Five Pitfalls in AI Skill Design and How to Avoid Them

Introduction: Experience Summary for Creating AI Skills

The article shares practical lessons learned from building AI skills, highlighting five frequent mistakes and their solutions.

Pitfall 1: No Acceptance Criteria Defined

Core Principle: "If you want AI to meet your requirements, the best method is to write the acceptance criteria as a checklist inside the skill. After each run, AI scores itself against the list. If any item fails, it re-runs until all criteria pass before outputting."

Practical Example: A long-form writing skill includes three mandatory hard checks before delivery. If a check fails, the skill restarts; only after all checks pass does it output the result.

Pitfall 2: Not Providing Domain Knowledge

Risk: "Although AI is smart, if you don't give it your industry experience and professional knowledge, it will fall back on generic knowledge or hallucinate, producing amateurish output with many fabrications."

Solution: "Provide the necessary professional knowledge, place it in reference, and force the model to study it before generating output."

Concrete Example: For a contract risk report, require reading contractmd; for labor relations, require employmentmd. Do not rely on the model's internal knowledge, and never let it fabricate.

Pitfall 3: Misusing LLMs for Deterministic Tasks

Capability Boundary: "LLMs are not universal. Habitual delegation of all work to AI leads to misuse — for example, asking AI to calculate monthly revenue, gross margin, and growth rate for a dozen stores. This is clearly unsuitable."

Division Principle: "LLMs excel at text, reasoning, and math tasks that are deterministic in nature; those can be written as scripts. Place scripts in scripts and call them when needed. Never let the LLM do the computation — it consumes excessive token s and is highly likely to produce errors."

Core Mnemonic: " Deterministic work goes to scripts, probabilistic work goes to LLMs."

Pitfall 4: Over-Reliance on Process and Automation

Problem Scenario: "When a workflow is long with many decision nodes, and you hand all judgments to AI without any oversight, a single early error cascades. The final output bears no resemblance to what you wanted, and you blame the tool."

Correct Approach: "At critical nodes, require human confirmation, or split the workflow into several small skill tasks. At the end of each, instruct: 'Wait for your confirmation, then call the next skill to continue.'" Specification Guideline: "Following workbuddy standards, a skill tutorial should not exceed 5,000 tokens; the ideal range is 2,000–3,500 tokens (roughly 200–300 lines). Therefore, split when necessary — do not chase a single all-encompassing, fully automated skill tutorial ."

Pitfall 5: Missing Samples and Output Specifications

Sample-Based Learning: "Provide samples for the model to learn the desired output format. Be clear about the structure, fields, and file type you expect. For a weekly report, show a few examples so the model internalizes the pattern."

Stability Value: "Especially for long documents, this method locks down the output specification, eliminating the worry that every generation looks different."