Observe. Build. Gather intelligence. Define your luck surface area.

Phase 1: Strategic Reconnaissance and Platform Deconstruction (Tasks 1–15)

The foundation of the Opportunity Finder lies in a deep, forensic understanding of the platforms it seeks to emulate and integrate. We cannot build a universal interface without first reverse-engineering the logic, data structures, and user flows of the target ecosystems. This phase focuses on dissecting the "Big Three" verticals: Co-founder Matching, Gig Economy, and Employment.

1.1 Co-Founder Ecosystem Analysis and Logic Cloning

1. Introduction: The Great Bifurcation of Talent Markets

The allocation of human capital within the modern innovation economy operates through two distinct, often diametrically opposed, market mechanisms: the traditional labor market and the co-founder mating market.

While superficially similar—both function to pair human talent with productive enterprise—their underlying economic drivers, psychological contracts, and valuation metrics differ so fundamentally that they constitute separate asset classes of human interaction. The job market is a mechanism for the exchange of certainty: a transaction where "track record" and retrospective proof of competence are traded for immediate, low-risk compensation in the form of salary.1 It is an economy of commoditized skills, cost reduction, and efficiency, governed by the logic of the "agent" who seeks to maximize return on time invested with minimal risk.

In sharp contrast, the co-founder market is an exchange of potential: a speculative, forward-looking transaction where "chemistry," shared vision, and future growth capacity are traded for deferred, high-risk equity.3 It is an economy of idiosyncrasy, value creation, and resilience, governed by the logic of the "principal" who seeks to maximize the terminal value of the enterprise, often at the expense of immediate comfort or security. The co-founder market has more structural commonalities with the "mating market" (marriage and family formation) than with the labor market (job descriptions and shorter term objectives like cost reduction), a reality that necessitates entirely different platforms, legal frameworks, and psychological heuristics for successful participation.

We are currently witnessing the industrialization of this previously artisanal market.

There are established platforms for funding and mentorship like Y Combinator (YC) which provides up $500,000 in funding, in exchange for a roughly 10% stake in the company and could be described at the big fish in the pond of top accelerators, but there is no shortage of excellent alternatives that cater to different founder profiles and stages of the startup lifecycle:

• Techstars is very prominent global alternative with over 50 programs worldwide. It provides $220,000 in funding in exchange for an equity stake of approximately 6-9% and has a massive network of 3,900+ mentors.

• 500 Global may be best for startups targeting international growth or emerging markets. It typically offers $150,000 for 6% equity.

• AngelPad is a highly exclusive "anti-hype" choice that focuses on B2B startups with strong technical foundations. It offers $120,000 for about 7% equity.

• HF0 Residency: A unique "mansion-style" accelerator providing a $500,000 investment for 2.5% equity, covering all living expenses for 10 teams of exclusively selected founders to allow those individuals to focus entirely on building.

• Antler is solo founders from the very beginning of their journey, before these people have a fleshed out idea or viable prototype or much of anything in the way of a team. It begins with an application and interview/chat conversation to screen applicants; successful applicants receive an offer with details of a cohort of Antler residents which involves a full+ full-time commitment. They invest roughly $125,000 and help you build from scratch.

• Entrepreneur First (EF) is perhaps similar to Antler, EF caters to individuals who attend weekend retreats or hackathons. It's even more explorative and focused on networking between creative people, rather than looking at early stage companies. The goal is help exceptional raw talent in tech to find co-founders and ideate.

Co-Founder Matching, CoffeeSpace, and Antler are attempting to codify the intangible qualities of "founder fit"—grit, curiosity, and psychological compatibility—into algorithmic interactions.5 This technological intervention signals a shift from serendipitous matching (college roommates, former colleagues) to systematic, data-driven pairing. This report provides an exhaustive analysis of this bifurcation, exploring the economic theory, behavioral psychology, and institutional architecture that separates the hiring of an employee from the "wedding" of a co-founder.

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2. Theoretical Frameworks: The Economics of Certainty vs. Ambiguity

To understand the divergence between these markets, one must first analyze the economic nature of the assets being traded. In the labor market, the asset is "Labor Power"—the capacity to perform defined tasks for a defined period. In the co-founder market, the asset is "Founding Capacity"—the ability to navigate undefined chaos to create new value.

2.1 The Currency of Valuation: Track Record vs. Slope

The traditional job market is retrospective. It functions on the premise that past performance is the best predictor of future behavior in stable environments. The Curriculum Vitae (CV) is a historical ledger, a document of "track record" that reduces information asymmetry by proving that a candidate has successfully executed a specific function (e.g., "Managed a $5M marketing budget" or "Wrote Java for 5 years").8 Hiring managers act as risk-averse purchasers, seeking to minimize the variance of outcomes. The question driving the transaction is: "Have you done this before?" This focus on track record inevitably biases the market toward specialization and cost reduction; if a candidate has done the job before, they can theoretically do it faster and cheaper this time.10

The co-founder market, however, is prospective and operates in environments of extreme instability where "past performance" is often irrelevant or even misleading. A track record of managing a large team at Google may be negatively correlated with the ability to build a product from zero in a garage, a phenomenon often described as the "big company overhang".11 Instead of track record, the co-founder market values "Slope"—the rate of an individual's learning and adaptation over time. As Sam Altman and Paul Graham of Y Combinator have noted, the most successful founders are often those with the steepest trajectory of improvement, regardless of their absolute starting point.12

This valuation of "potential" over "proof" fundamentally alters the selection mechanism. In a job interview, a candidate who admits "I don't know how to do that yet" is often disqualified. In a co-founder "date," that same admission, followed by "...but I will figure it out by Monday," is a signal of the high-agency "founder mindset".14 The currency here is not the skill itself, but the meta-skill of acquiring new skills rapidly under pressure. This is why "potential" and "growth" are cited as the primary drivers of the co-founder market: the startup effectively bets on the integral of the founder's learning curve over time, rather than their static value at day one.

2.2 The Compensation Structure: The Principal-Agent Divergence

The economic structure of the relationship dictates the psychological contract. The job market is built on the "Principal-Agent" model. The employer (Principal) hires the employee (Agent) to perform a service. Because the Agent does not own the output, their incentive is to maximize their wage while minimizing their effort, creating a misalignment that requires management, oversight, and "carrots and sticks" (bonuses and performance reviews) to correct.15 The compensation—salary—is a fixed, senior claim on the company's cash flow. It is paid first, regardless of profitability, representing a low-risk, capped-upside tranche of capital.

The co-founder relationship is a "Principal-Principal" model. Co-founders are peers who share the residual claim on the company's value (equity). They get paid last, only after all creditors and employees are satisfied. This structural reality aligns incentives perfectly: neither partner can succeed unless the enterprise succeeds. This alignment is why "cost reduction" is rarely a priority in co-founder matching; you do not look for the "cheapest" co-founder, you look for the one who maximizes the probability of the outcome.4

The "Zero-Salary" phase serves as a critical sorting mechanism in the co-founder market, filtering out those with high time preference (who need immediate gratification) and selecting for those with low time preference (who are willing to delay gratification for a potentially massive future payout).1 This economic filter is why the co-founder market often feels "elitist" or inaccessible; it requires a financial and psychological safety net that allows individuals to operate without income for extended periods, a constraint that heavily shapes the demographics of the founder pool.

Table 1: Structural Economic Divergence

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3. The Psychology of Compatibility: Mating Theory in the Boardroom

If the economics of the co-founder market resemble a high-stakes investment, the psychology resembles a marriage. The "founder dating" analogy is not merely colloquial; it is structurally accurate. Both relationships involve indefinite time horizons, total integration of finances and reputation, and high exit costs. Consequently, the mechanisms for evaluating a partner shift from assessing competence (Can they do the job?) to assessing character and chemistry (Can I survive a crisis with them?).3

3.1 The "Big Five" Personality Traits and Founder Dyads

Research into organizational psychology suggests that "Person-Organization" (P-O) fit—the alignment between an individual's values and the entity's mission—is a far stronger predictor of founder success than "Person-Job" (P-J) fit, which governs the employee market.18 The Five-Factor Model (Big Five) provides a rigorous framework for analyzing this compatibility:

1. Openness to Experience: This is the hallmark of the entrepreneur. Founders typically score significantly higher on Openness than the general population.20 However, compatibility requires nuance. A dyad where both founders are in the 99th percentile for Openness but low in Conscientiousness often results in "ideation loops"—a startup that constantly pivots but never ships. A balanced dyad often pairs a high-Openness "Visionary" with a moderately Open but high-Conscientiousness "Builder."
2. Conscientiousness: This trait, associated with orderliness, duty, and achievement striving, is the engine of "relentless execution" cited by Sam Altman.12 In the job market, Conscientiousness is universally positive. In the co-founder market, it can be a source of conflict if mismatched. A hyper-conscientious founder may view a more chaotic, creative partner as "lazy" or "undisciplined," leading to resentment.
3. Neuroticism (Emotional Stability): Startups are emotionally volatile environments. High Neuroticism is a significant liability, as it correlates with poor stress management and burnout. The co-founder market ruthlessly filters for "emotional resilience"—the ability to maintain equilibrium during the "Trough of Sorrow".21
4. Agreeableness: This is the most complex trait in founder dynamics. While high Agreeableness facilitates smooth social interactions (useful for employees), successful founders often exhibit lower Agreeableness, allowing them to challenge social norms and make difficult decisions (e.g., firing friends, pivoting). However, between co-founders, there must be enough Agreeableness to facilitate "repair attempts" after conflict. A "double-disagreeable" pair may implode from constant fighting; a "double-agreeable" pair may fail from avoiding necessary conflict (Groupthink).22
5. Extraversion: Complementarity is key here. The classic "Hacker and Hustler" archetype is essentially a pairing of an Introverted product builder with an Extraverted sales leader. This division of labor allows each founder to operate in their zone of psychological comfort while covering the startup's diverse needs.23

3.2 Attachment Theory and Conflict Resolution

The co-founder market places a premium on conflict resolution styles that is virtually non-existent in the job interview process. In a corporate setting, conflict is often suppressed or managed through HR structures. In a startup, conflict is existential. If co-founders cannot resolve a disagreement about strategy, the company dies.

Using John Gottman’s research on marital stability, we can analyze founder dynamics. The "Four Horsemen"—Criticism, Contempt, Defensiveness, and Stonewalling—are as toxic in a co-founder relationship as in a marriage. Platforms like Y Combinator advise founders to "work on a project together" specifically to induce stress and observe the partner's reaction.24 This "stress test" is designed to reveal the partner's attachment style:

• Secure Attachment: The partner views conflict as a problem to be solved together.
• Anxious Attachment: The partner needs constant reassurance and may spiral during silence.
• Avoidant Attachment: The partner withdraws (stonewalls) when things get tough.

Questions such as "When you feel stressed, do you tend to want to talk about what's going on or avoid talking about it?" 25 are standard in the co-founder dating playbook because they probe this psychological infrastructure. In the job market, such questions would likely be considered intrusive or irrelevant to the technical requirements of the role.

3.3 The "Chemistry" of Shared Delusion

Finally, the co-founder market requires a shared "reality distortion field." Founders must mutually agree to believe in a future that does not yet exist and often contradicts current data. This shared belief system acts as the "glue" during periods of failure. In the job market, an employee who ignores market data is "incompetent." In the co-founder market, a founder who ignores market data to pursue a contrarian vision is "visionary" (provided they are eventually right). This necessity for "shared delusion" drives the market toward "chemistry" and "vibes" over resume data points—you are looking for a co-conspirator, not just a colleague.3

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4. The Industrialization of Serendipity: Platforms and Algorithms

For decades, the co-founder market was localized and inefficient, relying on serendipitous networks (universities, previous employers). The rise of dedicated matching platforms represents the industrialization of this market, attempting to solve the "liquidity problem" of talent by aggregating high-intent individuals and applying algorithmic filters.

4.1 Y Combinator (YC) Co-Founder Matching: The Institutional Standard

Y Combinator's platform is arguably the most significant development in the co-founder market. It functions as the "Harvard" of founder dating—a high-status, high-intent marketplace.

• The Mechanism: The platform utilizes a robust profile system that mimics the rigorous YC application. It captures not just functional skills (coding, design) but also "softer" preferences like "interest in B2B vs. Consumer," "location preferences," and "commitment level."
• The "Vetting" Effect: The primary value of the YC platform is not just the software but the brand filter. By situating the matching tool within the YC ecosystem (Startup School), it self-selects for individuals who subscribe to the "YC ethos"—rapid iteration, MVP focus, and ambition for venture scale.5 This creates a homogenous pool of "high-agency" individuals, reducing the risk of matching with a "lemon" (someone who wants to play startup but isn't committed).
• Algorithmic Logic: The matching algorithm prioritizes complementarity over similarity. It actively seeks to pair "Builders" (engineers) with "Domain Experts" (industry insiders). It avoids matching two non-technical founders, recognizing that such pairings often struggle to ship product. With over 100,000 matches and 40,000 profiles, YC has created the deepest liquidity pool in the world for this specific asset class.26
• Success Cases: The platform has successfully engineered matches that led to funded companies. Whalesync, founded by Curtis and Matthew, is a prime example. Curtis, a technical founder with a Google exit, met Matthew, a product expert, through the platform. They explicitly state they would never have crossed paths otherwise.5 This proves that algorithmic matching can bridge structural holes in social networks that traditional organic matching cannot.

4.2 CoffeeSpace: The "Hinge" of Founder Dating

While YC offers an institutional approach, CoffeeSpace adopts the vernacular of modern dating apps ("Swipe Right").

• Behavioral Design: By using a swipe-based interface, CoffeeSpace lowers the cognitive load of "prospecting." It acknowledges that finding a co-founder is a numbers game. The interface encourages "discovery"—allowing founders to see profiles they might not have explicitly filtered for but who spark interest.6
• Verification as Trust: A critical innovation of CoffeeSpace is its integration with Proxycurl to pull verified data from LinkedIn. In online markets, "resume fraud" is a risk. By automating the verification of education and past employment, CoffeeSpace solves the "Track Record" validation problem instantly, allowing the human interaction to focus entirely on "Potential" and "Chemistry".6
• The "Playground" for Exploration: CoffeeSpace positions itself for the earlier stages of the funnel—the "curious" phase. This is vital because many potential founders are still employed (trapped in the Job Market) and need a low-friction way to dip a toe into the Co-Founder Market without fully committing. This expands the Total Addressable Market (TAM) of founders.27

4.3 Antler and On Deck: The "Arranged Marriage" Model

If YC and CoffeeSpace are dating apps, Antler and On Deck are "The Bachelor" or "Love Is Blind"—immersive, time-bound environments designed to force coupling through proximity and pressure.

• Antler's Residency: Antler invests in individuals before they have a team. They accept a cohort of 70-100 operators, place them in a physical location for 10 weeks, and facilitate "forced dating" through design sprints. This model leverages the psychological principle of propinquity (proximity increases liking). Antler also pays a stipend, which is a crucial economic innovation: it lowers the opportunity cost of leaving the job market to enter the co-founder market, effectively subsidizing the search friction.7
• On Deck (ODF): On Deck focuses on "Belief Checks" and community curation. The ODF Fellowship serves as a "finishing school" for founders, helping them transition from the "employee mindset" to the "founder mindset." By curating a high-trust community, ODF reduces the "counterparty risk" of co-founding—you trust the person because you trust the network that admitted them.30

Table 2: Comparative Architecture of Co-Founder Platforms

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5. The "Track Record" Trap: Why Resumes Fail in Co-Founding

A recurring failure mode in the co-founder market is the application of job market logic—specifically, the over-reliance on "Track Record." This is known as the "Track Record Trap."

5.1 The Manager vs. The Builder

In the corporate world, a "Senior Vice President" title signals competence in managing large budgets, navigating bureaucracy, and leading established teams. These are scaling skills. However, a startup in the "zero-to-one" phase has no budget, no bureaucracy, and no team. It requires building skills—writing code, cold-calling customers, and designing logos. A candidate with a sterling corporate track record may fail spectacularly as a co-founder because they have lost the muscle memory for individual contribution. This mismatch is often visible in the "Idea Guy" phenomenon, where a non-technical corporate executive seeks a technical co-founder to "build their vision," viewing the developer as staff rather than a partner. This dynamic destroys the peer-to-peer chemistry required for a successful dyad.11

5.2 The "Sheryl Sandberg" Anomaly

The case of Sheryl Sandberg joining Facebook is often cited as a model for co-founding, but this is a category error. Zuckerberg hired Sandberg after Facebook had achieved Product-Market Fit and was entering the scaling phase. She was given a massive equity package, but she was functionally an executive hire (COO), not a co-founder in the genesis sense. Her value was entirely her "Track Record" (Google, Treasury Dept), which was exactly what Facebook needed at that stage. Confusing this "scaling hire" with a "founding partner" leads to disastrous equity splits and role confusion in early-stage startups.32

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6. The Legal and Structural Engineering of the Dyad

The difference between the job market and the co-founder market is most starkly codified in the legal documents that govern the relationship. Employment contracts are designed to be terminated; founder agreements are designed to withstand existential stress.

6.1 The Vesting Schedule: The Startup Prenup

In the job market, compensation is earned pro-rata: you work a day, you get paid for a day. In the co-founder market, ownership is earned over time. The standard "Four-Year Vesting with a One-Year Cliff" is the central mechanism of the co-founder market.

• The Cliff: If a co-founder leaves (or is fired) within the first 12 months, they walk away with nothing. This harsh penalty serves as a powerful screening mechanism against "tourists" and ensures that only those committed to at least a year of struggle enter the partnership.
• Alignment: This structure aligns the founder's economic interest with the long-term survival of the firm, shifting the focus from "salary extraction" to "equity value creation".15

6.2 Deadlock Provisions: Russian Roulette and Texas Shootouts

In a marriage, divorce is handled by family court. In a co-founding team, a deadlock between 50/50 partners can freeze the company, leading to bankruptcy. To prevent this, co-founder agreements include "Shotgun" or "Russian Roulette" clauses—game-theoretic mechanisms designed to resolve disputes through economic force.35

• The Mechanism: Founder A offers to buy Founder B's shares at Price $X. Founder B must then make a binary choice: either sell their shares to A at Price $X, or buy A's shares at Price $X.
• The Game Theory: This forces Founder A to name a "fair" price. If they lowball (trying to steal the company), B will simply buy them out at that cheap price. If they bid too high, they risk overpaying. It is a perfect market mechanism for price discovery in an illiquid asset, utilizing greed and fear to ensure fairness. Such brutal efficiency is unimaginable in an employment contract, highlighting the "distinct flavor" of the co-founder market—it is a partnership of equals where resolution requires one party to exit entirely.

6.3 Equity Splits: The 50/50 Standard

Y Combinator and other experts strongly advocate for equal (50/50) equity splits among co-founders. The logic is that the value of the startup lies 100% in the future execution, not the past idea. An unequal split (e.g., 60/40) implies that one founder's past contribution (the "idea") is worth more than the other's future labor, which creates resentment and misalignment. In the job market, pay disparity is the norm (the CEO makes more than the intern). In the co-founder market, equity disparity is a signal of a dysfunctional partnership.4

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7. Case Studies: The Reality of Founder Mating

7.1 Success: The Algorithmic Match of Whalesync

Whalesync (YC S21) stands as a testament to the efficacy of the new market structure. Founders Curtis and Matthew met via YC Co-Founder Matching.

• The Context: Curtis was a technical founder with a previous exit to Google (High Track Record). Matthew was a product thinker. They had no social overlap.
• The Process: The algorithm surfaced them based on complementary interests in "No-Code" and "Data Syncing." They engaged in a "trial period"—effectively "dating" by building a prototype before incorporating.
• The Outcome: The "artificial" introduction led to a genuine partnership that successfully navigated YC and raised venture capital. This case proves that "stranger danger" can be mitigated by high-intent platforms and structured trial periods, validating the "market" approach to co-founding.5

7.2 Failure: The "Free Dev" Trap

A pervasive failure mode on platforms like YC Matching involves the "Idea Guy" seeking a "Technical Co-Founder."

• The Dynamic: Non-technical founders often approach the platform with a "hiring" mindset: "I have the vision, I need you to code it." They view the equity they offer as payment for a service.
• The Friction: Technical founders on these platforms are seeking ownership, not employment. When they sense they are being "interviewed" for a job rather than "courted" for a partnership, the chemistry fails. This highlights the "flavor" mismatch: the non-technical founder is operating in the Job Market (looking for resources), while the technical founder is in the Co-Founder Market (looking for a peer).31

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8. Conclusion: The Rise of the "Potential" Economy

The divergence between the job market and the co-founder market is a fundamental feature of the modern innovation economy. As we move further into an era where "talent is the scarcest resource," the mechanisms for allocating that talent must evolve. The job market, efficient for commoditized labor, is ill-suited for the high-variance, high-trust demands of venture creation.

The rise of platforms like Y Combinator Matching, CoffeeSpace, and Antler represents the maturation of the Co-Founder Market as a distinct asset class. These platforms are not merely "job boards for founders"; they are sophisticated "mating engines" that digitize the intangible signals of ambition, grit, and chemistry. By shifting the focus from "Track Record" (what you have done) to "Potential" (what we can do), they unlock human capital that would otherwise remain trapped in the friction of the labor market.

For the aspiring founder, the lesson is clear: do not bring a resume to a date. The co-founder market demands vulnerability, the willingness to work for zero salary, and the ability to project a vision of the future that is compelling enough to bind two people together for a decade. It is, in every sense, a marriage—and like any marriage, it succeeds not because of the "qualifications" of the partners, but because of the strength of the bond between them.

Key Strategic Implications:

1. For Founders: Adopt "dating" protocols—long walks, deep philosophical questions, and stress-testing projects—rather than "interview" protocols.
2. For Investors: Analyze the "Founder Dyad" as a single unit of resilience. The "fit" between founders is a more predictive metric than the sum of their individual resumes.
3. For Policy/Legal: Standardize "Founder Prenups" (vesting, shotgun clauses) to reduce the transaction costs of partnership formation and dissolution.

The co-founder market is the engine of the "Potential Economy." Understanding its distinct flavor—its risks, its psychology, and its rewards—is the first step for anyone wishing to build the future rather than merely be employed by it.

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Task 1: Reverse-Engineer YC Co-Founder Matching Attributes The YC Co-Founder Matching platform represents the gold standard for high-potential networking. Unlike standard job boards, it operates on a "double-blind" logic where interest must be mutual before identities are fully revealed.

• SMART Objective: By Week 2, produce a yc_scoring_model.json document that identifies 100% of the hidden weighting variables used in YC profiles (e.g., prestige markers, location rigidity) to inform the Agent’s internal scoring algorithm.
• Technical Execution: The agent must recognize that YC profiles prioritize "north star" metrics. Snippets indicate that profiles highlight specific prestige markers: "Harvard Law," "E7 at DoorDash," or "Y Combinator Alum". The agent must be trained to parse these non-standard credentials—identifying that "E7 at DoorDash" implies a specific level of engineering seniority equivalent to a CTO at a smaller startup.
• Sub-Task: Create a taxonomy of "Prestige Signals" extracted from 500 YC profiles to weight the agent's ranking logic.
• Sub-Task: Analyze the "blind" workflow to determine at what stage the agent can intervene. Since the platform requires an invite-accept-match cycle , the agent cannot simply "scrape contact info." It must be designed to manage the state of the connection request.

Task 2: Deconstruct CoffeeSpace’s Semantic Matching Engine CoffeeSpace differentiates itself by using a "swipe" mechanic similar to dating apps and an underlying semantic matching engine that looks beyond keywords.

• SMART Objective: Complete a comparative analysis of CoffeeSpace’s "swipe" logic vs. traditional search by Week 3, identifying three specific UX features to clone for the agent’s "Triage Mode."
• Strategic Insight: CoffeeSpace’s value proposition is "mission alignment". The Opportunity Finder must not just match "Python" with "Python"; it must match "Decentralized AI" with "Privacy-First Architecture." The agent requires a "Mission Vector" in its database schema to replicate this.
• Sub-Task: Analyze CoffeeSpace’s onboarding questionnaire to recreate their psychometric profiling (e.g., risk tolerance, work style).
• Sub-Task: Investigate the integration of Proxycurl by CoffeeSpace for automated LinkedIn enrichment. This feature is critical for reducing user onboarding friction and should be cloned.

Task 3: Analyze Starthawk’s Search and Messaging Protocol Starthawk offers a more traditional directory-based search with direct messaging capabilities.

• SMART Objective: Map the Starthawk messaging API (or DOM structure) to enable the agent to autonomously draft and queue introduction messages.
• Strategic Insight: Starthawk allows filtering by specific criteria like "has idea" vs. "no idea". This binary distinction is crucial for the agent to route opportunities correctly—a user looking to join a startup needs different matches than one looking to found one.
• Sub-Task: Define a "Readiness State" attribute in the Opportunity Schema based on Starthawk’s filters.

Task 4: Establish Privacy and "Stealth Mode" Protocols Many users of co-founder platforms are currently employed and browsing in "stealth mode." The agent acts as a proxy, but its automated behavior must not de-anonymize the principal.

• SMART Objective: Define a "Zero-Knowledge" interaction protocol by Week 3 that ensures no identifiable data is transmitted to third-party platforms during the scraping/querying phase.
• Technical Constraint: YC profiles are private to approved users. The agent must operate via an authenticated session that is strictly gated.
• Sub-Task: Implement a "Local-Only" processing rule where profile data is downloaded and analyzed locally, rather than sending candidate data to external LLM APIs without PII redaction.

1.2 Gig Economy and Bounty Platform Analysis

The gig economy presents a different challenge: high volume, low latency, and rigid categorization.
Task 5: Map TaskRabbit and Dolly Service Taxonomies TaskRabbit and Dolly (focused on delivery) utilize strict categorical hierarchies. A user offering "labor" cannot simply be listed; they must be listed under "Heavy Lifting," "Assembly," or "Moving."

• SMART Objective: Create a unified GigCategory ontology that maps 100% of TaskRabbit skills and Dolly vehicle types to a standardized internal format.
• Research Insight: Dolly requires specific vehicle attributes (Pickup, Box Truck). The agent needs to know the user's asset inventory (e.g., "Do you own a truck?") to unlock these opportunities.
• Sub-Task: Scrape the full category trees of both platforms to build a translation layer (e.g., "I have a drill" -> TaskRabbit "Mounting & Installation").

Task 6: Deconstruct Upwork’s Bidding and "Connects" Economy Upwork gamifies the proposal process with "Connects" (a virtual currency required to apply). Indiscriminate auto-applying will drain the user's budget instantly.

• SMART Objective: Develop a "Return on Connects" (RoC) scoring model that predicts the probability of a reply before the agent spends credits.
• Strategic Insight: Speed is a factor, but "proposal relevance" is higher. The agent must be capable of parsing the job description and answering specific screening questions, which are common on Upwork.
• Sub-Task: Analyze 100 successful Upwork proposals to identify common structural elements (e.g., "Restating the problem in the first sentence").

Task 7: Scout Web3 Bounty Ecosystems (Gitcoin & Immunefi) For technical users, the highest value "gigs" are often bug bounties or development grants on platforms like Gitcoin and Immunefi. These operate on radically different mechanics—often permissionless and result-based.

• SMART Objective: Integrate the Gitcoin Allo Protocol data structure into the agent’s scouting radar by Week 4.
• Technical Context: Gitcoin uses the Allo Protocol for capital allocation. The agent can query indexers or the blockchain directly to find active grant rounds, bypassing the need for UI scraping.
• Sub-Task: Map Immunefi’s severity scales (Critical, High, Medium) to dollar value estimates to normalize them against hourly freelance rates.

1.3 Job Market and Niche Platform Analysis

Task 8: Analyze "Hidden" Job Markets (Pallet, Community Boards) High-quality startup roles often appear on curated boards like Pallet or in private Slack/Discord communities before hitting Indeed.

• SMART Objective: Identify and catalog the top 50 niche Pallet boards and community servers relevant to the user's domain.
• Strategic Insight: Pallet boards are community-specific (e.g., "Bankless Jobs"). The agent needs a "Community Discovery" module to find these fragmented URLs.
• Sub-Task: Evaluate the feasibility of scraping Pallet, which uses a specific infrastructure distinct from standard ATSs.

Task 9: Assess Technical Limitations of Major Job Boards (LinkedIn, Indeed) The major platforms are hostile to automation. Official APIs are generally restricted to enterprise partners.

• SMART Objective: Determine the "Safe Operating Limits" for scraping LinkedIn and Indeed to avoid account bans.
• Research Insight: PhantomBuster suggests a limit of ~80 profiles/day for LinkedIn scraping. The agent must enforce strict rate-limiting logic.
• Sub-Task: Evaluate the Indeed Job Sync API documentation to see if "Read-Only" access is possible for personal use (unlikely, necessitating scraping).

Task 10: Define the Unified "Opportunity Schema" To allow the user to compare a $50k bounty, a $150k job, and a co-founder role with 50% equity, the data must be normalized.

• SMART Objective: Draft the JSON schema for the UniversalOpportunity object, covering 95% of fields across all target platforms.
• Schema Design:
  • type:
  • compensation_type:
  • risk_profile: [Low, Medium, High] (Derived from platform/stage)
  • remote_policy:
  • source_metadata: {...platform_specific_fields }

Task 11: User Intake Strategy (SMART Goal Conversion) Users rarely state their goals clearly. "I want a better job" is not actionable.

• SMART Objective: Design an onboarding interaction that forces the user to define constraints (e.g., "Min $120k," "Max 30 min commute").
• Sub-Task: Create a "Trade-off Slider" UI (e.g., Equity vs. Salary) to weigh the matching algorithm.

Task 12: Compliance and Legal Framework

• SMART Objective: Establish a legal compliance matrix for the agent’s operation.
• Legal Context: While scraping public data is generally protected (HiQ vs LinkedIn), scraping behind a login (like YC Matching) violates Terms of Service. The agent must be configurable to "Obey TOS" (restrictive) or "User Discretion" (permissive).
• Sub-Task: Implement robots.txt parsing as a default setting.

Task 13: Human-in-the-Loop (HITL) Architecture

• SMART Objective: Define the intervention points where the agent must pause for approval.
• Design Principle: No message is sent and no application is submitted without explicit user sign-off.
• Sub-Task: Design the notification payload for HITL requests (e.g., "Draft Application Ready. Review?").

Task 14: Select Agentic Framework (CrewAI vs. LangGraph)

• SMART Objective: Finalize the orchestration stack.
• Decision: Use CrewAI for the high-level collaboration between "Scout" and "Analyst" agents due to its role-based architecture. Use LangGraph for the specific, complex state machines required for form-filling and multi-step application processes, as it offers finer control over loops and retries.

Task 15: Infrastructure Blueprint

• SMART Objective: Produce a high-level system architecture diagram.
• Components:
  • Ingestion: Firecrawl, Apify.
  • Storage: Vector DB (Weaviate), Relational DB (Postgres).
  • Compute: Docker Containers on AWS Fargate.
  • Interface: Next.js Dashboard.