Skill primers: teach a session your context in 30 seconds
A skill is a reusable file that primes a fresh AI session with your project, voice, and rules · stop re-explaining yourself.
::TL;DR · the whole lesson in three lines
- MOVEA skill is a reusable file that primes a fresh AI session with your project, voice, and rules · stop re-explaining yourself.
- DRILLYou will draft your first skill primer for a task you do at least weekly, then test it cold on a fresh session.
- WINYou have at least one working skill file checked in or saved durably.
::concept · what's actually happening
A skill (sometimes called a 'primer' or 'profile') is just a markdown file with a name, a description, and a body of instructions. When you invoke it, the AI loads that context as if you had typed it yourself. You write it once, you reuse it forever.
read full concept · 4 more paragraphs →collapse concept ↑
The economic argument is simple: if you find yourself typing the same paragraph of context at the start of three different sessions, that paragraph wants to be a skill. Re-typing it is paying tax for nothing.
Good skills are narrow and verb-shaped · 'review-pr' or 'draft-cold-email,' not 'general-helper.' The narrow framing means the model knows exactly what mode it is in and which knowledge to pull forward.
The skill description is the part the model uses to decide whether to invoke it · spend disproportionate effort on it. If the description is vague ('helps with stuff'), the model will guess wrong about when to fire. If it is sharp ('triggered when user pastes a GitHub PR URL'), the firing logic is reliable.
Skills compound across sessions in a way that chat history does not · they are durable, version-controllable artifacts. A six-month-old skill you forgot you wrote will still work tomorrow. A chat from six months ago is mostly gone.
::drill · do the thing
You will draft your first skill primer for a task you do at least weekly, then test it cold on a fresh session.
::L32 drill · copy-paste into any AI chat
I want to create a reusable skill primer for this task I do often: [DESCRIBE THE TASK · e.g. 'reviewing my marketing copy for brand voice,' 'triaging incoming support tickets']. Help me draft the skill file with: 1) a 60-character description that makes the firing condition obvious (when should this skill activate?), 2) a 200-word body that gives a cold AI session everything it needs to do this task my way (my context, my voice rules, my done-criteria, my common mistakes to avoid), 3) one example input and one example correctly-handled output so the model has a concrete pattern. Write it tight. No filler. No 'I hope this helps.' I will paste this into a skill file and use it for years.
::steps
- 01Pick a task you do at least weekly with mostly-the-same shape each time.
- 02Run the prompt and get a draft skill.
- 03Save it as a real skill file in your AI client's skills directory.
- 04Open a fresh session (no prior context).
- 05Invoke the skill cold and run it on a real instance of the task.
- 06Edit the skill body wherever the output drifted from what you wanted.
::outcome · what should be true
- You have at least one working skill file checked in or saved durably.
- A fresh session can do the task without you re-explaining context.
- The skill description is sharp enough that firing is reliable, not random.
- You can name three more skills you want to write next.
::trap · the most common failure
Operators write one giant 'helper' skill that tries to cover every task · then the model can never decide when to fire it, and the body is too generic to be useful. Narrow beats broad. Five tight skills beat one fat one every time.
::end of the curriculum
You're at Pilot level. There's no Level 6.
The next move is doing the work, not another lesson. If you want operator-grade infrastructure, that's /orangebox. If you want the lab's working journal, /founders-view. If you want to collaborate on the curriculum itself, the source is public on GitHub.
::other lessons at Operator level
Local AI · Ollama — privacy, offline, and the limit of free
At Operator level you need an honest opinion about local-only AI. Even if you don't use it daily, you should have run it once.
Model routing — switching between Claude, GPT, Gemini mid-task
Operators don't pick one AI. They route each task to the model that does it best. Knowing the strengths is the skill.
MCP servers — the plug socket that turned AI into a real tool
Model Context Protocol is the standard plug. Knowing what plugs in changes what your AI can actually touch — your files, your inbox, your calendar, your repos.
Agent mode — when AI takes action, not just answers
The frontier of useful AI is agents that DO things — browse, click, file, send. The actual skill is the safety pattern, not the magic.
Computer use — when AI takes the mouse and keyboard
Claude in Chrome, ChatGPT Atlas, computer-use beta — the frontier is AI that drives your browser like a human. Knowing the safety pattern is the actual skill.
What AI cannot replace — taste, judgment, relationships
The operators winning in 2026 are the ones who learned what AI is for and what is theirs. Knowing the line is more valuable than any prompt.
Agents 101: model plus tools plus loop
An agent is a model with tools running in a loop until done · know when you need one and when you don't.
MCP: structured tools for AI
Model Context Protocol is the USB-C of AI tooling · learn the shape before you wire anything.
Local models with Ollama
Run Llama, Qwen, or Mistral on your own laptop · no API, no logs, no monthly bill for the work that should stay home.
Vision models: when to use them
Vision lets the model see images · powerful for screenshots and diagrams · weak for precise spatial work · know the line.
Audio and Whisper transcription
Whisper turns audio into text · meetings, voice memos, interviews · the AI-era replacement for note-taking.
RAG vs long context: when to retrieve, when to dump
RAG fetches the right slice of your data at query time · long context stuffs everything in · know which problem you actually have.
Embeddings: meaning as numbers
An embedding is a list of numbers that captures the meaning of text · learn the shape and you unlock semantic search, deduplication, and clustering.
Fine-tuning vs prompt engineering
For individuals, fine-tuning is almost never worth it · know exactly when it actually is.
AI safety in personal use
PII, NDAs, financial data, and other people's secrets · know the rules of what you do not paste.
Multimodal prompting: combining text, image, audio
The strongest prompts use the medium that fits the question · sometimes you describe, sometimes you show, sometimes you do both.
Chain-of-thought: making the model show its work
Asking the model to reason step-by-step before answering raises accuracy on hard problems · know when it earns its cost.
Tool use and structured output
Function calling makes the model return JSON your code can use · know the contract before you build on it.
Cost optimization: tokens, caching, model selection
AI is metered · the operators who stay profitable measure what they spend and choose the model that fits the task.
::part of the AtomEons /learn curriculum · 45 lessons · 5 levels · cc-by 4.0