Knowing facts isn't enough
Facts are easy — your agent can look them up. But doing your best work isn't about facts. It's about processes. How to structure analysis. How to generate a report. How to review a pull request. How to onboard a new project.
Most AI tools can answer questions. Very few can learn how you work and encode that knowledge for the whole team.
How skills work
qbit.me runs on the qbit agent, which has a native skill system. Skills are on-demand knowledge documents that teach your agent how to handle specific tasks. They're procedural — not "what is true" but "how to do something."
And here's the critical part: your agent can create and update skills itself.
The learning loop: After solving a complex problem, the agent offers to save the approach as a skill. You approve. The next time you need that task done, the agent already knows how. Every interaction makes your craft smarter.
The learning loop in practice
Week 1: You teach the agent
You ask your agent to review a pull request. It checks for style issues, logic errors, and test coverage based on your team's standards. It produces a thorough review. Then it asks: "I noticed I run the same checks every time. Should I save this as a skill?"
Week 2: The skill exists
You type "/review-pr" or just ask naturally. The agent loads the skill and runs it. Consistent quality. No missed checks. No "I'll catch it in review later."
Week 3: Automation
You set the skill to run on every new pull request via webhook. The agent reviews each PR automatically, flags issues, and posts a summary. You only step in for the tricky ones.
What kinds of skills can your agent learn?
- Development: Run code reviews, lint projects, generate tests, scaffold new modules
- Writing: Draft documentation, edit proposals, format newsletters, ghostwrite in your voice
- Research: Summarize papers, compare tools, extract key findings, structure literature reviews
- Analysis: Review data for trends, flag anomalies, generate executive summaries
- Process: Onboard projects, run checklists, triage issues, produce weekly standups
Skills vs. memory
Both are persistent across sessions, but they serve different purposes:
- Memory is factual — what's true about your work, your tools, your preferences. It's injected into every session automatically by the agent.
- Skills are procedural — how to do something specific. They're loaded on demand, only when needed.
Memory tells the agent what's true. Skills tell the agent how to act. Together, they make your agent effective without wasting tokens on procedures it doesn't need right now.
Why this matters for you
- Consistency: Every time you ask, you get the same high-quality result. No drift, no shortcuts.
- Growth: Your agent gets smarter over time. Your own expertise compounds with every interaction.
- Transparency: Skills are plain markdown files. You can read them, edit them, approve them. Nothing is hidden in a black box.
- Ownership: You decide what the agent learns. Approve, reject, or refine — the craft stays in your hands.
Every AI tool starts from zero. Yours shouldn't. qbit.me builds what you need — so your agent learns your craft the same way you honed your expertise: by watching, doing, and remembering.