Generated Skill

YAML
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name: designing-ai-prompt-arbitrage-pipelines
description: Architects reusable multi-stage AI prompt pipelines that abstract a proven workflow in one domain into logic primitives and remap them onto a structurally analogous domain for new product opportunities. Use when asked to repurpose an existing content-generation pipeline (e.g., worksheet generators, report generators) into an adjacent market, or to design headless multi-step LLM pipelines with validation gates instead of single-turn prompting.
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# Designing AI Prompt Arbitrage Pipelines

Given a working pipeline in Domain A, produce a mapped pipeline for Domain B in four primitives:

1. Intake & Extraction — parse raw source material into structured data
2. Transformation & Mapping — convert structure into domain-specific core logic, decoupled from style
3. Compliance & Validation — run automated integrity + accessibility gates before output
4. Enhanced Output — fuse validated core output with a value-add layer that justifies premium pricing

Output format: a primitive-by-primitive table mapping Domain A logic → Domain B logic, followed by a named "Core Arbitrage Pipeline" section and a monetization rationale.

Progress:

• Identify Domain A: the proven, already-working pipeline (its inputs, steps, gates, outputs)
• Identify Domain B: the target market with structurally similar steps but different content
• Abstract Domain A's steps into 3-5 generic "logic primitives" (name each one abstractly, e.g., "Intake & Extraction Primitive")
• For each primitive, write parallel bullets: "[Domain A] Logic:" and "[Domain B] Arbitrage:"
• Identify the specific validation/integrity check unique to Domain B (the thing that breaks if ignored — e.g., stitch math, legal citation accuracy, nutrition totals)
• Identify the specific accessibility/usability conversion required (e.g., visual chart → text-to-speech-friendly prose)
• Define the "premium fusion" output — what elevates a commodity output into a differentiated product (an added digest, summary, certification, or narrative layer)
• State the technical constraint driving architecture choices (e.g., mobile/headless execution ruling out visual node editors, single-turn LLM calls ruling out true verification)
• Name the orchestration method (sequential headless primitives with independent verification gates, not a single monolithic prompt)
• Optionally wrap the design in illustrative dialogue between two technical personas debating tradeoffs

Example 1: Input: "Map an educational worksheet generator onto a vintage handicraft pattern business." Output:

• Primitive 1 (Intake): Worksheet parses academic text into headings/key terms → Handicraft scans vintage PDFs for physical vectors and stitch counts.
• Primitive 2 (Transform): Worksheet merges research into content non-destructively → Handicraft acts as a geometry node converting shapes into stitch arithmetic (6-12-18-24 increase logic), decoupling geometry from style.
• Primitive 3 (Validate): Worksheet checks GDPR/WCAG → Handicraft runs "Stitch Math Integrity" gate (row sums = bracketed counts) and an "Accessibility Filter" (visual charts → text instructions).
• Primitive 4 (Output): Worksheet produces print-ready HTML/CSS → Handicraft fuses pattern with a "Cognitive Digest" research summary, creating a premium "Smart-Craft" artifact.

Example 2: Input: "Map a resume-optimization pipeline onto a wedding-vow writing business." Output:

• Primitive 1 (Intake): Resume parser extracts job titles/skills from raw text → Vow pipeline extracts relationship milestones and names from couple's raw notes.
• Primitive 2 (Transform): Resume maps skills to ATS keywords → Vow pipeline maps milestones to narrative arc structure (opening hook → shared history → promise → closing line).
• Primitive 3 (Validate): Resume checks keyword density and formatting compliance → Vow pipeline checks tone consistency and runs a "cliché density" gate plus a read-aloud time-length check (60-90 seconds).
• Primitive 4 (Output): Resume outputs ATS-formatted PDF → Vow pipeline fuses the vow text with a printable keepsake card design, creating a premium "ceremony artifact" product.

• Always keep primitive names domain-agnostic (Intake, Transform, Validate, Output) so the same skeleton reapplies to any new arbitrage target.
• Identify the one non-negotiable integrity check for the new domain early — this is usually the thing that makes or breaks user trust (stitch math, nutrition sums, legal citations, measurement units).
• Push for headless, sequential execution with explicit gates rather than a single giant prompt; name why (mobile constraints, reliability, no true parallelism in single-turn calls).
• Always propose a "premium fusion" layer in the final primitive — raw converted output is commoditized; an added digest/summary/certification is what justifies price.
• When illustrating via dialogue, give each persona a distinct technical bias (one obsessed with compute/architecture, one obsessed with correctness/math) and let them interrogate weak points in the pipeline.
• Tie the pipeline to a measurable acceptance threshold (e.g., "error rate below 0.5% across 50 iterations") to make the design testable, not just conceptual.

• Don't describe the new domain pipeline from scratch — always explicitly map it back to the proven source pipeline's steps, one-for-one.
• Don't skip the validation primitive; arbitrage pipelines fail credibility without a domain-specific integrity gate.
• Don't conflate "single LLM call with a long prompt" with "multi-agent pipeline" — call this out explicitly as an anti-pattern.
• Don't leave the monetization angle implicit; always state what makes the output premium versus a plain commodity conversion.
• Don't over-engineer with heavy visual tooling if the stated constraint is lightweight/headless/mobile execution.