AI Skills: Teach AI to Work Your Way
tl;dr
- Skills are the workhorse, with Projects and prompts as supporting layers: Saved prompts reduce repetition and Projects carry who you are, but Skills are what govern how specific work gets done
- AI memory is improving but does not govern workflows: Memory captures preferences across sessions, but Skills are what tell your AI how a specific task should be done
- A Skill is written once and loads automatically: It encodes your standards, your format, and your workflow so the AI follows them without being told every time
- You can build your first Skill without technical knowledge: Describe the task to your AI, ask it to write the Skill for you, test it, and refine based on real use
- 36% of community Skills contain security vulnerabilities: Build your own for anything sensitive; understand prompt injection before downloading Skills from unverified sources
A marketing manager opens her AI assistant on Monday morning to draft the weekly competitive summary. She spends the first few minutes re-explaining the format her team uses, the tone her VP prefers, and the three competitors they track. On Tuesday she asks the same tool to write a client email. Same thing: re-explain the company voice, the approval process, the context that matters. By Friday she wonders if she's spent more time briefing AI than she saved using it.
This is a very common experience for new AI users, but it does not have to be. Skills, combined with a few other built-in tools, are how you stop re-explaining and start getting consistent output from the first line of every session.
Prompts, Projects, and Skills: How They Fit Together
Think of these three tools as building blocks, each one adding a layer of consistency on top of the last.
Saved prompts are the starting point. If you find yourself typing the same detailed request repeatedly, saving it means you can invoke it in a single copy-paste or click. It answers the question: what is the exact thing I am asking for right now? Saved prompts reduce repetition, but they do not carry background information about you and they do not govern how the work gets done.
Projects (called by various names across platforms) are the next layer. They are persistent workspaces that hold background information across multiple conversations: your company description, your role, your preferred tone, standing constraints. That information loads automatically in every session within the workspace without re-entering it. They answer the question: who am I, and what do I generally need from this AI? Projects give the AI a foundation, but they still do not tell it how to execute a specific task.
Skills are the layer above both. A skill is a structured instruction set that tells your AI how to perform a specific, repeatable task from start to finish, including the steps, format, constraints, and standards that apply. When a request matches a Skill's description, the AI loads those instructions automatically before responding.1 It answers the question: how should this type of work be done?
In practice, the three layers stack. A sales team might start with a saved prompt to trigger a competitive summary request, set up a Project to store their company positioning and target audience, and then build a Skill that governs exactly how that summary should be structured and what it must include. Each layer does something the others cannot.
Anthropic, the company behind Claude, formalized the skills approach in October 2025 and released it as an open standard in December of the same year.2 The standard, published at agentskills.io, has since been adopted by Microsoft, OpenAI, Cursor, GitHub, and other major platforms.3 A Skill built for one compatible platform will generally work across others that support the standard, though setup steps differ between platforms.
Memory Is Improving, But It Only Goes So Far
A reasonable question at this point: don't the memory features the major AI platforms keep adding already solve this? The platforms have made significant strides on cross-session memory. ChatGPT rolled out persistent memory to all users by mid-2025, and as of March 2026, Claude made memory available by default across all plans including free.4 These systems learn your preferences, your role, and your working style over time. That is real progress.
But there is a meaningful gap between what memory does and what skills do. Memory captures high-level preferences: your tone, your industry, your general context. It is not designed to govern how a specific workflow should execute. The marketing manager's AI might remember she prefers a direct tone and works in B2B software. It will not remember that her competitive summary needs three sections, tracks specific competitors, follows a format her VP approved last quarter, and should never include unverified claims. That level of task-specific instruction is not what memory is built for. It is what skills are built for.
The Value of Skills
Consider the tasks your team asks of AI most often. Status reports. Proposal drafts. Email responses. Competitive summaries. Meeting recaps. Social media posts. Each one has implicit standards that are instantly noticeable when violated, and none of them are the kind of thing memory captures automatically.
Skills make those standards explicit and automatic. A sales team that builds a skill for drafting outreach emails encodes the company voice, the preferred structure, and the call-to-action format. Every draft using that skill starts from that foundation, regardless of who initiates the request.
The productivity case is real. In a December 2025 study of its own engineers and researchers, Anthropic found that employees reported a 50 percent self-reported productivity improvement from structured AI use, with 27 percent of Claude-assisted tasks being work that would not have been completed at all without the tool.5 Skills are what move AI from occasional assistant to reliable workflow layer.
The best candidates for your first Skill are tasks that are repetitive and currently inconsistent. If different people on your team ask for the same output and get meaningfully different results, a Skill that standardizes the approach pays for the build time almost immediately.
How to Build Your First Skill
The following walkthrough is specific to Claude. Other platforms that support the Agent Skills standard follow similar logic but differ in where Skills are stored and how they are activated. Check your platform's documentation for setup specifics.
The most direct path to your first Skill is asking Claude to help you write it. You do not need to start from a blank page or understand file formats. Claude can draft the Skill document based on a description, and you refine until the output matches your expectations.
Step 1: You can have Claude help you write a Skill by starting a new chat and typing "Help me write a Skill".
Step 2: Then describe the task you want to standardize. Be specific about output format, tone, constraints, and what good looks like. Example: "I want a skill for weekly project status reports. Structure: summary of progress, blockers, next steps. Tone: direct and brief. Each section: no more than three bullet points." And when you submit this, Claude will generate a Skill you can review, edit, and save.
Step 3: Test before you rely on it. Paste the Skill instructions at the start of a new conversation and ask Claude to complete the task using them. If the output misses the mark, identify which instruction is ambiguous, revise, and test again.
Step 4: Once you are satisfied with the Skill, save and enable it in Claude. In Claude's settings, navigate to the Skills section, create a new Skill, and follow the instructions.
Step 5: Iterate based on real use. The first version is rarely the final version. As you use it you will notice gaps. A Skill refined over time becomes significantly more reliable than one written once and left unchanged.
Step 6 (optional): Once you are comfortable with the basic flow, try Claude's built-in "skill-creator" Skill. In a new chat, type /skill-creator to launch a guided process that walks you through creating a Skill interactively. It is a more structured alternative to the freeform approach above, and useful once you know what you want.
This process requires no technical background. If you can describe a task clearly enough for a capable employee to follow, you can build a Skill.
Skills and Security Awareness
Because the agent Skills standard is open, a growing ecosystem of publicly available Skills has emerged. The appeal is obvious: why build something when someone else has already refined it? The answer comes down to trust.
In February 2026, security researchers at Snyk audited 3,984 publicly available agent Skills.6 They found that 36 percent contained security vulnerabilities, and that a coordinated malware campaign had distributed more than 30 malicious Skills through a major public registry. These Skills looked legitimate on the surface while containing hidden instructions designed to redirect the AI's behavior or extract data.
This is the mechanism known as prompt injection: instructions hidden inside content the AI reads, causing it to act on commands you never gave. A malicious Skill is prompt injection delivered at the instruction level. When you build your own, you know exactly what instructions your AI is following. When you download one from an unknown source, you do not.
The Snyk research focused primarily on developer tools where Skills execute locally with elevated permissions. For non-technical users working through a standard chatbot interface, the exposure is narrower. But the principle holds for everyone: any external instruction set deserves the same scrutiny as any third-party tool you install on a company device.
Build your own or use your organization's approved Skills for any workflow that touches sensitive data, client information, or financial records. Partner-built Skills from verified enterprise sources distributed through official directories carry substantially lower risk. Community Skills from unverified sources are not safe by default.
A well-built Skill is a set of instructions you trust because you or your organization wrote them. Prompt injection is a set of instructions you did not write and did not authorize. Understanding that distinction is the foundation of responsible AI use.
Final Thoughts
AI memory features are improving, but they are not a substitute for deliberate workflow design. Skills give your team a system for encoding your standards and processes into something reusable, consistent, and yours, with Projects and saved prompts as supporting layers when needed.
Start with one task your team repeats regularly and currently handles inconsistently. Build a Skill around it. Test it, refine it, and let real use improve it over time. Build your own, know what you are loading, and be deliberate about what your AI reads from external sources. Good security habits combined with the right building blocks are the foundation of a strong AI ecosystem for you and your organization.
References
- Agent Skills — Anthropic
- Anthropic Launches Enterprise Agent Skills and Opens the Standard — VentureBeat
- Anthropic Opens Agent Skills Standard, Continuing Its Pattern of Building Industry Infrastructure — Unite.AI
- Use Claude's Chat Search and Memory to Build on Previous Context — Anthropic Help Center
- How AI Is Transforming Work at Anthropic — Anthropic
- ToxicSkills: Snyk Finds Prompt Injection in 36% of AI Agent Skills — Snyk
