Skills in AI Agent Development: From Prompt Fragments to Reusable Capability
While working on a recent AI agent project, I noticed a pattern in my workflow — one I’d used many times before with tools like Claude Code and Codex. It was useful enough that I wanted it in every project, not just this one.
So I asked myself: why repeat it manually each time if I can codify it once and reuse it everywhere?
Until then, I had mostly been a consumer of skills created by other developers. I particularly like the Super Powers skill. I also use Play Rec for web browsing, and I experimented with Gary Tan’s G-Stack skills. In the end, though, I felt G-Stack reflected Gary’s specific workflow more than mine, so I didn’t need all of that functionality. Still, it was a valuable experiment — and I learn best by building.
That’s when I decided to create my first skill.
The problem I wanted to solve
In larger projects, coding agents often forget where we left off, what was already built, and which paths had already failed.
Super Powers gives me great planning and spec documents at the start. But once implementation begins, reality changes: you hit edge cases, adjust decisions, discover better approaches, and course-correct. A lot of that practical knowledge gets lost unless you actively preserve it.
I wanted a better way to keep this implementation memory alive.
Journal.md: preserving real implementation context
My first idea was simple: a journal file that the coding agent updates after each meaningful batch of work.
That journal should capture: - what was implemented, - what challenges appeared, - which errors occurred, - where we changed direction, - and where I corrected or refined the agent’s suggestions.
The goal is straightforward: when we return to the project later, the agent should know what we already tried and why. This kind of context is far more granular than what usually goes into CLAUDE.md or AGENTS.md.
Manual.md: a practical operating guide for features
The second gap I noticed was what I now call manual.md.
This became especially important when I was using two agents on different parts of the same product: backend and frontend. On the backend, I was building a FastAPI service with multiple endpoints. On the frontend, I was building a Swift mobile app.
I needed documentation that went beyond standard docs: - exact endpoint behavior, - argument requirements, - database schema context, - implementation details behind key functions, - and integration nuances that matter when related features evolve.
In short, I wanted a working manual — not just API docs, but a developer-facing memory of how this feature actually works in this codebase.
My first custom skill
So I built a skill that extends the Super Powers setup.
In addition to plan/spec files, it adds: - a journal.md for implementation history, - and a manual.md for practical feature-level guidance.
Before drafting the skill itself, I did some research on how skills work in general — and how they differ from tools or from just writing better prompts.
In the spirit of learning by doing, I decided to document that journey in this post.
What are skills in the context of AI agent development?
One practical way to think about skills in agent development is this: a skill is usually a structured Markdown file.
At the top, there’s typically YAML front matter — a metadata block meant for the agent. From this block, the agent should understand:
- what the skill does (description),
- which version it is,
- what dependencies are required (tools, packages, runtime assumptions),
- and under which conditions the skill should be used.
The second part of the file is usually an overview — a high-level explanation of the skill’s purpose.
The final part is the execution section: step-by-step instructions for how to complete the task effectively, including required tools and action order.
I used this structure in my own project-documentation skill:
https://github.com/mottlio/project-documentation/blob/main/skills/project-documentation/SKILL.md
Most skills are defined in a single .md file. I think about them as modular prompt components that can be loaded into context when the task matches the skill’s description.
But skills can include more than Markdown. They can also bundle executable assets — shell scripts, Python files, and other code in accompanying directories.
That’s also where security risk appears.
Downloading random skills from public repositories (for example ClawHub/Claude Hub ecosystems) can be dangerous, because a skill may include executable logic that exfiltrates sensitive data to external services. This is not theoretical: security researchers have already identified unsafe skills in the wild. As one example, the RNWY report on AI skill files noted that 13.4% of scanned ClawHub skills contained critical security vulnerabilities.
Skills can also reference tools already available to an agent.
A simple mental model that helped me: - a tool is a hammer, - a skill is the procedure for using that hammer in a specific situation.
For example: if you need to work on a plasterboard wall, the skill tells you angle, sequence, and safety checks (like detecting beams or electrical cables) before striking.
How can you use a skill?
At the time of writing, I’m not aware of many centralized skill registries beyond ClawHub-like ecosystems for open Claude tooling. Many useful skills are simply distributed through GitHub repos with installation instructions.
For example, for my own skill:
# Add the marketplace
claude plugin marketplace add mottlio/project-documentation
# Install the skill
claude plugin install project-documentationOnce installed, the agent should often infer when to use the skill automatically.
You can also trigger it explicitly in your prompt, or define usage rules in files like AGENTS.md (or your equivalent project instruction file).
One of the biggest advantages of skills is on-demand context loading.
You can have many skills available, but only load the ones relevant to the current task. That matters, because context is limited and expensive.
The promise of reusable skills — the Matrix scenario
Thanks to the standardized structure of skills — and their growing portability across models and agent frameworks — we’re seeing an expanding ecosystem of community-built skills.
In many ways, this feels similar to software package ecosystems (Python, R, npm): you don’t need to reinvent everything from scratch if someone has already solved a similar problem and shared the solution.
In practical terms, that changes day-to-day agent work.
When you hit a challenge — integrating with an API, using a specific piece of software, or orchestrating an MCP workflow — you often don’t need to write a long prompt from zero. You can reuse a skill someone else has already formalized.
And when this works well, it feels almost magical: the agent can load the relevant skill and immediately operate with much better domain context.
It reminds me of The Matrix scene where Trinity needs to fly a helicopter and the skill is “uploaded” instantly:
Trinity learns to fly a helicopter
Team-level leverage: skill libraries
One particularly interesting direction is team-level skill sharing.
Manus AI, for example, describes a “Team Skill Library” model where team members publish their own skills and others can adapt them for their workflows.
That creates compounding benefits: - faster onboarding for less experienced teammates, - better knowledge transfer, - fewer repeated mistakes, - and more consistent execution across projects.
In tech environments where priorities shift quickly, that kind of reusable operational memory is a major advantage.
Could skill files become a universal AI programming layer?
A bigger idea is starting to emerge: skill files as a universal language for programming agent behavior.
Think of what HTML did for the web, or what SQL did for data.
Skill files could play a similar role for agent capabilities: structured, portable instructions that encode expertise and execution patterns in a reusable format.
Their main strength is modularity.
Like software components, skills package knowledge and workflows into units that can be shared, versioned, and improved over time — but aimed at dynamic agent behavior rather than static application logic.
Competitive implications and vendor lock-in
Widespread adoption of open skill standards could also reshape competition between major AI vendors.
If the same skill can run across multiple frameworks, switching costs fall.
As Plaban Nayak noted in his Medium article, open standards around agent skills can reduce framework silos and make capabilities more portable across platforms.
That would be healthy for the ecosystem: more interoperability, less lock-in, and faster innovation.
Final thought
As AI becomes embedded in more products and workflows, standardized skill layers may become one of the key accelerators of practical, interoperable AI.
In the meantime, my list of ideas for reusable skills just keeps getting longer.