experimenting-edge skill

Overview

This skill captures a compact decision-making and knowledge framework for guiding an AI through a specific task area. It consolidates core concepts, a clear invocation protocol, measurable success criteria, and common anti-patterns to avoid. Use it to ensure consistent, repeatable outcomes when the AI handles related requests.

How this skill works

The skill exposes a passive knowledge base of key concepts and terminology the AI should reference when reasoning. It defines a decision logic protocol: when to activate the skill, what inputs to require, how to choose among actions, and how to escalate. The skill also provides success criteria so the AI can self-evaluate results and detect failure modes.

When to use it

• When a task requires domain-specific terminology or concepts to be applied consistently.
• When multiple action paths exist and you need a deterministic protocol for choosing one.
• When outcomes must be measurable and verifiable against clear acceptance criteria.
• When you need to avoid common pitfalls or anti-patterns in agent behavior.
• When handoffs or escalation to a human are likely and should follow defined triggers.

Best practices

• Reference the passive knowledge base first to align terminology and assumptions before acting.
• Validate required inputs early; request clarifying questions if key data is missing.
• Follow the decision logic stepwise: evaluate, select, execute, and verify against success criteria.
• Log decisions and rationale briefly to support audits or later review.
• If success criteria are unmet after an action, run the fallback path or escalate according to the protocol.

Example use cases

• Customer support triage where consistent diagnosis and escalation rules improve response quality.
• Automated content review that needs to apply a specific taxonomy and measurable acceptance checks.
• Small-scale workflow automation requiring deterministic decision steps and clear rollback conditions.
• Training an assistant to apply a company policy consistently across user queries.
• Implementing a verification layer that checks outputs before committing them to a downstream system.

FAQ