Finding and Reviewing Skills
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Prefer inspectable, narrowly scoped skills from accountable sources and test them on low-consequence work first.
What You Will Be Able to Decide
- Explain finding and reviewing skills in product and business terms.
- Apply this decision: Prefer inspectable, narrowly scoped skills from accountable sources and test them on low-consequence work first.
- Recognise this material risk: a convenient skill exfiltrates data, changes unintended files, or produces outdated guidance.
- Use this review: Run one customer email through the intake tool with missing context, ambiguous intent, and a request that needs human approval.
A founder is deciding what to delegate to AI and what evidence to require before accepting the result. This lesson gives you a concrete question to take into a build brief, proposal review, or product decision.
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? The course example is An AI assisted intake tool that turns customer emails into draft tasks; use it to decide what evidence would justify the choice before a builder implements it.
What Does Finding and Reviewing Skills Mean for Your Product?
A founder is deciding what to delegate to AI and what evidence to require before accepting the result.
Use the illustrative service for this course (An AI assisted intake tool that turns customer emails into draft tasks) to make the choice concrete. What may the AI produce, what must it never decide, and what evidence lets a person approve the result?
Technical term
Finding and Reviewing Skills
Skill review examines source, permissions, dependencies, instructions, outputs, and maintenance before an agent is allowed to use the package.
How Should a Founder Use Finding and Reviewing Skills?
For an ai assisted intake tool that turns customer emails into draft tasks, ask what would happen if a convenient skill exfiltrates data, changes unintended files, or produces outdated guidance.
For this decision, the useful standard is that the output satisfies explicit constraints and survives review outside the conversation that produced it.
- Decision: Prefer inspectable, narrowly scoped skills from accountable sources and test them on low-consequence work first.
- Evidence to request: show that the output satisfies explicit constraints and survives review outside the conversation that produced it.
- Owner: name who will respond if a convenient skill exfiltrates data, changes unintended files, or produces outdated guidance.
- Record the result in the AI work brief, review record, and acceptance criteria.
- Practical review: Run one customer email through the intake tool with missing context, ambiguous intent, and a request that needs human approval.
How Do You Choose an Approach to Finding and Reviewing Skills?
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Prefer inspectable, narrowly scoped skills from accountable sources and test them on low-consequence work first.
The risk is that a convenient skill exfiltrates data, changes unintended files, or produces outdated guidance. Compare a simpler option with the proposed one, including who will operate either choice.
- Describe the user or business outcome that must be protected.
- Identify the most credible failure and its consequence.
- Compare the simplest adequate approach with one realistic alternative.
- Set a review point for when the decision may need to change.
What Evidence Should You Accept for Finding and Reviewing Skills?
What Warning Signs Should You Look For?
- The proposal does not address this risk: a convenient skill exfiltrates data, changes unintended files, or produces outdated guidance.
- Nobody can show whether the output satisfies explicit constraints and survives review outside the conversation that produced it.
- The decision has no named owner or review point.
What Should You Ask a Consultant?
- What changes for the user if we choose this approach to finding and reviewing skills?
- How have we reduced or accepted this risk: a convenient skill exfiltrates data, changes unintended files, or produces outdated guidance.
- Can you demonstrate that the output satisfies explicit constraints and survives review outside the conversation that produced it?
- Who owns the result, and when will we reconsider it?
Key takeaway
Key Takeaway
Prefer inspectable, narrowly scoped skills from accountable sources and test them on low-consequence work first. Ask for evidence against the specific risk: a convenient skill exfiltrates data, changes unintended files, or produces outdated guidance.