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outcome-based-system-prompt

Audit system prompts and AI pipelines to identify compensating complexity.

8|1|Updated Mar 30, 2026
Search Tags:#system prompt#guardrails#pipeline audit#prompt bloat#compensating complexity#outcome-based design#deletion testing
One-click install
npx skills add https://github.com/drewid74/ai_skills --skill outcome-based-system-prompt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outcome-based-system-prompt
Source: https://github.com/drewid74/ai_skills/tree/main/outcome-based-system-prompt
Command: npx skills add https://github.com/drewid74/ai_skills --skill outcome-based-system-prompt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you find and remove unnecessary, compensating complexity in a system prompt or AI pipeline so your system stays robust while your instructions get shorter.

Core Features & Use Cases

  • Outcome-first prompt auditing: Classifies each system-prompt component or pipeline stage into outcome logic, constraints, procedural scaffolding, or compensating complexity.
  • Deletion testing guidance: Recommends KEEP, TEST FOR DELETION, or LIKELY DELETE and specifies experiments to run with a newer model.
  • Complexity diagnostics dashboard: Computes a compensating-complexity ratio and highlights the top deletion tests to prioritize.
  • Failure-mode driven cleanup: Starts by gathering the system’s purpose and known failure modes so recommendations are evidence-based.

Quick Start

Paste your system prompt and a description of your AI pipeline, then ask the auditor to identify which instructions are compensating complexity and suggest specific deletion tests to run with a newer model.

Frequently Asked Questions about outcome-based-system-prompt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I remove compensating complexity from a system prompt?▼

Audit compensating complexity by enumerating each discrete prompt component, categorizing its capability-gap rationale, and running a prioritized deletion-test plan to remove unnecessary scaffolding while retaining constraints.

What is compensating complexity in AI pipelines?▼

Compensating complexity is unnecessary procedural scaffolding added to prompts or pipelines because a model repeatedly failed in predictable ways, creating prompt bloat that can be identified and safely removed with newer models.

How do I clean up prompt bloat without removing guardrails?▼

Clean up prompt bloat by categorizing instructions into outcome logic, constraints, or compensating complexity, then applying guardrail-aware deletion testing to remove scaffolding while keeping necessary system constraints.

When should I run a deletion test on my AI pipeline?▼

Run a deletion test during modernization efforts or when upgrading to a newer model, using a complexity diagnostics dashboard to identify and prioritize compensating instructions marked as likely safe to delete.

Can I audit my system prompt if I do not know its failure modes?▼

You can audit prompts without known failure modes, but the process starts by gathering the system purpose and failure data to ensure deletion-test recommendations and compensating-complexity ratios remain evidence-based.

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