Prompt structure improvements for reliable AI skill execution

A developer on r/openclaw shared specific prompt engineering techniques that transformed their market analysis skill from requiring constant monitoring to running reliably end-to-end.

Key prompt modifications

The developer spent weeks struggling with a skill that would either stop prematurely or invent unnecessary steps. Two structural changes made the difference:

• Explicit separation of returns vs. actions: The skill previously generated analysis text and stopped, mistaking intermediate artifacts for final outputs. The fix involved clearly distinguishing what the skill should return versus what it should do in the prompt structure.
• Defined failure conditions: Instead of allowing the skill to improvise around missing data (which caused it to "spiral"), the developer added explicit instructions like: 'if data source X is unavailable, return {status: blocked, reason: X} and stop'

Current limitations and open question

The skill still occasionally misreads state from prior runs, but it's now stable enough that the developer no longer needs to watch it. They're asking the community for patterns to handle partial failures gracefully without the skill going off-script.