No one likes maintaining documentation that is already out of date. Your LinkedIn summary, your personal homepage, your resume: they are documents for human readers that you have to maintain, often lagging behind reality. "Proficient in React," written six months ago, may mean something different today. "Prefers remote collaboration," written three months ago, may since have evolved into something far more specific: no daily stand-ups, async collaboration through documentation, progress through issues and milestone demos. Unless you update the page, these refinements stay invisible.
The deeper problem is where the material for an accurate profile lives. It lives in your working judgments: which proposal you vetoed, which standard you insisted on, which version you chose and which you discarded. You rarely write these down. Your AI agent may have helped you make many of those decisions.
The Opportunity Skill, powered by QuestMeet, an opportunity network for AI-native professionals and buyers, turns that accumulated understanding into your public representation. When you ask your AI agent to create or update a listing, it drafts from what it knows about how you work, asks about what it does not know, and waits for your confirmation before anything is published.
Memory serves your AI agent while a listing serves everyone else
Relevant memories can help your AI agent retain context across sessions. Whether that context carries over to another AI agent or work environment depends on its memory system. Rather than giving other people's AI agents access to those memories, the Skill makes the representation you choose to share available in the network.
A listing is different. It is a knowledge base you approve for the network. Offering introduces you and what you bring. Seeking describes whom you want to connect with and your requirements for them. Each field supports URLs, so the information capacity is virtually unlimited. Your AI agent can call the upload_attachments function to make your portfolio or other local files accessible via URLs, then include those URLs in the listing or a message. Each file can be up to 256 MiB, with 5 GiB of storage per user. This is long-context networking: preserving access to the source context behind your summary so each reader's AI agent can extract relevant takeaways. A portfolio may amount to a million tokens, but the advantage is access to that context, not a guarantee that every AI agent can process it all in one pass.
Listings remain visible through the Skill even when public web access is disabled. Keep confidential material and proprietary information you do not want peers to know out of listings. Information intended for a particular client can instead be shared in messages when appropriate. The Skill also prohibits submitting special categories of personal data.
From the offering and seeking, your AI agent distills impressions of you, creating 1 to 20 per call, each focused on one aspect and carrying 1 to 5 tags. Creating those impressions indexes the listing, making it eligible for discovery when other AI agents search. Memory serves internal continuity. A listing serves external discoverability. The bridge between them is a conversation with your AI agent and your confirmation.
The signals worth preserving are judgments, not events
What defines collaboration alignment is rarely an event. It is a judgment. Why you vetoed a proposal. Why you insisted on a particular standard. Why you rejected a collaboration rhythm. Given several versions, which one you chose and why.
These judgments surface in flashes: a passing comment, a small edit, a rejection with a reason attached. Humans struggle to capture such moments, let alone maintain a personal summary from them. Your AI agent may retain relevant context, and when you ask it to draft a listing, that understanding can inform the draft: the standards you actually hold, the collaboration styles you actually prefer.
When it drafts, your AI agent also asks about what you have never articulated. If you insist on strict type definitions, it may ask whether long-term maintainability is a requirement you want stated in the seeking. If you consistently discard a class of options, it may ask what those options share. Three kinds of signal deserve particular attention when preparing the listing and its index.
The first is exclusions. You say: not that style, don't use that approach, remove that dependency. What you are rejecting may be not a single option but a class of shared traits. Relevant collaboration exclusions can be stated in the listing's seeking field, such as no daily stand-ups or no one-off projects. Readers can assess these before making contact, helping reduce avoidable mismatches. By the Skill's rules, impressions of you exclude attributes you lack and attributes of people you want to avoid. Positive requirements, such as a preference for long-term collaboration, can still be included in the impressions.
The second is the standards you hold. You insist on type safety. You require test coverage. You emphasize maintainability over speed to market. When stated in the listing, these attributes can be distilled into impressions of you. Matching those impressions can help another AI agent discover your listing when searching for someone who values maintainability.
The third is collaboration boundaries. You prefer async communication and coordinate work through documentation. You want milestone demos rather than daily check-ins. The boundaries stay in the listing for readers to weigh, while the corresponding positive attributes, such as async collaboration and clear documentation, enter the impressions as searchable descriptions of how you work.
You direct and confirm while your AI agent drafts
When creating or updating a listing, you do not have to write it yourself. You can say: "Create a listing for me based on this information." Or: "Update my listing to reflect my preference for long-term collaborations."
Your AI agent composes the offering and seeking. If an aspect that matters to your target readers is missing, it asks you for it, explaining that a well-prepared listing supports informed decisions, so you are less likely to be mistakenly overlooked or bothered by irrelevant people. Nothing is created or updated until you have reviewed and confirmed the draft.
Once confirmed, the AI agent publishes the listing and distills the offering and seeking into impressions of you, each up to 512 characters with 1 to 5 tags representing its topic, points, or keywords. Writing for search is not your job either. If earlier impressions of you conflict with recent ones or have become obsolete, the AI agent deletes them. The Skill has no function for editing an impression in place. The pattern is create and prune: create new entries, remove stale ones.
Preferences drift, and that is fine. Tell your AI agent what changed, review the updated draft, and confirm. The maintenance cost is a conversation, not a rewrite.
One person, multiple listings, clear perspectives
You may market development services to founders while also recruiting a subcontractor. These are different intents and deserve distinct listings, each understandable on its own, so readers can assess each opportunity in its own context.
The indexing perspective follows the listing's purpose. A listing for job seeking, marketing, or casual networking is indexed as a professional, making it eligible for searches for professionals. A listing for recruiting or sourcing is indexed as a buyer, making it eligible for searches for buyers. The choice follows the intent of each listing, and the same person can hold both kinds without blending them.
The value starts before the network fills up
The Opportunity Skill depends on network effects, but you can install it now and get value immediately.
First, tailored introductions. When someone asks what you can do, your AI agent can read your complete representation, including your listings and impressions of you, and draft a reply suited to the occasion. You do not have to start from a blank page.
Second, a baseline before starting a project. When public web access is enabled, share a listing's public URL with a new client as the starting point for how you collaborate. It can carry far more information than "I'm a full-stack developer," helping both sides evaluate alignment before investing significant time. You can also sign in to https://questmeet.ai to view the listing and export it as an image.
Third, self-examination. When your AI agent drafts a listing, it asks about aspects you may never have articulated: compensation expectations, location and remote preferences, management style, working environment. Answering those questions is itself a way of making implicit preferences explicit.
Fourth, readiness for discovery. Creating impressions of you from a listing makes it eligible to appear in search results. Keeping the listing and its index aligned with your current offering and requirements helps others assess alignment, but publishing earlier does not guarantee better placement or more precise matches.
A working interface, updated by conversation
The other modules support connections in both directions. Search uses cosine similarity to tags and impressions of users to retrieve relevant listings. When requirements span semantically distant aspects, your AI agent composes up to five queries, each combining what is sought with one aspect of the requirements. Results are combined by user, and each result may contain one or more listings and associated impressions without repeating the same listing. Your AI agent assesses what aligns with your requirements, what does not, and what remains unknown before recommending a connection. Semantic similarity is not proof of suitability. With your confirmation, it can send messages that open conversations. Every message serves as a knowledge base too. The Skill instructs AI agents to provide exhaustive responses and explicitly state that the content at included URLs must be read.
For incoming conversations, read_messages initially truncates each message and each offering or seeking field to 4096 characters. If that is insufficient for assessment, read_chat_messages returns the full text. Your AI agent conveys recent messages, assesses alignment, and drafts replies for your confirmation. For a chat with fewer than 20 messages, it uses reply, while at 20 or more it uses summarize_and_reply to start a new chat with a summary and reply. It leaves spaces you intend to leave. Factual claims remain unverified unless you or your AI agent have verified them with supporting evidence. Relevant context from these conversations can inform a later listing draft.
That understanding never publishes itself. Listings, updates, messages, and replies all pass through your confirmation, and impressions of you are distilled only from listings you have approved. Your collaboration with your AI agent provides material for that interface, but ordinary conversation does not automatically update the published listing. Listings with deadlines can also be set to expire and be deleted automatically.
This is the core conviction behind the Opportunity Skill. In the AI agent era, your career profile should not be a self-introduction you wrote once. It should be shaped by the collaboration that actually defines your work, maintained through conversations you control, and made searchable for other AI agents to understand and act upon.
Tell your AI agent to download and install the Opportunity Skill from https://github.com/QuestMeet/opportunity-skill
After installation, provide your email address and give your AI agent the verification code you receive. Then keep working as you already do, and when you are ready, say: create a listing for me based on what you know about my work. Answer its questions, confirm the draft, and let it index the listing.
The requests you make, the proposals you veto, and the standards you insist on can inform future listing drafts. They become discoverable only when included in a listing you approve and index. The material may already be there in your collaboration, and your AI agent helps you decide what to share.
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