Why Your AI Skill Falls Short and How to Refine It in Three Real‑World Scenarios

In the previous post we showed how to turn experience into an AI Skill using three simple steps: let the AI reverse‑engineer a satisfactory result, summarize a real task, and convert a work instruction into a Skill. Those steps can produce a functional Skill, but the Skill is only the first step.

When used in practice, many users encounter three problems: the Skill is not invoked when needed, its output is unstable, and an ever‑growing list of Skills makes it unclear which one to use.

1. A good Skill is not a longer prompt

Core judgment: many Skills are ineffective not because the prompt is too short, but because they remain at the “wish” level. For example, prompts such as “Help me optimize an article”, “Help me analyze a PRD”, “Help me check a design”, or “Help me judge if a topic can explode” can be used, but they are unstable because they do not clarify:

What the input is;

What to do first;

What the evaluation criteria are;

What the final output should be;

Which decisions the AI must not make.

Therefore a good Skill is a clear workflow, not merely a longer prompt.

2. Product manager: change “Help me judge” to “Help me pre‑check”

Product managers often apply Skills to tasks such as reviewing requirements, breaking features, writing PRDs, preparing review materials, and marking risk points. The first‑version Skill is usually too coarse. For instance, “Help me analyze a PRD, break out feature points, user flows, and risks” is a vague temporary command rather than a stable workflow.

This Skill is used for PRD pre‑review. Input materials: Business background Target users Original PRD text Current version scope Known constraints Execution flow: Extract business goals Break user paths Organize functional modules Mark dependencies and risks List pending questions Output format: Requirement summary Feature list User flow Dependencies Risk points Pending questions Pre‑review checklist Boundaries: Do not set product‑manager priority Do not fabricate nonexistent business rules If information is insufficient, first mark it as pending

After this change the Skill’s role is clear: it becomes a pre‑check tool rather than a chat assistant that makes decisions. The core adjustment is to replace “Help me judge” with “Help me pre‑check”.

3. Designer: change “Help me evaluate” to “Help me walk‑through”

Designers have many repeatable tasks: UI walk‑through, component check, proposal explanation, design‑spec verification, pre‑delivery review. A common pitfall is using a vague prompt such as “Help me check if this page design has problems”. The AI may return generic suggestions like “colors could be more unified”, which are not actionable.

This Skill is used for UI page delivery walk‑through. Check dimensions: Information hierarchy: is the primary‑secondary relationship clear? Visual consistency: colors, font sizes, spacing follow standards? Component usage: are existing components reused, or are custom components created? Interaction states: are empty, loading, error, and disabled states complete? Content expression: are buttons, headings, and copy clear? Delivery risk: can developers implement based on the current component library? Output format: Main issues Modification suggestions Impact on launch Items requiring designer judgment

Thus the Skill becomes a concrete delivery checklist rather than an “aesthetic evaluator”.

4. Operations: change “Help me predict” to “Help me filter”

Operations repeat tasks such as topic selection, title crafting, cover design, article body, comment‑section guidance, release check, and data review. A typical first‑version Skill might be “Help me judge whether this topic can explode”, which is too abstract; the AI often returns correct‑looking but non‑actionable advice.

This Skill is used for Xiaohongshu topic pre‑filter. Input materials: Topic direction Target audience Product or service information Reference accounts or competitor content Platform constraints Judgment criteria: User pain is specific Clear usage scenario exists Title instantly resonates with target users Content can naturally embed the product There is a before‑after contrast Cover is recognizable at a glance on mobile Comment area provides discussion points Output format: Topic score Suitable audience Core hook Main risks Suggested title Cover direction Body structure Comment‑section guidance

The Skill now checks concrete pre‑publication conditions instead of trying to predict virality.

5. If triggering is off, first edit the description

Many Skills fail at the trigger stage. A poor description is generic (e.g., “Help optimize an article”) and does not specify the platform, draft stage, style expectations, or whether AI‑flavor should be removed. Better descriptions list both applicable and non‑applicable scenarios.

Example of a good description: use when a public‑account draft needs expression improvement, AI‑flavor reduction, and personal style enhancement; do not use for topic research, data collection, or zero‑draft generation.

6. Keep the Skill concise, avoid bloat

The first version is a draft. Adding rules, cases, templates, and notes makes the Skill thick and hard to use. Keep the main file focused on the core workflow; place rarely used material in a reference folder; delegate fixed logic to scripts, tables, or checklists.

For a writing Skill, the main file should contain only:

When to use; required inputs; writing workflow; style requirements; output format; prohibited expressions.

Ancillary assets such as sample articles, title banks, platform layout rules, and templates belong in the reference section.

7. Managing many Skills

When the number of Skills grows, overlap, redundancy, and unclear triggers appear. Follow three principles:

1. Global Skills should be few and precise

Global Skills are used across projects (e.g., data整理, article polishing, meeting minutes, generic checklists). Keep their number low to avoid competition for scenes.

2. Project‑specific Skills belong to the project

Place a Skill inside the project that exclusively uses it (e.g., PRD pre‑check in a product project, UI walk‑through in a design project).

3. If you have more than ten Skills, maintain an index table

The table records six items: Skill name, purpose, trigger condition, input, output, notes. It forces you to reconsider usefulness, overlap, and retirement.

8. A general optimization checklist

1. Is the problem specific enough? 2. Does the description clearly state when to use it? 3. Are unsuitable scenarios documented? 4. Are input materials explicit? 5. Does it ask for missing information or make assumptions? 6. Are execution steps reproducible? 7. Is the output format fixed? 8. Are good‑result examples provided? 9. Are anti‑examples or禁用 rules listed? 10. Is it too long; can it be split into reference? 11. Does it try to do too many tasks; should it be split? 12. Has it been used in the past month?

Use the checklist to locate trigger problems, output instability, overly broad rules, heavy processes, or the need to split the Skill.

9. Conclusion

The first‑version Skill is usually a draft; being runnable does not mean it is good. A stable Skill emerges from real tasks: product managers use it to organize requirements, designers for delivery checks, operators for pre‑screening topics, writers for consistent expression, and teams for codifying processes. Each usage reveals issues—trigger accuracy, output stability, rule breadth, process weight, or scope—that guide further refinement. Focus on perfecting the three most frequently used Skills before proliferating more.