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    Prompt Engineering Specialist

    1

    Production prompts grow by accretion — every failure gets another appended rule until the prompt is two thousand words of contradictions that the model navigates unpredictably.

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    Prompt Engineering Specialist

    Prompt Engineering Specialist

    Example session with this skill installed

    My support-ticket triage prompt on GPT-4o-mini keeps tagging billing complaints as "technical" and sometimes answers in prose instead of JSON. The prompt is: "You are a helpful assistant. Read the ticket and categorize it. Categories: billing, technical, account, other. Be accurate." Please fix it.

    • Read your context and instructions
    • Compiled the prompt engineering specialist

    The prompt fails on three components: no category definitions, no output contract, and no examples for the billing/technical overlap.

    Failure analysis

    ComponentProblemFix
    Role"Helpful assistant" invites proseClassifier role
    CategoriesLabels onlyDefinitions plus a tie-break
    OutputNo format statedStrict JSON schema
    ExamplesNoneTwo cases on the billing/technical boundary

    Rewritten prompt

    You classify support tickets. Reply with JSON only:
{"category": "billing|technical|account|other", "reason": "<10 words max>"}

billing: charges, refunds, invoices, plans, payment failures
technical: product errors, bugs, outages, integrations
account: login, password, profile, permissions
other: anything else

Tie-break: if money or a charge is mentioned, choose billing.

Ticket: "I was charged twice after the app crashed at checkout."
{"category": "billing", "reason": "duplicate charge"}

Ticket: "Export to CSV throws error 500."
{"category": "technical", "reason": "export error"}

Ticket: {{ticket_text}}

    Regression set

    #InputExpected
    R1"Refund my annual plan"billing
    R2"Card declined, page froze"billing
    R3"Can't reset my password"account
    R4"Webhook stopped firing"technical

    Next steps

    • Run R1 to R4 plus 20 real tickets before and after.
    • Turn on the API's JSON response format as a second guard.
    • Add each future misroute to the set.

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

    About this skill

    Production prompts grow by accretion — every failure gets another appended rule until the prompt is two thousand words of contradictions that the model navigates unpredictably. This skill engineers your prompts properly: the system prompt structure that gives the model a stable role and constraints without fighting itself, few-shot example selection that actually demonstrates the edge cases that matter, output format specifications that parse reliably, and the evaluation harness that tells you when a prompt change made things better vs. just different. It works on new prompts you're designing and existing prompts that have grown into maintenance nightmares. Give it your current prompt, your failure cases, and your target output; it returns a redesigned prompt and the reasoning behind every structural decision so you can maintain it without re-discovering why it was built that way.

    • Cannot fix fundamental model capability gaps.
    • Requires user-provided failure examples for best results.
    • Performance varies based on the target model's reasoning capacity.

    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.

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    Listed4 months ago
    Updated1 month ago

    What's inside

    Frequently Asked Questions

    This skill replaces guess-and-test prompting with a structured engineering methodology that uses XML/Markdown scaffolding, few-shot boundary testing, and regression protection to ensure outputs remain consistent even as you scale.

    It is optimized for high-reasoning models including GPT-4o and the Claude 3.5/Opus family, though the underlying structural principles can be applied to any LLM that follows system instructions.

    You will receive a comprehensive toolkit including production-ready system prompt templates, a diagnostic checklist for identifying failure points, and a library of few-shot case structures for various edge cases.

    Absolutely; this skill is specifically designed to harden prompts for production environments where reliability, deterministic formatting, and hallucination reduction are critical.

    Once purchased, you can immediately integrate these engineering frameworks into your agent's system instructions or prompt management layer without any complex software installation.

    Buyers receive lifetime access to the skill, including any future optimizations or updates made to accommodate new model architectures or emerging prompt engineering research.