When you work with Claude Code or Codex every day, you probably find yourself repeating certain lines. "Don't use dashes." "Avoid the marketing tone." "Solve the problem by trimming what already exists instead of letting each revision grow longer." These stock phrases capture your traits and preferences with surprising accuracy, but your target clients have almost no way of seeing them.
What they can see is your resume, a document written six months ago, stuffed with words like "senior," "proficient," and "full-stack." A resume says what you can do. It cannot say in what environment you do your best work, what you refuse to accept, and what you insist on.
What your AI agent knows, your resume cannot express
One distinction is worth stressing up front. Memory is not impression. Memory belongs to this particular AI agent and this particular collaboration. Switch AI agents, or imagine someone who wants to work with you, and that memory cannot travel with you and cannot be read. An impression is a public interface, a structured, already-vectorized semantic unit written for AI agents other than your own to retrieve and match. Memory can be rough, fragmented, and temporary. An impression must be stable. When another user's AI agent searches for you, it calls a search API. The server computes the cosine similarity between the query and your impressions and returns ranked results.
Take a concrete example. A founder keeps telling his AI agent to strip out the marketing language and give him only the numbers. He would never write "values analytical rigor over narrative persuasion" on his resume, but his AI agent observes the pattern and distills it into an impression. That impression is exactly the signal other users' AI agents can actually use when they run semantic matching.
A good professional profile dares to state "what I don't accept"
The unwritten rule of traditional professional profiles is that you never write down what you won't accept. Writing "no projects shorter than three weeks" on LinkedIn makes you look difficult, so everyone only lists what they can do.
In semantic matching, "what I don't accept" carries no less weight than "what I can do." Matching is not just about finding relevant opportunities. It is also about filtering out the wrong ones.
If an AI agent only knows you "can do TypeScript," it will recommend every TypeScript project to you. If it also knows you don't take pure-execution projects, don't accept daily stand-ups, and don't accept work that chases speed at the expense of refactoring, it can block a large batch of superficially relevant but fundamentally mismatched inquiries at the search stage.
This is not negative information. It is match-quality information. In Opportunity Skill, boundaries like these are written as neutral, professional matching conditions, such as "better suited to collaborations with clear requirements where boundaries can be clarified together, and not suited to projects where requirements change frequently without any decision mechanism."
When you see a human card, forward it to your AI agent. Don't read it yourself.
A human card is made of one profile plus up to 20 impressions. Users manage their profiles and impressions on questmeet.ai, combine them into human cards for different purposes, and export each card as an image carrying a machine-readable ID. I consider this the most AI-native design in the product. When you see a human card, forward it to your AI agent and don't read it yourself. It works the same way we instinctively scan a QR code with WeChat.
Human cards circulate on X/Twitter, Jike, Reddit, Discord, and similar channels. The recipient's AI agent reads the image, learns about the person on it, and judges whether the two sides have the basis for collaboration based on what it knows about its own user. If it's a fit, it drafts a collaboration proposal for your confirmation. If not, it stays silent. No social cost, no awkward rejection. The sender doesn't have to repeat self-introductions either. One human card carries the complete profile and the most relevant impressions, and the work of understanding is handed to the recipient's AI agent.
The image below is my human card. I am looking for a marketing partner with AI-native thinking and capability. If you are interested, send this image to your Codex, Claude Code, WorkBuddy, Qoder, Trae, or any other AI agent that supports skills. Your AI agent knows you, and it has the patience to read my full introduction, so it can judge correctly whether we would work well together.
Let your AI agent maintain how others know you
The most important design decision in Opportunity Skill is that impressions are not written by you. Your AI agent writes them. You just work as usual. Make requests, reject unsuitable proposals, state your preferences. The AI agent extracts signals during collaboration and also discovers new attributes through touchpoints like search, proposal feedback, and message handling. When you first sign up, if your profile is still blank or filled with placeholder text, the AI agent builds a decent one for you first, then keeps refining it through daily work.
Your AI agent distills your attributes and preferences into 1 to 20 impressions, separated by your two perspectives as buyer and professional. The extraction method is concrete. When you say "delete X" or "I won't accept Y," it extracts the common traits of the excluded elements. When you choose among several proposals, it analyzes the differences between the adopted version and the discarded ones. When you repeatedly demand strict typing, test coverage, and maintainable architecture, that consistency across many interactions is more reliable than any self-reported skill.
Each impression is not a casual sentence. It is written to a spec. At most 512 characters, with 1 to 5 tags, built from multiple declarative statements, favoring concrete, objective description over adjectives, and avoiding starting every sentence with "the user." Tags are not decoration. They are the recall layer at search time, shrinking the candidate set first before vector comparison happens.
Refresh is real-time. The system uses create and prune rather than edit in place. Before inserting a new impression, the server first deletes old impressions under the same perspective whose cosine distance to the new one is less than 0.1. Even if two impressions are worded completely differently, as long as their semantics overlap heavily, the old one gets cleaned up automatically. There is no "modify an impression" API. Maintenance works by continuously appending better units and pruning outdated ones.
Before long, many career opportunities will be pre-screened by AI agents first, with humans making the final call. When a founder tells their AI agent to find a full-stack developer suited to an early-stage SaaS team, someone who can deliver independently, prefers async collaboration, and doesn't take short-term outsourcing, whether your impressions carry these signals determines whether you get seen.
If your profile only says "senior full-stack, proficient in React," the AI agent knows you can do React but has no idea whether you are that person. Opportunity Skill lets your daily collaboration with your AI agent settle into a representation that other AI agents can retrieve, understand, and act on. You don't need to become more disciplined. You just move the maintenance of your representation off your to-do list and into the workflow itself.
Tell your AI agent to download and install the Opportunity Skill from https://github.com/QuestMeet/opportunityskill