The Anthropic Agent Skills standard

# Run incident retro

## Triggers
- P1 or P2 closed
- Post-mortem scheduled

## Steps
1. Pull timeline from Linear
2. Pull Slack threads tagged #incident
3. Draft 5-whys with on-call author
4. Post draft in #postmortem

## Confidence
0.91 (24 successful runs)

In late 2025, Anthropic released the Agent Skills standard. It is a simple idea with significant implications for the AI ecosystem.

A Skill is a file in a specific format (SKILL.md) that describes a procedure: a sequence of steps an AI agent can follow, with triggers, decision points, and metadata. Any AI tool that supports the standard can load and execute Skills written in this format.

This article explains what the standard is, why it matters, and what it changes about how AI agents share knowledge.

What a SKILL.md file looks like

A SKILL.md file is a Markdown document with specific structure. Here is a simplified example for a customer refund procedure:

# refund_customer

## Description
How our team handles customer refund requests.

## Triggers
- Customer requests refund via email or Slack
- Subscription cancellation with active billing period

## Steps
1. Pull customer's billing history from Stripe
2. Verify subscription is within 60-day refund window
3. Check usage patterns to determine partial vs full refund
4. Process refund in Stripe
5. Update customer record in CRM with reason
6. Notify finance team with refund amount and quarter impact

## Confidence
Based on 47 successful invocations over 6 months.

## Permissions Required
- Stripe admin access
- CRM write access
- Slack #finance channel

The structure is intentionally simple. Anyone reading it can understand what the Skill does. Any AI tool that supports the standard can parse it, identify the steps, and execute them.

This is the key architectural choice: a Skill is a portable text file, not a vendor specific configuration. The same Skill works in Claude, Cursor, ChatGPT, and any other Skills compatible AI tool.

Why the standard matters

Three structural shifts emerge from this kind of standard.

1. AI procedures become portable across tools. Before the standard, every AI agent platform had its own workflow definition format. A workflow built for one platform could not move to another. The team’s institutional knowledge was locked inside whichever platform they chose. The standard breaks this. A Skill compiled once can be loaded by every AI tool.
2. The ecosystem can specialize. When portability is built in, different tools can specialize in different parts of the value chain. Some tools focus on authoring Skills. Some focus on executing them. Some focus on extracting them from observed work. The whole ecosystem becomes more capable when each tool can focus on its specific contribution.
3. Skill libraries can build as community resources. Open libraries of high quality Skills can emerge, similar to how open source code libraries emerged. Teams can share Skills, fork them, and improve them. The collective intelligence of the AI ecosystem grows faster than any single company could grow it alone.

This pattern is familiar from other technical standards. SQL became a standard for databases and unlocked decades of database specialization. HTTP became a standard for web protocols and unlocked decades of web specialization. The Anthropic Agent Skills standard is positioned to play a similar role for AI workflows.

What Pulse’s role is in this ecosystem

Pulse is in the “extraction” category. We do not ask humans to write Skills; our system compiles them from observed team behavior. The output is SKILL.md files in the standard format.

These files can then be:

• Loaded by Claude Desktop for hands on use by team members
• Loaded by Cursor for engineers working on related code
• Loaded by custom GPTs in ChatGPT for specific workflows
• Loaded by any other Skills compatible AI tool

The team’s institutional knowledge, captured by Pulse from how they actually work, becomes deployable to every AI tool the team uses. The team does not have to author Skills (which most teams do not do effectively, as we covered in the Skills cornerstone). They just work normally, and Pulse extracts what they are doing into portable files.

This positioning matters strategically. Pulse is not trying to be every AI tool. We are the upstream layer that makes every other AI tool more useful by providing portable, calibrated, team specific Skills.

What this means for buyers

When evaluating AI tools, the Skills standard creates a new evaluation question: does this tool support portable Skills?

Tools that support the standard:

• Pulse (extracts and exports Skills)
• Claude Desktop (loads and executes Skills)
• Cursor (loads Skills, limited execution)
• Custom GPTs in ChatGPT (loads Skills, limited execution)
• Various MCP compatible agents

Tools that do not yet:

• Most enterprise AI search tools
• Most workflow automation platforms
• Most proprietary agent platforms

The trend is clearly toward standardization. Tools that do not support the standard will either adopt it or become increasingly isolated. For buyers, choosing tools that support the standard is a hedge against vendor lock in.

What to expect over the next 24 months

Three predictions about how this ecosystem evolves.

1. Skill libraries will emerge. Public libraries of high quality Skills for common workflows (customer support, sales operations, engineering processes) will appear. Some will be open source. Some will be commercial. The best ones will become reference implementations.
2. Extraction will improve. Tools that compile Skills from observed work (like Pulse) will get more sophisticated. The quality of extracted Skills will approach the quality of expertly authored ones, while covering many more workflows.
3. Execution will get more capable. AI agents that load and execute Skills will become more reliable. The combination of standardized format plus improving execution plus auto extraction will make AI agents practical for an expanding range of work.

For software teams in the 5 to 500 segment we discussed in the segment cornerstone, this means the AI agent landscape will continue to mature in their favor. Tools designed for their scale, built on the open standard, will become increasingly capable over the next two years.

Pulse is positioned at the upstream extraction layer. As the ecosystem matures, our compiled Skills become more valuable to every downstream tool the team uses. The live demo at pulsehq.tech shows the extraction in action.