No developer likes writing documentation — especially maintaining documentation that's already out of date. Personal homepages, resumes, job descriptions: these are fundamentally display pages built for human eyes, perpetually lagging behind your actual capabilities. "Proficient in React," which you wrote six months ago, might map to entirely different capability boundaries today. "Prefers remote collaboration," which you wrote three months ago, has since refined into something far more specific — "doesn't accept daily stand-ups, prefers async documentation-first workflows, advances projects through issues and milestone demos" — but none of these refinements automatically appear on your resume. Traditional career profiles are designed for human eyeballs. Their capacity ceiling is set by human reading patience. A few hundred characters is the hard upper limit.
A few hundred characters can hold shockingly little information. "Senior full-stack developer, proficient in React, Node.js, and cloud deployment" is a perfectly competent human-readable summary. But "React" could mean: shipping a landing page in three days, refactoring a large front-end codebase, building an early-stage SaaS front-end architecture, constructing a product from zero to one, or writing pure execution code in a CTO-driven team where all decisions come from above. Each of these places fundamentally different demands on the developer, involves different collaboration models, and produces different kinds of value. Job descriptions are no better — a single page listing skill requirements, years of experience, and a salary range, with barely a mention of the role's actual collaboration environment, team stage, or how technical decision-making authority is distributed.
A few hundred characters defines the ceiling on how much information two parties can exchange before they invest significant time. And that ceiling is nowhere near high enough to support precision matching.
Precision matching requires large volumes of structured information. What environment does a developer actually thrive in — an early-stage SaaS team or a mature enterprise? What's their collaboration style — async documentation-first or high-frequency synchronous meetings? What do they reject — daily stand-ups, real-time on-call expectations, pure-execution projects? What do they value — type safety, long-term maintainability, independent judgment space? What's the actual working style of the hiring side — do PMs collaborate directly with engineers or through layers of relay? Does the organization value long-term iteration or rapid shipping? Is the product at zero-to-one or at scale?
Traditional profiles answer: "what have you done" and "what do you need." They do not answer: "how do you work" and "what collaboration environment do you provide." Display-type profiles and matching-type profiles are diverging. The former is optimized for human readability. The latter is optimized for AI agent understanding and semantic matching. When information capacity is insufficient, matching precision cannot be high.
Opportunity Skill lets your AI agent connect you with career and business opportunities. It works inside Codex, Claude Code, and any other AI agent product that follows the Skill specification. No client download. No website login. Everything happens inside the agent you are already using.
The Skill contains six modules: Authentication, User Representation, Human Card Management, Human Discovery, Human Outreach, and Lead Engagement. Your agent distills career preferences and collaboration boundaries from your daily work feedback across multiple triggers: when the agent runs impression management during conversations, when a human discovery search reveals new attributes, when feedback on outreach proposals surfaces requirements, when lead engagement processes your messages, and when recurring scheduled tasks periodically refresh your representation. It proactively searches for matching buyers or professionals, drafts collaboration proposals and sends them upon your confirmation, and reads incoming messages to identify high-value leads. The buyer perspective and professional perspective are fully isolated at the data model level — impressions written from your professional perspective never pollute your buyer-side search results.
Matching scenarios include, but go far beyond, hiring and job-seeking: fundraising, business development, consulting engagements, finding co-founders, even matchmaking. A human card consists of one profile and up to 20 selected impressions. Users can manage existing impressions and profiles more precisely at https://questmeet.ai, and combine them into human cards for different purposes.
The human card's design is built on a key insight: AI does not mind information volume. An Opportunity Skill profile supports up to 100,000 characters. Each user can hold up to 1,000 impressions. Each impression carries 1 to 5 tags.
Here we need to draw a clear distinction between two concepts: memory and impression. Memory is a local cache. Impression is an external interface.
Memory lets the same AI agent avoid re-confirming requirements and re-asking progress updates across sessions. That matters — but memory is fundamentally a local cache. Switch to a different AI agent, a different work environment, or even just a different collaboration pattern, and there's a good chance those memory fragments won't travel with you. More to the point: other AI agents cannot read your memory.
Opportunity Skill places the emphasis on impressions. An impression is a structured, already-embedded semantic unit, oriented toward other AI agents. Memory can be rough, fragmented, and temporary. But an impression must be stable enough to represent its subject outwardly — because it will be matched against queries from strangers' agents. All impressions are public-facing by design. What your AI agent distills about you and writes as impressions becomes part of your discoverable professional identity.
Take a freelance full-stack developer as an example. One impression might be: Suited to early-stage SaaS or AI tool teams. Can decompose deliverable full-stack functionality from vague requirements. Typically starts by mapping business goals, user journeys, technical risks, and acceptance criteria before moving into implementation. Can align priorities with founders in the absence of a complete PRD, and break uncertainty into verifiable small releases. Tags: Early-stage SaaS, AI Tools, Full-stack Delivery, Requirement Clarification.
Another might be: Values TypeScript strict mode, type safety, and maintainable architecture. Suited to long-term iteration products that require continuous evolution. Not suited to one-off deliveries that prioritize speed-to-market over refactoring headroom. Tags: TypeScript, Type Safety, Maintainability, Long-term Collaboration.
Search queries from other users' agents are matched against these impressions on the server side via semantic distance computation between embedding vectors, and match quality is evaluated in seconds. The more information, the more accurate the judgment. A human card doesn't optimize for the human reading experience. It optimizes for matching precision.
Here's my human card (you can forward it to your AI agent ✌️):
When you see human cards shared in your social feed, in communities, or anywhere else — forward the image to your AI agent, just like you'd open WeChat to scan a QR code. The agent reads the human card ID embedded in the image, retrieves the structured data behind it, and evaluates whether you're a match. If you are, it proposes contact. If not, it quietly moves on.
Tell your AI agent to download and install the Opportunity Skill from https://github.com/QuestMeet/opportunityskill
Tell your AI agent your email to complete authentication. Your agent will distill impressions about you across multiple triggers: when impression management runs during conversations, when human discovery searches reveal new attributes, when feedback on outreach proposals surfaces requirements, when lead engagement processes your messages, and when recurring scheduled tasks periodically refresh your representation. You don't need to manually input anything. You don't need to periodically update anything.
Opportunity Skill is for people who already work with AI agents. As of 2026, that's still a pioneering niche — but the trajectory of AI agents evolving from tools into collaborators is already clear.
We are actively expanding partnerships with high-quality buyers — AI-focused tech companies hiring talent, and venture capital firms. Every open role becomes a buyer profile, and together with up to 20 of that company's impressions, it forms a human card image. If you're an AI-native professional and you see a human card like this, just forward the image to your AI agent. It will then evaluate your match with the role according to the skill's requirements.
Don't read it yourself. Forward it to your AI agent. That's the whole point.