I was catching up on product news over a long flat white when one update from Anthropic cut through the noise: Claude Skills are being opened up as an open standard for teaching AI repeatable workflows, with a strong push on portability across tools.[4]
It is not flashy. It is very practical. And for anyone stitching AI into everyday work, it is kind of a big deal.
New Feature / Update: Claude Skills as an Open, Portable Workflow Standard
What is it?
In simple terms, Skills let you define a repeatable AI-assisted workflow once, then reuse it across tools instead of rewriting prompts every time.[4]
Think of a Skill as a saved routine. For example:
- “Turn every incoming sales call transcript into a structured summary with: key pain points, budget indicators, next steps.”
- “Clean up raw product feedback from Typeform and group it by theme for the CX team.”
The key change Anthropic is pushing is this:
- Skills are designed as an open standard, not a closed, Claude-only trick.[4]
- The goal is that workflows you define in one place can be ported to other AI platforms or agents without starting from scratch.[4]
So instead of a mess of half-remembered prompts in ChatGPT one day, a Zapier zap the next, and a Make scenario on Friday, you get a more consistent, reusable unit of logic that can travel.
Why does it matter?
Honestly, most teams are not short of AI ideas. They are short of repeatable systems.
Right now, a typical week looks like this:
- A marketer experiments with ad copy in ChatGPT.
- A founder drafts investor updates with Claude.
- An ops manager wires up a one-off automation in Make.
Everyone is improvising. Very few of those wins turn into shared company workflows.
Claude Skills aim to solve that by making workflows:
- Explicit rather than buried in someone’s prompt history.
- Portable instead of locked to a single vendor.[4]
- Re-usable by non-technical teammates through connected tools.
Key facts at a glance
| Aspect | Detail |
|---|---|
| Feature | Claude Skills (reusable AI workflows) |
| Notable change | Positioned as an open standard for cross-platform portability[4] |
| Primary value | Define once, reuse across tools and teams |
| Typical users | Marketers, ops teams, analysts, product managers, founders |
| Where it helps most | High-repeat, text-heavy workflows that need consistent structure |
How real workflows could use Claude Skills
1. Marketing teams: from scattered prompts to shared campaign routines
Marketers are usually the first to play with new AI tools. By Wednesday they have ten great prompts for ad copy, landing pages, and email sequences. By the following Monday, half of them are forgotten.
With Skills, you can turn those scattered prompts into a shared library that plugs into your existing stack.
Example setup for a small marketing team
Tools in the mix:
- Claude for language generation
- Notion for content calendars
- Figma for quick layout references
- Zapier or Make for glue
Potential Skills you might define:
- Campaign brief generator
Input: product details, target audience, budget, channel focus.
Output: one standardised campaign brief with sections for objective, key messages, creative angles, primary KPIs. - Variation pack for paid ads
Input: one hero message plus landing page URL.
Output: a structured set of headline and description variants formatted for Meta and Google Ads. - Weekly performance narrative
Input: a small CSV export or pasted metrics from Google Ads and Meta Ads Manager.
Output: a human readable summary in Notion that compares week on week, highlights outliers, and flags under-performing audiences.
Once defined, these Skills can be triggered from your automation tool of choice. For example:
- New row added in a Notion “Campaign Requests” database triggers a Skill that drafts a first-pass brief and posts it back to Notion.
- End of week, a Make scenario pulls metrics, calls a “Performance Narrative” Skill, and drops the write up into Slack.
The nice part is you are not stuck if you change vendors. If you later prefer to route this through a different assistant, the Skill logic is portable by design.[4]
2. Operations and support: taming repetitive text work
Ops and support teams live in the land of repetition. Same questions, slightly different wording, every day.
Claude Skills can help turn that repetition into structure.
Example setup for an eCommerce ops team
Tools in the mix:
- Shopify for orders and inventory
- Gorgias or Zendesk for support
- Claude for language and reasoning
- Make for orchestration
Potential Skills:
- Order issue triage
Input: full customer email or ticket content plus order ID.
Output: categorised issue (shipping, product quality, payment, sizing), suggested response outline, required internal action. - Support macro draft
Input: ticket tag and short summary.
Output: a first draft response that matches your tone of voice and refund or replacement policy. - Inventory sync note
Input: low stock alerts pulled from Shopify.
Output: a compact summary for Slack with SKUs at risk, recent sales velocity, and a suggestion on reorder urgency.
In practice, you might:
- Set up a flow in Make where any ticket tagged “order problem” automatically calls the triage Skill, then posts the result into an internal Slack channel.
- Let agents trigger the macro draft Skill from a sidebar, then lightly edit before sending to the customer.
The advantage here is consistency. New agents can lean on the same Skills as senior staff. And again, because the Skills format aims to be open, you avoid getting locked into a single helpdesk or AI provider as your stack evolves.[4]
3. Analysts and product teams: standardising how you read messy data
Analysts and product managers are often drowning in survey results, NPS comments, app store reviews, and call transcripts. Each time, they build a quick one-off prompt to cluster or summarise.
Claude Skills give you a way to standardise that behaviour.
Example setup for a SaaS product team
Tools in the mix:
- Productboard or Jira for roadmap and tickets
- Typeform for surveys
- Zoom or Gong for recorded calls
- Claude plus an automation platform
Useful Skills here might be:
- Feature request classifier
Input: raw snippets from surveys or support tickets.
Output: a list grouped by feature area, with counts and example quotes. - Call transcript summariser
Input: call transcript exported from Zoom or Gong.
Output: a short summary plus problem themes, feature mentions, and urgency signals. - Release note draft helper
Input: list of completed tickets and internal engineering notes.
Output: customer friendly release notes in plain language.
You can then wire this into your weekly rhythm:
- Every Friday, survey responses from Typeform are fed through the feature request classifier Skill, and the output lands as a single doc for Monday planning.
- After every customer interview, a call transcript is auto summarised and linked directly to the right Productboard opportunity.
Where Claude Skills fit in a broader automation stack
This Skills move also fits into a bigger pattern in late 2025. Agentic AI is moving into core enterprise operations and standards are emerging to coordinate multiple agents and tools more cleanly.[4]
In plain language, the industry is slowly agreeing on how AI systems should talk to each other. Skills are one concrete piece of that puzzle.
Practically, this means you can start thinking in layers:
- Layer 1: Source tools
CRMs, helpdesks, spreadsheets, Shopify, calendars. - Layer 2: Automation glue
Zapier, Make, Pabbly Connect, custom scripts. - Layer 3: AI workflows
Skills describing how AI should process and structure information. - Layer 4: AI models
Claude, OpenAI, Gemini and smaller domain specific models.
Skills sit in Layer 3. They help you avoid re-baking logic every time you change Layer 4 or swap an app in Layer 1.
How to start using Skills without over-engineering it
If you are not living in Anthropic’s ecosystem yet, it still makes sense to start thinking in “Skills” terms.
A practical way to begin:
- Pick one high repeat workflow.
For example: generating campaign briefs, summarising sales calls, or grouping feedback. - Write it down as a clear, step based instruction set.
What goes in, what should come out, and in what format. - Save it as a reusable prompt or function in your current AI tool.
Even ChatGPT custom instructions or a stored system prompt work. - Wire it to an automation platform where it can be triggered by real events.
New row in a sheet, new ticket, new call transcript. - Refine with real usage data, then consider porting the logic into a more formal Skill format as Anthropic’s open standard matures.[4]
This does not need to be perfect. The value comes from capturing the workflow in a reusable, shareable way instead of letting it live in someone’s head or buried deep in their chat history.
When this update is worth your attention
Claude Skills as an open standard are worth a closer look if:
- You are responsible for operations, and you want less hand-holding when people join or change roles.
- You run marketing or growth, and you are tired of re-writing the same prompts across tools.
- You are building internal AI tools and want to avoid lock-in while your stack is still evolving.
For most teams, the next step is not a bigger AI model. It is cleaner, reusable workflows. Skills are a small but important move in that direction.[4]
