Every engineering team experimenting with AI hits the same wall sooner or later.
The demos look great. The model answers questions, writes code, summarizes meetings. Then you try to use it inside real workflows. Suddenly things feel brittle. Outputs are inconsistent. Context gets lost. You are constantly re-explaining what the AI should do, how your team works, and what tools it is allowed to touch1.
The uncomfortable question many teams are starting to ask is simple and very human:
Why does the AI still feel like an intern with short-term memory, instead of a reliable teammate.
In 2026, a quiet shift is starting to answer that question. It is not about bigger models or better prompts. It is about skills2.
The Hidden Gap Between Models and Real Work
Large language models are incredible at reasoning in the abstract. But work is never abstract.
Real work is opinionated. It has constraints, conventions, and context that lives across tools like Slack, GitHub, Jira/Linear, and Notion. It involves permissions, edge cases, and habits that are rarely written down3.
Today, most AI systems try to bridge that gap with prompts and memory. That approach does not scale4. Every prompt becomes a mini instruction manual. Every new conversation resets expectations. Teams spend more time managing the AI than benefiting from it.
This is where the idea of skills enters the picture.
Instead of telling a model what to do every time, you give it capabilities with structure. A skill is a defined action the AI can perform, with clear inputs, outputs, and rules. Think less like chatting, more like giving the AI a reliable muscle memory5.
What Exactly Is a Skill in AI Terms
A skill is not just a tool call. It is not just an API wrapper either.
A skill usually combines:
- A clear purpose, like preparing a daily standup or reviewing a pull request
- Access to specific systems, such as GitHub or Linear
- Guardrails about when and how it can act
- A predictable output format that humans and systems can rely on
In practice, skills sit between raw model intelligence and messy human workflows. They translate intent into action.
This concept is showing up independently across the AI ecosystem, which is often a strong signal.
Anthropic describes skills as a core building block for agents, allowing them to perform structured tasks repeatedly and safely inside real environments6.
Cursor introduced skills as a way to give AI consistent behavior inside development workflows, reducing the need for repetitive prompting7.
Open source efforts like OpenSkills aim to standardize how skills are defined and shared, similar to how APIs evolved in the early web8.
Platforms like skills.sh and Smithery are experimenting with marketplaces and registries for reusable AI skills910.
None of these projects are identical, which is the interesting part. They all point in the same direction from different angles.
Why Skills Matter More Than Better Prompts
Prompt engineering helped teams get early wins. It also introduced a new kind of fragility.
Prompts are invisible infrastructure. They live in docs, in someone’s notes, or in a single engineer’s head. When that person leaves or the prompt changes slightly, behavior drifts.
Skills make behavior explicit and versioned.
Instead of saying “please prepare my standup like last time,” a skill defines what “prepare standup” actually means for your team. Which repositories to scan. How far back to look. How to format blockers. Where to post the result11.
This has three big effects:
First, reliability. The AI behaves the same way tomorrow as it did today.
Second, trust. Engineers can inspect and improve skills like they would any other piece of system logic.
Third, leverage. Once a skill exists, it can be reused across people, teams, and products.
This is why skills are quietly becoming more important than raw model improvements for teams doing real work.
The Standardization Moment
We are approaching a standardization moment.
In the early days of APIs, every service had its own conventions. Over time, patterns emerged. REST, OpenAPI, and common auth flows made integration predictable. Skills are heading down a similar path.
Projects like OpenSkills and AITMPL are early attempts to define portable, composable skill definitions that can work across models and platforms812.
If this succeeds, skills could become the unit of reuse for AI, just like libraries are for software. Teams would share skills the same way they share code.
That changes the economics of building with AI.
Where Skills Show Up First
The strongest early use cases for skills are operational tasks:
- Daily and weekly reporting
- Meeting preparation and follow-ups
- Code review summaries
- Incident timelines
- Planning and backlog hygiene
These tasks already follow patterns. They already consume a lot of human time. And they already span multiple tools.
Skills shine exactly in these workflows57.
This is why skills integrate naturally with existing tools rather than replacing them. Slack, GitHub, Linear, and Jira are not the problem. The problem is stitching context across them in a reliable way.
Skills become the glue.
Looking Ahead to 2026
It is unlikely that one skills framework will win outright.
What is more likely is convergence. Shared concepts, similar abstractions, and eventually interoperability. By 2026, skills may be as normal in AI systems as webhooks are in SaaS products.
When that happens, the question will no longer be whether AI can help a team. The question will be which skills the team has invested in.
That is a much healthier conversation.
At One Horizon, we think about skills as the practical interface between AI and how engineering teams actually work. Not as magic, not as replacement, but as structured, inspectable capabilities that earn trust over time.
If you are curious where this goes next, you can explore more at onehorizon.ai.
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Footnotes
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YouTube (2025). "The AI Skills Layer Explained". https://www.youtube.com/watch?v=GFM8cK4-BoM ↩
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skills.sh. "Skills Overview". https://skills.sh/ ↩
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Anthropic (2025). "Agent Skills Overview". https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview ↩
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Cursor (2025). "Context Skills". http://cursor.com/docs/context/skills ↩
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Open source effort (2025). "OpenSkills". https://github.com/numman-ali/openskills ↩ ↩2
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Anthropic (2025). "Agent Skills Overview". https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview ↩
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Cursor (2025). "Context Skills". http://cursor.com/docs/context/skills ↩ ↩2
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OpenSkills (2025). "Open Source Skill Definitions". https://github.com/numman-ali/openskills ↩ ↩2
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Smithery (2025). "Skill Marketplace". https://smithery.ai/skills ↩
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Smithery Docs (2025). "Skills in Practice". https://smithery.ai/skills/docs ↩
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AITMPL (2025). "Skill Execution Patterns". https://www.aitmpl.com/ ↩
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AITMPL Docs (2025). "Composable AI Skills". https://www.aitmpl.com/docs ↩
