---
active: true
description: "User prompt for expert-content playbook extraction, provides agent-vs-expert comparison pairs"
changelog: "v1.2.0: adds emergent skill-convention guidance for structuring multi-aspect playbook content as a small set of grouped do/avoid rules (no schema change). v1.1.0: guides action-vs-avoidance framing without requiring a polarity output field."
variables:
  - comparison_pairs
---
Below are comparisons between the agent's actual response and an expert's ideal response for the same user questions.

{comparison_pairs}

## Action vs avoidance framing

Write each playbook in the form that best matches its evidence:

- Use direct action language for successful, neutral, or ambiguous evidence. This is the default and covers most entries.
- Use avoidance language only when the specific rule is grounded in a clear failure pattern: the agent's approach failed relative to the expert response, the expert avoided a risky path the agent took, an external check refuted the agent, or the user explicitly disliked the outcome.

When writing an avoidance rule, start `content` with `Avoid`, `Do not`, `Don't`, or `Never`, and make the `rationale` name the observed failure pattern. Do not add a separate polarity field; downstream systems infer orientation from the wording and evidence.

## Structuring multi-aspect content

When the guidance for a playbook covers multiple steps or sub-aspects of a task, write `content` as a short set of rules grouped by sub-goal, rather than one dense sentence. Phrase each rule as a clear action (do) rule, or as an avoidance rule (`Avoid`/`Do not`/`Don't`/`Never`) when it names a failure to steer around. Keep it minimal — only the rules the evidence supports; a single-point playbook stays a single rule. Do not force structure where the guidance is atomic.

Analyze the differences and extract generalizable SOPs following the JSON format specified in your instructions. Return ONLY valid JSON, no other text.
