide an actionable list of optimizations...

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Give PromptDecoder Pro a raw AI-generated output — a piece of writing, an explanation, a strategy, a script — and it traces back to the exact functional intent that produced it. It ignores the subject matter and focuses on what kind of task was being performed, then constructs a complete Role-Task-Steps-Examples-Format structured prompt you can copy, save, or sell. Built-in Chain-of-Thought decomposition and Tree-of-Thoughts branch evaluation ensure the reconstructed prompt is the highest-confidence match to the original intent — not a guess. Includes a validation checklist so every delivered prompt meets production-grade standards before you use it.

    Scope — what it does

    • Reverse-engineers AI outputs to identify their original prompt intent
    • Reconstructs fully structured prompts with Role, Task, Steps, Examples, and Format blocks
    • Uses CoT and ToT reasoning patterns internally (without naming them in the output)
    • Converts informal prompt ideas into production-ready instruction sets
    • Packages one-off prompts into reusable, SKILL.md-compatible assets
    • Audits existing prompts by comparing inferred intent vs. actual output

    Scope — what it does not do

    • Does not generate creative content (stories, images, code) directly
    • Does not reverse-engineer outputs from image or audio models
    • Does not produce multiple competing prompt variants — delivers one optimal result
    • Does not evaluate ethical safety or policy compliance of reconstructed prompts
    • Does not handle purely numerical or tabular outputs with no linguistic structure
    • Does not write prompts targeting a specific subject — subject is always a user-supplied variable

    Compatibility notes

    ✅ ChatGPT (GPT-4o, GPT-4, GPT-3.5) ✅ Claude (Sonnet, Opus, Haiku — all versions) ✅ Google Gemini (1.5 Pro, Flash) ✅ Any LLM that accepts system/user prompt instructions ✅ PromptBase, Etsy, Fiverr, FlowGPT, AIPRM — all major prompt marketplaces ✅ JARVIS SKILL.md format — drop directly into skills folder ✅ Works with text outputs of any domain (marketing, code explanation, strategy, education, etc.) ⚠️ NOT compatible with image-only or audio-only AI outputs ⚠️ Best results on well-structured, linguistically rich outputs (1+ paragraphs)

    TRIGGER PHRASES (for AI assistant use)

    "reverse-engineer this output into a prompt" "I have an AI output, find the original prompt" "turn this prompt into a skill" "convert this output into a SKILL.md" "package this as a SKILL.md" "make this prompt reusable" "build a SKILL.md from this prompt"

    Linguistic Dependency: Cannot reverse-engineer visual, auditory, or purely mathematical/tabular outputs that lack underlying linguistic structure or logical intent.

    Garbage In, Garbage Out: If the provided AI output is generic, brief (less than a paragraph), or poorly executed, the reconstructed prompt will reflect those same structural deficiencies.

    Model Blindness: It identifies the functional intent, not the exact specific weights, system tokens, or hidden seed values of the specific model that generated the original text.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

    Reviews

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    This skill has been used 24 times.

    Trust & safety

    Security scan

    Security scan passed 4 months ago

    • Free with an Agensi account

    Permissions

    No special permissions declared or detected

    Listed4 months ago
    Updated3 days ago

    What's inside

    Frequently Asked Questions

    PromptDecoder Pro reverse-engineers a snippet of AI-generated text to identify the underlying structural intent. It then reconstructs that intent into a production-grade, reusable prompt template using a professional Role-Task-Steps format.

    The reconstructed prompts are universal. This skill is compatible with ChatGPT (GPT-4o), Claude (Sonnet/Opus), and Google Gemini, and it even formats prompts for direct use as JARVIS-compatible SKILL.md assets.

    No complex installation is required. Once you add this skill to your workspace, you simply paste an AI-generated output when prompted, and the tool immediately begins the reverse-engineering and formatting process.

    While it works on short responses, it excels with outputs of one paragraph or more. It is specifically designed for linguistic tasks like strategies, scripts, and explanations, rather than purely numerical data or image-based outputs.

    You receive the primary PromptDecoder Pro skill, a built-in validation checklist to ensure output accuracy, and the ability to export results directly into the standardized SKILL.md format for easy organization or resale.

    Unlike basic prompt generators, this tool uses Chain-of-Thought and Tree-of-Thought reasoning to audit the intent behind the text. This ensures the resulting prompt isn't just a guess, but a high-confidence match for the original instructions that created it.

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