
Prompt Engineering Specialist
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
- Debug and fix inconsistent LLM outputs in production environments
- Refactor long prompts to improve instruction following and context retention
- Generate high-quality few-shot examples for complex edge cases
$9.99
· or 50 creditsSecure checkout via Stripe
Included in download
- Debug and fix inconsistent LLM outputs in production environments
- Refactor long prompts to improve instruction following and context retention
Sample input
My system prompt for extracting JSON from invoices is failing 20% of the time on multi-page PDFs. It keeps adding conversational text outside the JSON. Help me fix the output format and reliability.
Sample output
I've redesigned your prompt using an XML-wrapped output contract. I've added two 'boundary' few-shot examples specifically for multi-page transitions.
Diagnosis: Format bleed due to weak instruction placement. Deliverable: New System Prompt + 3 Regression Test Cases.
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
$9.99
· or 50 creditsSecure checkout via Stripe
Included in download
- Debug and fix inconsistent LLM outputs in production environments
- Refactor long prompts to improve instruction following and context retention
- Instant install
Sample input
My system prompt for extracting JSON from invoices is failing 20% of the time on multi-page PDFs. It keeps adding conversational text outside the JSON. Help me fix the output format and reliability.
Sample output
I've redesigned your prompt using an XML-wrapped output contract. I've added two 'boundary' few-shot examples specifically for multi-page transitions.
Diagnosis: Format bleed due to weak instruction placement. Deliverable: New System Prompt + 3 Regression Test Cases.
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.
Use Cases
- Debug and fix inconsistent LLM outputs in production environments
- Refactor long prompts to improve instruction following and context retention
- Generate high-quality few-shot examples for complex edge cases
- Eliminate conversational 'filler' from automated AI agent responses
Known Limitations
- 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 Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/prompt-engineering-specialist -o /tmp/prompt-engineering-specialist.zip && unzip -o /tmp/prompt-engineering-specialist.zip -d ~/.claude/skills && rm /tmp/prompt-engineering-specialist.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Compatible with SKILL.md-compatible agents including Claude Code and Cursor.
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