- What are Claude Skills and why should I care?
- How does a Claude Skill actually work?
- Which workflows are best suited to automation with Skills?
- How is it different from ChatGPT and the other AI tools I already use?
- Three real-world scenarios: how does work change for an e-commerce store, a professional firm and a manufacturing company?
- What risks should I manage before setting up Skills for my team?
- Why now: where do Italian companies really stand with AI?
- How to get started: the practical steps to set up your first Claude Skill
- What mistakes are most common when setting up Skills?
- How do I know if Skills are really working for my team?
- So, is it worth setting up Claude Skills for my company?
- How we see it at SHM
- Frequently Asked Questions
- Claude Skills turns a repetitive workflow into a saved instruction that activates automatically, with no need to rewrite the prompt every time.
- Your team writes the 'skill' once: from then on, anyone who uses it gets the same result, to the same standard.
- For Italian SMEs and marketing teams, it's a concrete way to cut time lost on low-value tasks and free up hours for strategic work.
- Claude isn't just another ChatGPT: it has a persistent instruction system that makes it better suited to automating recurring internal processes.
94% of companies still run repetitive tasks, and 70% of managers spend up to three hours a day on them. Claude Skills is the practical answer: write the workflow once, and Claude applies it whenever needed, with no copying and pasting.
What are Claude Skills and why should I care?
A Claude Skill is a saved instruction that tells Claude how to behave every time it handles a certain type of task. You no longer have to explain what to do each time: you've written it once, and Claude remembers it.
Claude is an AI (artificial intelligence) assistant developed by Anthropic, an alternative to ChatGPT. The practical difference for a business comes down to one specific feature: Skills, permanent instructions your team configures just once.
Imagine a new colleague you have to give the same explanations to every morning. Skills eliminate that repetition: you write the instructions once, and Claude follows them automatically whenever that type of task comes up again.
For a marketing department, a skill might be "always answer quote requests in this tone and with this structure." For an operations team, "when you receive a warehouse report, extract the key data in this format." The principle is the same: standardize without losing flexibility.
| Your team's situation | Is Claude Skills worth it? | why |
|---|---|---|
| You rewrite the same instructions every time you use AI | Yes | A skill removes the repetition and ensures consistency |
| Several people use AI and get different results | Yes | A shared skill aligns the whole team to the same standard |
| You only use AI for occasional, always different tasks | No, for now | There isn't enough repetition to justify the setup |
| You already have a system of saved prompts on ChatGPT | Yes | Skills are more structured and activate automatically |
| Your team hasn't adopted any AI tool yet | No, for now | Better to start with basic adoption before setting up advanced Skills |
How does a Claude Skill actually work?
A Skill is a block of text that describes to Claude the context, tone, format and rules to follow for a certain type of task. Once saved, it activates every time the task comes up again.
The logic is simple. Without Skills, every time a team member wants to use Claude to, say, write a reply to a client, they have to retype all the context: who we are, what tone we use, what to avoid. With a Skill, all of that is already there.
A Skill has three essential parts:
- Context: who you are, what your company does, who you're talking to.
- Instructions: what Claude should do in this type of task, step by step.
- Constraints: what it must never do (tones to avoid, confidential information, forbidden formats).
Once written, the Skill can be reused by the whole team. Whoever uses it doesn't need to know how it's built: they activate it and get a result consistent with company standards.
- 1 · Manager or team leadIdentify the repetitive workflowChoose a task your team repeats at least 3-4 times a week with the same rules.
- 2 · Manager or team leadWrite the SkillDescribe context, step-by-step instructions and constraints. Test the result on 3-5 real cases.
- 3 · TeamShare it and train the teamExplain to colleagues when and how to activate the Skill. 15 minutes of alignment is enough.
- 4 · EveryoneUse the Skill in daily tasksThe team activates the Skill whenever that type of task comes up, without rewriting anything.
- 5 · ManagerReview and updateEvery 4-6 weeks, check whether the Skill still produces results aligned with company standards.
Which workflows are best suited to automation with Skills?
The best workflows are high-repetition ones, with clear rules and predictable output. They don't have to be complex: the simplest ones often deliver the biggest time savings.
Not every task is a good fit for a Skill. The main criterion is repeatability with stable rules: if the instructions change a lot each time you do the task, a Skill won't help. If the rules are always the same, it's the perfect candidate.
| Company type | Candidate workflow | What the Skill does | Estimated time saved |
|---|---|---|---|
| E-commerce | Replying to negative reviews | Empathetic tone, solution offered, no automatic discounts | 15-20 min/day |
| Professional studio | First draft of a commercial proposal | Standard structure, mandatory sections, formal tone | 30-45 min/proposal |
| Manufacturing company | Weekly production report | Key data extraction, table format, fixed KPIs | 1-2 hours/week |
| Retail store chain | Briefing for the store's social media posts | Local tone, brand hashtags, standard call to action | 20-30 min/post |
| Marketing team | Copy review for brand consistency | Checklist for tone, banned words, maximum length | 10-15 min/text |
How is it different from ChatGPT and the other AI tools I already use?
ChatGPT and Claude take different approaches to persistent instructions. For teams running repetitive processes with fixed standards, Claude Skills offers a sturdier structure.
Many Italian companies started with ChatGPT, the most widely used tool. Switching isn't always necessary: what matters is understanding what your team really needs. As we explained in our article on AI roles in the marketing team, the right tool depends on who uses it and for what.
The main difference is how instructions persist: with Claude Skills, rules are saved at the workspace level (the team's shared working space) and apply automatically. With ChatGPT, custom instructions exist but are tied to the individual user, not the team.
| Feature | Claude Skills | ChatGPT (custom instructions) | Manually saved prompts |
|---|---|---|---|
| Instructions shared across the team | Yes | No, for now | No, for now |
| Automatic activation | Yes | Yes | No, for now |
| Centralized updates | Yes | No, for now | No, for now |
| Learning curve | Low | Very low | None |
| Suited to standardized workflows | Yes | Partial | Partial |
If your team already uses ChatGPT for occasional tasks, there's no rush to migrate everything. But if you want to standardize processes that involve several people, Claude Skills is worth exploring as a dedicated tool. We covered this in our article on ChatGPT Work for sales teams: the right tool changes with the type of workflow.
Three real-world scenarios: how does work change for an e-commerce store, a firm and a manufacturer?
Skills work differently depending on the business context. Three illustrative scenarios show where the savings are most immediate.
The three examples below are illustrative scenarios: the numbers show the order of magnitude of the savings and are not real client data.
| Scenario | Automated workflow | Before Skills | After Skills | Estimated savings |
|---|---|---|---|---|
| E-commerce (20 orders/day) | Replying to return requests and complaints | 15 min per reply, inconsistent tone | 3 min per reply, consistent tone | ~2 hours/day |
| Professional firm (5 consultants) | First draft of a business proposal | 45 min per proposal, a different structure for each consultant | 10 min per proposal, uniform structure | ~3 hours/week per consultant |
| Manufacturing (8-person operations team) | Weekly production progress report | 2 hours of data collection and formatting | 30 min reviewing the generated draft | ~6 hours/week |
What risks should I manage before setting up Skills for my team?
There are three main risks: poorly written instructions that produce wrong outputs, sensitive data entered into Skills, and reliance on a single tool with no backup plan.
A poorly configured Skill isn't neutral: it produces errors systematically, on every task where it's activated. That's why testing is essential before you share a Skill with the whole team.
On the privacy front, Skills must not contain clients' personal data, credentials or confidential information. They are process instructions, not data archives. Always check the terms of use of the platform you use and, if your company operates in a regulated sector, involve your data protection officer before adopting any AI tool.
| Risk | Probability | How to mitigate |
|---|---|---|
| Poorly written Skill that produces wrong outputs | High if untested | Test on 5-10 real cases before sharing |
| Sensitive data entered into the Skill | Medium | Written rule: Skills contain only process instructions, never personal data |
| Reliance on a single tool | Low but real | Document your Skills in plain text too, independent of the platform |
| Skills not kept up to date | High over time | Scheduled review every 4-6 weeks |
| Uneven adoption across the team | Medium | Short training (15-30 min) and a dedicated internal point person |
Why is everyone talking about it now: where do Italian companies really stand with AI?
The Italian AI market grew 50% in 2025, but most SMEs haven't invested yet. Those who start now still have a real competitive edge.
The Italian artificial intelligence market reached €1.8 billion in 2025, up 50% from the previous year, according to the Osservatori Digital Innovation of the Politecnico di Milano. But market growth doesn't mean widespread adoption.
The most telling figure for anyone running an SME: 76% of Italian SMEs have not invested in AI and have no plans to, and only 7% have launched structured training programs. If you start now, you're not behind: you're still in a favorable position compared to your competitors.
Among those already using AI, the results are tangible: according to the Microsoft Work Trend Index 2026, 58% of AI users say they produce results they couldn't have achieved a year ago. This isn't an abstract statistic: it means that anyone adopting tools like Claude Skills today is building an advantage that gets harder to close with every passing month.
How to get started: the practical steps to set up your first Claude Skill
You start with a single repetitive workflow, write the Skill in under an hour, test it on real cases and share it with the team. No technical expert required.
The most effective path is incremental: one Skill at a time, starting with the workflow that wastes the most time. As we described in our article on Codex Record & Replay, the principle is the same: automate starting from a concrete example, not from an abstract project.
- 1 · ManagerMap your repetitive workflowsAsk your team: which tasks do we do most often with the same rules? List 5-10 candidates.
- 2 · ManagerPick the candidate with the most impactPrioritize the most frequent workflow with the most stable rules. Avoid the ones that change often.
- 3 · Manager or team leadWrite the first draft of the SkillContext (who we are, who we serve), step-by-step instructions, constraints (what not to do).
- 4 · Team leadTest on 5-10 real casesRun the Skill on concrete examples from the past. Compare the output with what you would have written yourself.
- 5 · ManagerShare it and train the team in 15 minutesExplain when to activate the Skill, what to check in the output, and how to report problems.
- 6 · EveryoneReview every 4-6 weeksRules change. Update the Skill when your standards or your market change.
What mistakes are most common when setting up Skills?
The most common mistakes are instructions that are too vague, Skills that are too ambitious on the first try, and a lack of review over time.
People who start with Claude Skills tend to make the same mistakes. Knowing them in advance saves time and frustration.
| Common mistake | Why it happens | How to avoid it |
|---|---|---|
| Instructions that are too vague | Trying to cover too many cases with a single Skill | One Skill = one specific workflow. Five precise Skills beat one generic one |
| No testing before sharing | Assuming well-written instructions are enough | Always test on 5-10 real cases before sharing with the team |
| A Skill that is never updated | Set it up and forget it | Schedule a review every 4-6 weeks |
| Putting sensitive data in the instructions | Mistaking the Skill for an archive | Skills contain only process instructions, never personal or confidential data |
| Starting with a workflow that's too complex | Trying to automate everything at once | Start with the simplest repetitive workflow, not the most important one |
How do I know if Skills are really working for my team?
Three indicators are enough to tell whether a Skill delivers value: time saved, output consistency and adoption rate in the team.
You don't need a complex measurement system. Just ask your team three questions every month: how much time are we saving? Are outputs more consistent than before? Is everyone actually using it?
If the answer to all three is yes, the Skill works. If any answer is no, it's a sign something needs rethinking, usually the instructions or the team's training. As we saw in the article on AI without profits, the problem is almost never the tool: it's how it's used.
| Indicator | How to measure it | Alert threshold |
|---|---|---|
| Time saved per task | Compare the average time before and after adopting the Skill | If the savings are under 30%, revise the instructions |
| Output consistency | Sample review: 10 outputs per week | If more than 20% need significant corrections, the Skill should be rewritten |
| Team adoption rate | How many people use the Skill compared to how many could | If under 60%, training is lacking or the Skill is too complicated to activate |
| Update frequency | How many times the Skill was reviewed in the last 3 months | If never updated, it's probably already obsolete |
| Problem reports | Number of negative feedback items from the team | More than 2-3 per month: urgent review of the instructions |
So, is it worth setting up Claude Skills for my company?
It's worth it if your team repeats the same tasks several times a week with stable rules, and you want to standardize outputs without relying on each team member's memory.
Somewhere in between: you already use some AI tools but haven't systematized your instructions yet. Start with a single Skill on your most frequent workflow and measure the result after 30 days.
Not yet if your team uses AI only for occasional, always different tasks, or hasn't adopted any basic AI tool yet. Start with adoption before thinking about advanced automation.
How we see it at SHM
For us, tools like Claude Skills only make sense when they start from a concrete analysis of the team's processes, not from excitement over something new. AI should be integrated to free people from routine, giving back time for the work that really matters: client relationships, strategy, creativity.
We've worked with manufacturing companies and professional firms that had already adopted AI tools but used them in a fragmented way, with inconsistent results. The problem wasn't the tool: it was the lack of a structured process behind it. Setting up shared Skills was the step that turned individual, ad hoc use into a repeatable system.
If you want to understand which workflows in your team lend themselves to automation, the starting point is always an analysis of your existing processes. You can explore the topic in our hub on AI and process automation or learn more about our strategic AI integration and business process automation training services. If you'd rather start with a direct conversation, we're at /contatti.
Frequently Asked Questions
Yes, and it's often in small teams that the savings are felt most. With few people, every hour saved on repetitive tasks is one more hour for clients or growth. Even a single well-configured Skill can make a difference.
No. A Skill is text written in plain language, not code. Anyone who can describe a process clearly can write an effective Skill. The only skill required is the ability to explain the rules of your own workflow.
Skills contain only process instructions, not data. The main risk is accidentally putting confidential information into the instructions: one clear team rule is enough to avoid it. For regulated industries, always check the platform's terms of use and involve your data protection officer.
Claude Skills is a specific feature of Anthropic's Claude platform. It doesn't integrate directly with ChatGPT or other tools, but you can use different tools for different tasks. What matters is having a clear process for which tool to use for which workflow.
For a simple, well-defined workflow, under an hour: 20-30 minutes to write the instructions, 20-30 minutes to test on real cases. The longest phase is often the initial mapping of candidate workflows, but you only do that once.
Claude Projects are shared workspaces where you can save context and instructions. Skills are instructions specific to a type of task, which can be activated within a Project. They're complementary: the Project defines the general context, the Skill defines the rules for a specific workflow.
The real competitive advantage isn't using AI: it's using it so your team doesn't have to start from scratch every time.
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