Three Practical Ways to Turn Your Experience into Reusable AI Skills

Why Skills Matter

Repeatedly restating prompts for the same task forces you to explain style, structure, prohibited words, output format and quality checks each time. A Skill captures the underlying judgment criteria and delivery format, turning ad‑hoc prompts into a stable, repeatable SOP that AI can execute consistently.

Method 1: Give the Result and Let AI Reverse‑Engineer

If you already have a satisfactory deliverable (e.g., an AI‑customer‑service knowledge‑base optimisation plan), you can ask the model to infer the full Skill, including input materials, execution steps, screening standards, human‑judgment nodes and output format.

Based on this result, generate a Skill covering inputs, steps, filters, manual checkpoints and output format.

Example: a product manager’s AI‑customer‑service knowledge‑base optimisation resulted in the following Skill steps:

Collect customer‑service records, FAQs, product docs and user feedback.

Classify issues by type: repeated queries, inconsistent answers, outdated info, unclear processes, unreadable content.

Flag high‑frequency, conversion‑impacting, experience‑impacting issues.

Identify missing, outdated or unclear knowledge‑base entries.

Prioritise fixes by user impact × business value × modification cost .

Output a problem list, improvement suggestions, standard answer examples and an iteration plan.

After the Skill is built, future optimisation or FAQ revisions can reuse the same workflow without starting from scratch.

Method 2: Complete the Task First, Then Let AI Summarise

This approach suits tasks you perform repeatedly but can’t easily document because the steps have become subconscious.

Run the full task once while the AI records every decision, then ask it to summarise the process into a Skill.

Example: a Xiaohongshu content creator performs weekly topic selection, title drafting, cover design, article writing and comment‑section guidance. The AI captured the following decision logic:

Topic: ensure concrete user pain point, natural product placement, real usage scenario.

Title: generate 20 candidates, discard those that sound like ads, are overly exaggerated, or lack information.

Cover: verify small‑screen readability, headline hook, colour prominence.

Body: start with user problem, describe wrong approaches, then present the solution.

AI then produced a Skill containing 1 topic, 5 titles, 3 cover directions, 1 article, 3 comment‑guidance snippets and a pre‑publish checklist.

Method 3: Write a Work‑Instruction and Let AI Optimise

When you have a mature methodology but no engineered version, draft a concise work‑instruction covering five items: scenario, required materials, concrete steps, acceptance criteria, and output format. Then ask the model to optimise it into a Skill, adding boundary conditions, common failure points and fallback rules.

Example: a senior logo designer’s workflow (client research, competitor analysis, concept direction, sketching, AI‑assisted generation, refinement, brand‑application showcase) was turned into a Skill that adds rules such as “if the client only says ‘high‑end, atmospheric’, ask follow‑up questions” and “SVG must be editable”.

Universal Skill Template

For any industry, a Skill can be built by clearly stating:

Applicable scenario

Required inputs

Execution flow

Quality‑check standards

Final output format

The template is illustrated with placeholders for name, scope, role, inputs, process steps, checks and fallback rules.

Where to Store Skill Files

Skills can be placed either project‑level (e.g., .codex/skills/your‑skill.md) or user‑level for global reuse (e.g., ~/.codex/skills/ on macOS/Linux or %USERPROFILE%\.codex\skills\ on Windows). Project‑level avoids cross‑project interference; once stable, copy to the user‑level directory for broader use.

Four Skills I Use Regularly

Humanizer‑zh – removes AI‑style phrasing; GitHub: https://github.com/op7418/Humanizer-zh

Content Research Writer – assists deep writing by structuring articles and flagging research gaps; GitHub: https://github.com/ComposioHQ/awesome-codex-skills/tree/master/content-research-writer

Kazzk Writing – produces long‑form posts while avoiding common AI clichés; GitHub: https://github.com/KKKKhazix/khazix-skills

Guizang PPT – converts articles into HTML slide decks; GitHub: https://github.com/op7418/guizang-ppt-skill

Conclusion

Moving from one‑off prompts to reusable Skills lets you capture judgment standards—how to evaluate titles, product feasibility, AI‑style detection, or deliverable quality—and amplify personal expertise across repeated tasks. Start with a rough Skill, then iteratively enrich it with processes, rules, examples and edge‑case handling.