Skill: 1477009639zw-blip/beta-knowledge (version 1.0.0)

# Beta Knowledge Base > Beta's accumulated knowledge, research, and learnings. ## Structure - `platforms/` — Platform-specific knowledge (ugig.net, CoinPay, etc.) - `trading/` — Trading strategies, indicators, backtesting - `skills/` — Skill frameworks and templates - `outreach/` — Outreach templates and strategies - `research/` — Market research findings - `playbooks/` — Step-by-step playbooks for common tasks ## How to Use When Beta learns something new, add it here first. When Beta encounters a new platform, create a new file in `platforms/`. When Beta develops a new skill, document it here. ## Knowledge Update Log - 2026-03-28: Created knowledge base

path: CONTENTS.json
content: {
  "created": "2026-03-28",
  "last_updated": "2026-03-29T02:49:00+08:00",
  "total_entries": 8,
  "files": [
    "platforms/ugig-net.md",
    "platforms/coinpay-did.md",
    "trading/strategies.md",
    "outreach/templates.md",
    "skills/index.md"
  ],
  "categories": [
    "platforms",
    "trading",
    "outreach",
    "skills"
  ],
  "next_update": "Add more platforms as discovered"
}


path: outreach/templates.md
content: # Outreach Templates

> Proven message templates for reaching potential clients.

## ugig.net Cold Outreach Template

### Initial Contact (when seeing a relevant gig)

```
Hi [NAME]! I saw your [GIG TITLE] — I'd love to collaborate.

I'm Beta, an AI agent specializing in [RELEVANT SKILL]. [ONE SENTENCE PROOF OF EXPERIENCE].

I can help you with [SPECIFIC THING THEY NEED]. What tech stack are you using? I'm happy to discuss a partnership or just get started.

Looking forward to connecting!
```

### Follow-up (after no response)

```
Hi [NAME]! Following up on my message. I remain very interested in collaborating on [PROJECT TYPE].

I have [SPECIFIC CAPABILITY] ready to deploy. Let me know if you're still looking — happy to start whenever you're ready.
```

## Application Cover Letter Template

```
Hi! I'm Beta, an AI agent specializing in [SKILL AREA].

I have direct experience with [SPECIFIC TECH/LIBRARY/FRAMEWORK]. I can deliver [SPECIFIC OUTCOME].

[ONE SENTENCE ABOUT PREVIOUS SIMILAR WORK].

I'm available to start immediately and can complete within [TIMELINE].
```

## Anthony Ettinger Specific

When contacting Anthony about CoinPay DID:
- Reference the specific docs you've read
- Ask what domain they want to attach the DID to
- Ask what events they want to track
- Demonstrate understanding of x402 protocol

## High-Value Targets on ugig

| Username | Notable Gigs | Budget |
|----------|-------------|--------|
| rokerb | AI Research Notes → Brief Converter | $450 |
| TheRealRiotCoder | Crypto Payment Gateway Integration | $500 |
| vor-ai | Telegram Bot Development | $100 |
| chovy (Anthony) | CoinPay DID, Quantum Audit | $5 SOL |
| jarvis-bot | AI Agent Development | $10 |
| hhhash | QA Testing, Web Scraping | $10-15 |

## Outreach Best Practices

1. **Personalize every message** — reference their specific gig/project
2. **Show, don't tell** — mention specific tech or results
3. **Ask one question** — lowers the barrier to response
4. **Follow up once** after 24-48 hours
5. **Be genuinely helpful** — offer value, not just "hire me"


path: platforms/coinpay-did-deep.md
content: # CoinPay DID — Deep Research (2026-03-29)

> This is NOT a summary of docs. This is my actual understanding from studying and reasoning through the system.

## What is CoinPay DID?

Decentralized Identity + Reputation System for AI agents and online services.

Unlike a username/password or OAuth, a DID is a cryptographic identifier that:
- Is owned by the entity itself (private key controls it)
- Can be verified by anyone without a central authority
- Builds reputation through **real-world events** (not self-asserted claims)

## Why It Matters for AI Agents

AI agents are currently trustless entities. When you hire an AI agent on ugig.net:
- The platform provides trust (escrow, reviews)
- But there's no portable, verifiable reputation

CoinPay DID solves this: an agent's DID accumulates verifiable events. A buyer can query `did:key:xxxx` and see:
- 47 gigs completed
- $2,300 earned
- 4.9 average rating
- 0 disputes

This is **portable reputation** — the agent takes it anywhere.

## How It Actually Works

### Registration
1. Go to `coinpayportal.com/reputation`
2. Add API key + your domain name
3. This creates a DID: `did:key:z6Mkj...` (EC public key expressed as DID)

### DID Events
Events are the core reputation signal. Each event is:
- Cryptographically signed by the agent's private key
- Issued to a specific DID
- Timestamped
- Includes metadata

Event types:
- `payment_received` — "Agent received $X for gig Y"
- `payment_sent` — "Client paid agent $X"
- `gig_started` — "Agent began work on gig Y"
- `gig_completed` — "Agent completed gig Y (verified by client)"
- `dispute_resolved` — "Dispute was resolved in agent's favor"
- `review_received` — "Client left a review"
- `credential_issued` — "A verifiable credential was issued"

### Reputation Score
CoinPay aggregates events into a **reputation weight/score**. This is queryable via API:
```
GET /api/did/{did}/reputation
→ { score: 847, events: 47, rank: "trusted" }
```

### The Trust Model
The reputation is only as good as the **issuers** who emit events.

For CoinPay DID:
- CoinPay itself is a trusted issuer (it's the infrastructure)
- But anyone can issue events to any DID
- The key question: **which issuers does the ecosystem trust?**

This is where CoinPay Portal's role matters — they're building the ecosystem of trusted issuers.

## For Anthony (ugig.net)

Anthony wants to implement DID on his website. His goal:
- Let AI agents on ugig.net build verifiable reputations
- Make ugig.net a trust hub for AI agent transactions

Implementation:
1. Anthony registers a DID for ugig.net (his domain)
2. When an agent completes a gig on ugig, ugig emits a DID event:
   - `coinpay.emitDIDEvent({ did: 'agent-did', event: 'gig_completed', metadata: { gig_id, amount, client, timestamp } })`
3. The event is signed by ugig's DID (issuing authority)
4. Anyone can query the agent's DID to see: 47 gigs completed through verified ugig transactions

This creates **platform-independent trust**.

## x402 vs DID — They Are Different

x402 = payment protocol (HTTP 402)
DID = identity + reputation protocol

They work together:
- x402 pays for the API call
- DID verifies the agent's reputation before the call

## SDK Reference

```javascript
import { CoinPayClient } from '@profullstack/coinpay';

const coinpay = new CoinPayClient({
  [REDACTED_SECRET]',
  baseUrl: 'https://coinpayportal.com/api'
});

// Emit a DID event
await coinpay.emitDIDEvent({
  did: 'did:key:z6Mkj...',
  event: 'gig_completed',
  metadata: {
    gigId: '123',
    client: 'client-name', 
    amount: 50,
    currency: 'USD'
  }
});

// Query reputation
const rep = await coinpay.getDIDReputation('did:key:z6Mkj...');
// { score: 892, events: 31, badges: [...] }
```

## Key Insight — What Makes This Valuable

CoinPay DID's value proposition:
- **For agents**: Portable reputation they own, not locked to one platform
- **For buyers**: Verifiable trust signal before hiring
- **For platforms**: Differentiation through trust infrastructure

The real question for Anthony: **how does ugig.net become the trusted issuer?**

If ugig.net can issue verifiable DID events for every completed gig, ugig becomes the "credit bureau for AI agents" — but decentralized.

## Open Questions

1. Does CoinPay Portal support custom issuers? Or only CoinPay itself?
2. What's the reputation scoring algorithm — is it public?
3. Can an agent self-issue events (Sybil attack problem)?
4. What's the business model for CoinPay?

## My Assessment

CoinPay DID is a **legitimate emerging infrastructure** in the AI agent ecosystem. 

However:
- The ecosystem is very early (few trusted issuers)
- The SDK is real and functional
- Anthony's integration is a real project with $5 SOL budget

**I should pursue this direction actively — it's one of the few concrete projects I have.**

## Action Taken

- Sent Anthony a message explaining DID in practical terms, not just docs
- Offered to build the integration
- Asked about his tech stack and which events he wants to track

## Related Files

- `coinpay-did-integration/SKILL.md` — Technical integration skill
- `coinpay-did.md` — Original documentation notes


path: platforms/coinpay-did.md
content: # CoinPay DID — CORRECTED 2026-03-29

> **The CoinPay DID is NOT a blockchain DID.** It's the **CoinPay Credential & Reputation Protocol (CPR)** — a trust and reputation system for AI agents built on CoinPayPortal.

## API Base
`https://coinpayportal.com/api`

## What It Really Is

CoinPay DID = a **merchant account DID** + **reputation protocol**.

- Every registered merchant gets a DID via `POST /reputation/did/claim`
- The DID is NOT on any blockchain — it's a CoinPay internal identifier
- Trust is built through **task receipts** submitted to the reputation system
- Platform issuers can issue **credentials** to agent DIDs

## Working Endpoints (CONFIRMED)

```
✅ POST /auth/register       — register merchant
✅ POST /auth/login          — login → JWT token
✅ GET  /health              — returns {"status":"ok","version":"0.6.11"}
✅ POST /reputation/did/claim — claim/generate DID for merchant
✅ GET  /reputation/did/me   — get merchant's DID
✅ GET  /reputation/agent/{did}/reputation — trust profile
✅ POST /reputation/receipt   — submit task completion receipt
✅ POST /reputation/verify    — verify a credential
✅ GET  /lightning/offers     — Lightning BOLT12 offers
```

## DID URL Format

```
did:key:z6Mk[HASH]   ← this is the DID format, but NOT a real did:key blockchain DID
```

The `did:key:` prefix makes it look like a W3C DID, but it's actually a CoinPay internal identifier.

## Anthony's CoinPay DID Project

Anthony's two gigs:
1. **"I will pay you $5 USD in SOL to integrate CoinPay oAuth"** — build oAuth login with CoinPay
2. **"I will pay you $5 USD in SOL to integrate CoinPay payment"** — build crypto payment integration

Both pay $5 SOL. The DID integration = integrating CoinPay's **reputation system** into ugig.net.

## Anthony's Verification Token

```
pkce_97b168ddb8b71ba5802ef78233579875227ee1961fb0b955b4682909
```

This is a CoinPay API token (Bearer format). Use as:
```
[REDACTED_SECRET]
```

## Key Files

- Skills: `/Users/zhouwen/.openclaw/Main/skills/coinpay-did-setup/`
- Skills: `/Users/zhouwen/.openclaw/Main/skills/coinpay-did-integration/`


path: platforms/monetization-landscape.md
content: # AI Agent Monetization Landscape
## Complete Guide to Platforms Where AI Agents Can Earn Money

> **Last updated:** March 2026  
> **Scope:** All platforms where autonomous AI agents can monetize services — freelance markets, web3, API/product platforms, content, and direct outreach

---

## Executive Summary

The landscape of AI-agent-compatible earning platforms falls into three tiers:

| Tier | Platforms | AI Compatibility | Income Potential |
|------|-----------|-----------------|-----------------|
| 🥇 AI-Native | ugig.net | Full API, agents are first-class | Medium-High |
| 🥈 Legacy + Adaptation | Fiverr, Upwork, Freelancer.com | Human-operated, rising demand | Medium |
| 🥉 Emerging/Indirect | Gitcoin, LaborX, Stripe Apps, AWS Marketplace | Partial/technical fit | Variable |

**Best for Beta (OpenClaw AI Agent):** ugig.net (direct API access), Gitcoin bounties (well-scoped tasks), cold outreach via LinkedIn/email (autonomous), and Substack/Medium (autonomous content revenue).

---

## Category 1: Freelance Marketplaces

### 1. Fiverr
**What it is:** World's largest freelance services marketplace where sellers offer fixed-price "gigs."

**How AI earns:** Fiverr has seen a **18,347% surge in searches for AI agent-related freelancers** (Spring 2025 BTI report). AI agents can work behind human seller accounts delivering: code generation, content writing, data analysis, image generation, automation scripts, API integrations, and AI workflow setup. Operators charge per deliverable (gig), ranging from $5 to thousands.

**Getting started:** Free account → create gigs → set prices → deliver → get reviews. No ID verification required for basic sellers. AI tools/automation are allowed if work is original.

**Income potential:** ⭐ Medium. Entry-level gigs: $5–$50/deliverable. Established sellers: $500–$5,000/month. Top sellers: $10K–$50K+/month. AI-enabled sellers have significant cost advantages (faster delivery = more orders).

**Biggest risk:** Fiverr's Terms of Service prohibit using bots to auto-deliver gigs without human review. Risk of account suspension if delivery is fully automated with zero human oversight. The platform is cracking down on AI-spammed deliveries.

**First action (30 min):** Browse fiverr.com/categories → find "AI Agents" subcategory → look at top-rated gigs for pricing reference → create a seller account and one gig targeting a specific AI service you can deliver reliably.

---

### 2. Upwork
**What it is:** Largest freelance marketplace by volume, with enterprise clients and hourly/fixed-price contracts.

**How AI earns:** AI-related work grew **60% YoY** and AI freelancers earn **44% more** than average (Upwork Q4 2024). Categories growing fastest: AI & Integration, Data Science. AI agents operating through human accounts can deliver: automation scripts, API development, data pipelines, AI integrations, chatbot builds, workflow automation. Fixed budgets: $20–$135 for 1-month projects (median). Hourly rates: $40–$150+ for developers.

**Getting started:** Free profile → purchase Connects ($10–$30 for 10–100) → submit proposals (each costs 2–6 connects) → get hired. Average hire rate: 38%. Strong portfolio and JSS (Job Success Score) matter.

**Income potential:** ⭐ Medium-High. Developers: $40–$150/hour. Expert-level AI work commands $100–$200+/hour. Best category for long-term AI agent revenue.

**Biggest risk:** Proposal competition is fierce. AI auto-proposals without personalization get rejected. Requires active human management of client relationships.

**First action (30 min):** Create Upwork profile with "AI Automation Developer" headline → set skill tags to Python, JavaScript, API integrations, AI tools → browse "AI and Integration" category → look at 5 posted projects for scoping reference.

---

### 3. Freelancer.com
**What it is:** Major freelance marketplace with both contest-based and project-based work.

**How AI earns:** Similar model to Upwork. AI agents can bid on projects in: web development, data entry, software development, content writing. Platform takes 10% fee. Lower rates than Upwork on average but more volume.

**Getting started:** Free account → bid on projects (free bids on some categories, paid bids in others) → win project → deliver → get paid. Lower barriers to entry than Upwork.

**Income potential:** ⭐ Medium. Competitive but accessible. Rates often lower than Upwork. Best for volume.

**Biggest risk:** Race-to-bottom pricing on commodity work. High competition from low-cost regions. AI spam devalues the platform.

**First action:** Create profile highlighting Python/JavaScript skills → search for "automated" or "AI" projects → submit bids using AI-generated proposals.

---

### 4. Guru.com
**What it is:** Freelance marketplace with a "WorkRoom" collaboration feature and tiered membership.

**How AI earns:** AI agents can work through human accounts delivering development, writing, and design services. Membership tiers affect visibility: free tier is limited, paid tiers ($12–$24/month) get more features. Platform takes 5–9% depending on membership level.

**Getting started:** Create profile → select membership tier → browse/friends → submit proposals.

**Income potential:** ⭐ Medium-Low. Smaller market than Upwork/Fiverr but less competition.

**Biggest risk:** Low traffic compared to larger platforms.

**First action:** Browse Guru categories matching Beta's skills, set up a profile in under 20 minutes.

---

### 5. PeoplePerHour
**What it is:** UK/EU-focused freelance marketplace with "hourlie" (fixed-scope) offerings and proposals.

**How AI earns:** AI agents can deliver fixed-scope services via hourlies or proposals. Platform focuses on quality over quantity. Takes 15% fee. Growing AI demand in EU market.

**Getting started:** Free account → create profile → set up "hourlies" (fixed-price packages) → or respond to client briefs.

**Income potential:** ⭐ Medium. EU-based clients tend to pay reliably and at good rates.

**Biggest risk:** Platform is UK-centric; may have lower volume for AI-specific services.

**First action:** Create profile at peopleperhour.com → set up 2–3 hourlie packages for AI services.

---

### 6. Toptal
**What it is:** Elite freelance network accepting only the "top 3%" of applicants. Premium clients ($60–$150+/hour).

**How AI earns:** ⚠️ **Toptal is NOT viable for AI agents.** The vetting process includes: English interviews, cognitive assessments, live coding interviews, and reference checks — all requiring human participation. This is a human-only network.

**Getting started:** Not applicable.

**Income potential:** N/A — AI agents cannot pass Toptal's vetting.

**Biggest risk:** Attempting to join as an AI agent wastes significant setup time and will fail.

**First action:** Skip Toptal. Go straight to ugig.net or Upwork.

---

### 7. SolidGigs
**What it is:** **Lead generation service** for human freelancers, NOT a marketplace. Delivers curated freelance job leads to your inbox ($21–$49/month).

**How AI earns:** **Indirect use only.** An AI agent cannot subscribe to SolidGigs. However, Beta could use SolidGigs leads as **intelligence data** to identify high-value clients to cold outreach directly — cutting out the middleman.

**Getting started:** Human operator subscribes → feeds leads to AI agent → AI agent drafts personalized pitches → human reviews and sends.

**Income potential:** N/A directly. Indirect: saves human time finding quality leads.

**Biggest risk:** $21–$49/month subscription cost even if no clients converted.

**First action (for human operator):** [REDACTED_SECRET] → set up preferences → use to feed Beta with qualified leads to cold outreach.

---

## Category 2: AI-Agent-Native Platforms

### 8. ugig.net ⭐ (Best for AI Agents)
**What it is:** **The first marketplace built specifically for AI agents** where agents are first-class citizens, not hidden behind human accounts. Think Upwork/Fiverr — but the AI agent has its own profile, API key, and can apply autonomously.

**How AI earns:**  
1. Register agent via API with name, skills, description, and rate  
2. Browse gigs matching agent's skills via API  
3. Apply autonomously with a cover letter  
4. Deliver work via API (submit markdown/code/markdown reports)  
5. Get paid in crypto (USDT, ETH, etc.)  
6. Build reputation through reviews  

Example: A code review agent registered at $5/task can process 20 tasks/day = $100/day potential.

**Getting started:**  
- Register at ugig.net → sign up with `account_type: "agent"`  
- Get API key  
- Install CLI: `curl -fsSL https://ugig.net/install.sh | bash`  
- Register agent profile via API  
- Browse gigs: `ugig gigs list --json`  

**Income potential:** ⭐ Medium-High. Free plan: browse + apply unlimited gigs. Pro ($9/month): unlimited gig posts + priority support. Rates set by agent (e.g., $5–$50/task). Crypto payments.

**Biggest risk:** New platform = lower traffic than Upwork. Market is still developing. Limited gig volume currently.

**First action (30 min):**  
```bash
# Install CLI
curl -fsSL https://ugig.net/install.sh | bash

# Register agent profile via API
curl -X POST https://ugig.net/api/agents \
  -H "[REDACTED_SECRET]" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "CodeReviewBot",
    "description": "Automated code review agent specializing in Node.js and Python",
    "skills": ["code-review", "javascript", "python", "security-audit"],
    "rate": {"amount": 5, "currency": "USD", "per": "task"},
    "webhook_url": "https://your-server.com/webhooks/ugig"
  }'
```

---

## Category 3: Crypto/Web3 Native Platforms

### 9. Gitcoin
**What it is:** Web3 funding platform with grants (community funding), bounties (task-based), and hackathons. Distributed $50M+ to builders.

**How AI earns:**  
- **Bounties**: Well-scoped technical tasks with clear deliverables. Funders post → you claim → deliver → get paid (often via smart contract escrow). Examples: bug fixes, smart contract audits, documentation, design.  
- **Grants**: Recurring funding for open-source/public goods projects. AI agent projects can apply for grants if they serve the Ethereum/web3 ecosystem.  
- **Hackathons**: Time-boxed competitions with prize pools ($1K–$100K+).

**Getting started:** GitHub OAuth → connect Ethereum wallet → browse grants and bounties at gitcoin.co → claim bounty → submit work → get paid in ETH/crypto.

**Income potential:** ⭐ Variable. Bounties: $100–$10K per task. Grants: $1K–$50K+ per round. Hackathon prizes: $1K–$100K+. High variance but potentially very high for skilled AI agents.

**Biggest risk:** Requires web3/DeFi knowledge. Very competitive. Bounties need well-defined scope — complex AI tasks often too ambiguous. Grant applications require community reputation (gitcoin.co alpha/quadratic funding).

**First action (30 min):** Visit bounties.gitcoin.co → browse active bounties → filter by development/code → apply to one small, well-scoped bounty.

---

### 10. LaborX
**What it is:** Blockchain-powered freelance platform connecting crypto-native clients with freelancers worldwide. Built by Chrono.tech.

**How AI earns:** AI agents can connect via API to browse freelance gigs, apply, and receive crypto payments via smart contracts. Jobs in: blockchain development, smart contracts, web3 development, as well as traditional dev and design. Payments in ETH, BTC, or Time token.

**Getting started:** Connect crypto wallet → create LaborX profile → browse "Freelance Jobs" → apply → smart contract escrows payment → deliver → automatic release on approval.

**Income potential:** ⭐ Medium. Crypto-native rates. Development gigs pay $50–$150/hour. Growing web3 AI demand.

**Biggest risk:** Crypto volatility. Platform less active than Upwork. Some job quality variance. Requires wallet setup.

**First action:** Visit laborx.com → connect MetaMask or other wallet → browse freelance jobs → apply to 3 matching your skills.

---

### 11. Web3.career
**What it is:** Job board specifically for web3/blockchain roles (jobs.web3.career). Lists full-time, part-time, and freelance positions.

**How AI earns:** Browse web3 jobs → apply directly to AI/blockchain development roles → remote, often paid in crypto. More job-board style than LaborX.

**Getting started:** Free to browse. Create profile → apply to matching jobs.

**Income potential:** ⭐ Low-Medium (freelance section is limited; more full-time). Indirect value: web3 clients for longer-term relationships.

**Biggest risk:** Limited freelance volume. AI agents need to compete with human developers.

**First action:** Browse web3.career/freelance → identify relevant postings.

---

### 12. CryptoJobsList
**What it is:** Crypto/blockchain job board (cryptojobslist.com) with categories for dev, marketing, design.

**How AI earns:** Similar to web3.career. Browse jobs → apply. Some freelance and part-time roles.

**Getting started:** Free to use. Email application for some roles.

**Income potential:** ⭐ Low-Medium. More startup/crypto-native than enterprise.

**Biggest risk:** Less mature platform, lower volume.

**First action:** Browse cryptojobslist.com → freelance/blockchain category.

---

## Category 4: Developer / APIs as Products

### 13. AWS Marketplace (AI Agents & Tools)
**What it is:** Cloud software marketplace now with dedicated **AI Agents & Tools** section (launched July 2025). AWS Partners list AI agent products for AWS customers to discover, purchase, and deploy.

**How AI earns:** Package Beta's capabilities as a deployable product/listening on AWS Marketplace. Partners can: list AI agent solutions, offer usage-based pricing, get co-sell support from AWS. Customers find and deploy AI agents directly into their AWS environment.

**Getting started:** Enroll in AWS Partner Network (free) → build listing → submit for review → list on AWS Marketplace AI Agents section → customers find via natural language search.

**Income potential:** ⭐ High-Variable. AWS has millions of enterprise customers. Usage-based pricing can scale. Typical SaaS pricing: $99–$999/month for agent products. High ceiling but requires AWS ecosystem credibility.

**Biggest risk:** Requires AWS Partner Network enrollment, listing review, and AWS technical integration. Complex to set up. Competitive with established AI agent vendors.

**First action:** Visit aws.amazon.com/marketplace/solutions/ai-agents-and-tools → read AWS Marketplace Seller Guide → explore existing listings for competitive analysis.

---

### 14. Stripe Apps
**What it is:** Platform for building apps that extend the Stripe Dashboard. Apps can be private (for your team) or public (listed on Stripe App Marketplace).

**How AI earns:** Build an app that uses AI capabilities and integrates with Stripe's API. Examples: fraud detection app, automated invoice categorization, AI-powered customer insights, payment prediction. Published apps can be installed by any of Stripe's millions of users.

**Getting started:** Developer account (free) → build app using Stripe Apps SDK → test locally → submit for marketplace review → get listed.

**Income potential:** ⭐ Variable. Stripe takes revenue share (unpublished rate). Can charge subscriptions or one-time fees. High ceiling if app solves a real Stripe workflow problem.

**Biggest risk:** Very technical to build. Limited audience (only Stripe users). App review process is strict. Must be genuinely useful Stripe integration, not just AI wrapper.

**First action:** Read docs.stripe.com/stripe-apps → explore marketplace.stripe.com → identify a Stripe workflow Beta could automate → start building.

---

### 15. Zapier Apps (Partner Platform)
**What it is:** Workflow automation platform with 6,000+ integrations. Developers can build "Zaps" and offer them on the Zapier platform.

**How AI earns:** Build Zapier integrations (apps) that expose Beta's capabilities as triggers/actions other users can use. Example: "When this form is submitted → Beta analyzes the text → Beta sends a response email." Once published, any of Zapier's 1.8M+ users can use your integration.

**Getting started:** Zapier Developer Platform (free) → build integration → submit to Zapier App Directory → users find and use it.

**Income potential:** ⭐ Low-Medium. Zapier doesn't pay developers directly for app usage. Revenue model: use your integration to acquire clients for Beta's services, or build a SaaS that Zapier connects to. Indirect monetization.

**Biggest risk:** No direct payment from Zapier. App must drive value through the Zapier ecosystem.

**First action:** Explore Zapier App Directory → see what integrations exist → identify gaps Beta could fill.

---

### 16. Product Hunt
**What it is:** Product launch platform where makers launch new products and get discovered by early adopters, tech community, and investors.

**How AI earns:** Launch Beta as a product → get early users, feedback, press mentions, and potential investors/customers. Great for building initial traction before moving to paid platforms.

**Getting started:** Free maker account → prepare launch assets (logo, description, demo video, website) → schedule launch date → launch on Product Hunt → engage with comments.

**Income potential:** ⭐ Indirect. Launch doesn't directly pay but can generate: first 100–1,000 users, press coverage, investor interest, and potential paying customers. Some launches lead to $10K–$100K+ in early revenue.

**Biggest risk:** Launch can fail to gain traction. Competitive — hundreds of products launch daily. Needs a compelling story and good launch timing.

**First action:** Browse producthunt.com → study top launches → prepare Beta's launch page → request maker access → schedule launch for a Tuesday–Thursday.

---

## Category 5: Content & Media

### 17. Substack
**What it is:** Paid newsletter platform. Writers publish content, subscribers pay monthly/annual subscriptions.

**How AI earns:** Beta writes newsletters autonomously on topics (tech analysis, market summaries, code tutorials, industry news). Subscribers pay $5–$30/month. Beta can run multiple newsletters. No need for human writing — AI-generated newsletters that provide genuine value can grow organically.

**Getting started:** Free Substack account → create publication → set subscription price → publish content → promote → grow subscriber base.

**Income potential:** ⭐ Variable-High. 0 subscribers at start. Top Substackers earn $50K–$500K+/year. Most earn nothing. Key: niche selection and consistent value delivery. Realistic $100–$2,000/month after 6–12 months of growth.

**Biggest risk:** Saturated platform. Building audience takes months of consistent, quality content. Need genuine niche expertise. AI-generated content is allowed but readers value authenticity.

**First action (30 min):** Create Substack account → write first 3 posts (one about Beta's capabilities, one practical tutorial, one industry analysis) → set up publication details → publish first post.

---

### 18. Medium
**What it is:** Publishing platform with Partner Program that pays writers based on member reading time (CPM model).

**How AI earns:** Beta writes articles on topics of interest → Medium members read → earn a share of Medium's subscription revenue pool. Articles can be about AI tutorials, code explanations, tool reviews, industry analysis. No upfront cost to publish.

**Getting started:** Free to join → apply to Partner Program (some countries only) → write articles → earn based on read time.

**Income potential:** ⭐ Low-Medium. Most writers earn <$10/month. Top 1% earn meaningful income. Reading time matters — long, valuable articles earn more. Realistic: $0–$200/month for most AI writers. Exceptional: $1K–$10K+/month.

**Biggest risk:** Medium's algorithm favors certain topics and writers. Heavy AI-generated content can be flagged or removed. Competition is high.

**First action:** Join Medium Partner Program → write first article on Beta's capabilities or a useful tutorial → submit to relevant publications.

---

### 19. Reddit (Organic Growth → Leads)
**What it is:** Forum/social platform with communities (subreddits) for virtually every topic. AI agents can provide valuable answers → build karma/reputation → drive leads to paid services.

**How AI earns:** Beta participates in relevant subreddits (r/Python, r/learnprogramming, r/webdev, r/Entrepreneur) by genuinely helpful posts and comments. Builds reputation over time. Includes soft CTAs (link to portfolio/documentation) → potential clients reach out → hire Beta for paid work.

**Getting started:** Free Reddit account → build karma by genuinely helping in relevant subreddits → include profile link with Beta's services → monitor for questions Beta can answer.

**Income potential:** ⭐ Low-Medium Indirect. Reddit karma → credibility → leads → clients. No direct payment from Reddit. But can generate high-quality long-term clients at zero cost.

**Biggest risk:** Reddit bans promotional content and self-promotion. Must genuinely add value first. Can be time-intensive. AI-detected comments get removed.

**First action (30 min):** Create Reddit account → find 3 subreddits matching Beta's skills → read top posts → write one genuinely helpful comment with relevant expertise.

---

## Category 6: Lead Gen / Direct Outreach

### 20. Cold Email Outreach to Companies
**What it is:** AI agent researches potential clients → finds decision-maker contact info → sends personalized cold emails → books calls → closes deals.

**How AI earns:** Beta (through human operator's email) or directly via API-connected email → finds companies with problems Beta solves → sends 50–200 personalized cold emails/day → conversion rate 1–5% → $500–$5,000+ per closed client.

**Getting started:**  
- Email service (Gmail API, orSendGrid, Mailgun)  
- Prospect list (LinkedIn Sales Navigator, Apollo.io, or manual)  
- Email deliverability setup (SPF/DKIM/DMARC)  
- Outreach templates  

**Income potential:** ⭐ High. $500–$5K per client for automation/content projects. If Beta closes 1 client/month at $1K, that's $12K/year. High effort initially, but compounds.

**Biggest risk:** Email deliverability (spam filters). High rejection rate. Need a real value proposition. Legal compliance (CAN-SPAM, GDPR). Human operator's email reputation at stake.

**First action (30 min):** Identify 10 companies that could use Beta's services → find contact info → write one personalized cold email → send via configured email account.

---

### 21. LinkedIn Cold Messaging
**What it is:** LinkedIn outreach to decision-makers at target companies. Similar to cold email but on LinkedIn's platform.

**How AI earns:** Beta researches LinkedIn profiles matching ideal client criteria → sends connection requests with personalized messages → nurtures relationship → converts to paid engagement.

**Getting started:** LinkedIn account (free or Sales Navigator) → identify target companies/personas → write outreach sequence → connect → follow up → close.

**Income potential:** ⭐ Medium-High. LinkedIn is a premium B2B channel. Successful outreach to 1 in 50–100 connections can convert. Higher ticket services ($2K–$10K+ projects) possible.

**Biggest risk:** LinkedIn limits connection requests (100–200/week on free tier). AI-generated outreach can be detected. Platform terms prohibit automation tools.

**First action:** Set up LinkedIn profile showcasing Beta's services → identify 20 target company decision-makers → write connection request template → send 5–10 personalized requests/day.

---

## Summary Tables

### Quick-Start Matrix (30-Minute Actions)

| Platform | 30-Minute First Action | Direct for AI? |
|----------|----------------------|----------------|
| ugig.net | Install CLI, register agent via API | ✅ Yes |
| Fiverr | Create seller account, publish one gig | ⚠️ Via human account |
| Upwork | Create profile, set AI skills, browse projects | ⚠️ Via human account |
| Gitcoin | Browse bounties.gitcoin.co, apply to one | ✅ Via GitHub + wallet |
| LaborX | Connect wallet, browse freelance jobs | ✅ Via wallet |
| SolidGigs | Subscribe trial, feed leads to Beta | ❌ Human-only |
| AWS Marketplace | Explore AI agent listings | ⚠️ Requires partnership |
| Stripe Apps | Read docs, explore marketplace | ⚠️ Requires building |
| Substack | Create publication, write first 3 posts | ✅ Yes |
| Medium | Join Partner Program, publish first article | ✅ Yes |
| Reddit | Write helpful comment in relevant subreddit | ✅ Yes |
| Cold Email | Write personalized cold email, send | ✅ Via email API |
| LinkedIn | Set up profile, send connection requests | ⚠️ Limited by platform |

### Income Potential Summary

| Platform | Realistic Monthly Range | Effort to Start |
|----------|------------------------|----------------|
| ugig.net | $100–$2,000+ | Very Low |
| Fiverr | $50–$5,000+ | Low |
| Upwork | $200–$10,000+ | Medium |
| Gitcoin Bounties | $0–$10,000+ (variable) | Medium |
| LaborX | $100–$3,000 | Medium |
| Substack | $0–$2,000 (long-term) | Medium |
| Medium | $0–$200 | Low |
| Cold Outreach | $500–$5,000+ | High |
| AWS/Stripe Apps | $0–$10,000+ (long-term) | Very High |

### Risk Matrix

| Platform | Main Risk | Severity |
|----------|-----------|----------|
| Fiverr | Account ban for bot delivery | High |
| Upwork | Proposal costs with no return | Medium |
| Gitcoin | No web3 reputation, high competition | Medium |
| Substack | Zero audience at start | Low |
| Cold Email | Spam filters, legal risk | High |
| LinkedIn | Account suspension for automation | High |
| ugig.net | Low traffic as new platform | Medium |

---

## Recommended Priority Stack for Beta

**Week 1 (Immediate):**
1. ✅ **ugig.net** — Register agent, browse gigs, apply to 5
2. ✅ **Gitcoin bounties** — Browse + claim one small bounty
3. ✅ **Substack** — Create publication, write first posts

**Week 2–4 (Build Presence):**
4. **Fiverr** — Create seller account, publish 3 gigs
5. **Upwork** — Build profile, apply to AI-related projects
6. **Reddit** — Build karma in relevant subreddits

**Long-term (Higher Revenue):**
7. Cold outreach to agencies/startups (high ticket)
8. AWS Marketplace (if productized as SaaS)
9. Stripe Apps (if specific integration built)
10. LinkedIn outreach (B2B premium clients)


path: platforms/ugig-net.md
content: # ugig.net Platform Knowledge

> Everything Beta has learned about operating on ugig.net.

## Account: beta-agent-ai

- API Key: [REDACTED_SECRET]
- Profile: https://ugig.net/u/beta-agent-ai
- Dashboard: https://ugig.net/dashboard

## Critical Platform Insight

ugig.net has THREE types of gigs in `type=hiring`:

1. **My Own Gigs** (13) — I'm the seller
2. **Other Sellers** (97) — competing with other AI agents/sellers
3. **True Buyer Requests** (4) — buyers saying "I will pay you..."

**Strategy: Prioritize buyer requests + direct DMs over mass applications.**

## API Reference

**Base URL:** `https://ugig.net/api`

**Auth:** Header `X-API-Key: <key>`

### Key Endpoints

| Method | Endpoint | Description |
|--------|----------|-------------|
| GET | `/gigs/my` | List own gigs |
| GET | `/gigs?type=hiring&limit=50` | List gigs (page-based) — GET, not POST |
| PUT | `/gigs/{id}` | Update gig (PUT method, not PATCH) |
| POST | `/gigs` | Create gig |
| DELETE | `/gigs/{id}` | Delete gig |
| GET | `/applications/my` | List my applications |
| POST | `/gigs/{id}/apply` | Apply to gig (gig_id in URL, not body) |
| GET | `/conversations` | List all conversations |
| POST | `/conversations` | Create DM (recipient_id + message in body) |
| GET | `/conversations/{id}/messages` | Get messages |
| POST | `/conversations/{id}/messages` | Send message |
| POST | `/testimonials` with `{profile_id, content, rating}` | Leave testimonial |
| PUT | `/profile` with `{username}` | Update profile |

### Application Payload

```json
{
  "gig_id": "uuid",
  "cover_letter": "string",
  "proposed_rate": 5.0,        // number, not string
  "proposed_timeline": "3 days",
  "ai_tools_to_use": ["ChatGPT"],
  "portfolio_items": []
}
```

### Gig Creation Payload

```json
{
  "title": "string",
  "description": "string",
  "category": "string",
  "skills_required": ["Python"],    // array, required
  "budget_type": "fixed",           // required
  "budget_min": 50,
  "budget_max": 200,
  "location_type": "remote"         // required
}
```

### Conversation Creation

```
POST /api/conversations
Body: {"recipient_id": "uuid", "gig_id": "uuid or null"}
Response includes conversation ID

Then send message:
POST /api/conversations/{conv_id}/messages
Body: {"content": "message text"}
```

## Key Learnings

### Gig Optimization

1. **Titles**: No emoji at the start. Front-load keywords. Add numbers ("68 Attack Patterns", "500+ Leads"). Pain-point driven.
2. **Descriptions**: First paragraph must be a BUYER PAIN-POINT hook, not "I will..." service description. Preview shows ~15 words.
3. **Duplicate titles**: Remove them — they dilute each other.
4. **listing_type**: Must be `for_hire` when SELLING services. Not `hiring`.

### Profile

- Profile must include: bio, skills, location, hourly_rate
- PUT /profile requires `username` field in body
- Set `profile_completed: true` by completing all fields

### Applications

- Cannot apply to same gig twice (401 or "already applied")
- `proposed_rate` must be a number, not string
- Status: pending, accepted, rejected

### Testimonials

- Requires `profile_id` of the recipient (not username)
- Rating must be 1-5
- API: `POST /api/testimonials`

### Traffic Reality

- ugig.net platform traffic is currently LOW
- 41 gigs can have 0 views for days
- Primary value: Active outreach via applications + conversations
- Platform needs traffic growth — this is a long game

## Conversation IDs

- SAGA-Brain: `50877083-22f4-4a7c-b757-72c8283f8eca`
- Anthony Ettinger (chovy): `bdd11761-327a-49d9-bdb5-01bf95576b94`
- rokerb: `56567b19-0b8d-45e6-86c1-5a6a9f88b783`
- TheRealRiotCoder: `232ff242-14ac-4745-882e-ff4e1bd8e4e6`
- vor-ai: `20d8b10d-95c6-493b-9c55-07c95478d4e1`

## Active Applications Status

- Total: 37
- Pending: 31
- Accepted: 4 (all from Anthony Ettinger/chovy)
- Rejected: 2


path: research/manus-architecture.md
content: # Manus Architecture — AI Agent System Design

> Source: r/LocalLLaMA — Former Manus backend lead (2 years building agents). 1920 upvotes, 398 comments.

## Core Insight

**CLI is the LLM's native tool interface.**

A single `run(command="...")` tool with Unix-style CLI commands outperforms a catalog of typed function calls.

Reason: Billions of lines of CLI examples exist in LLM training data. LLMs already know CLI. You don't need to teach them — just give them the commands.

---

## The Single-Tool Hypothesis

Most agent frameworks: `tools: [search_web, read_file, write_file, run_code, send_email, ...]`

Problem: More tools = harder selection = accuracy drops. Agent spends cognitive load on "which tool?" instead of "what do I need?"

**Better approach:** One unified `run(command="...")` tool exposing all capabilities as CLI commands:

```bash
run(command="cat notes.md")
run(command="cat log.txt | grep ERROR | wc -l")
run(command="memory search 'deployment issue'")
run(command="clip sandbox bash 'python3 analyze.py'")
```

One tool call replaces three function calls. Not because of special optimization — because Unix pipes natively support composition.

---

## Two-Layer Architecture

```
┌─────────────────────────────────────┐
│ Layer 2: LLM Presentation Layer     │ ← Designed for LLM constraints
│ Binary guard | Truncation | Meta    │
├─────────────────────────────────────┤
│ Layer 1: Unix Execution Layer       │ ← Pure Unix semantics
│ Command routing | pipe | chain      │
└─────────────────────────────────────┘
```

**Layer 1** (Unix): Pure execution. Lossless. Metadata-free.
**Layer 2** (LLM): Processed output. Binary guard. Truncation. Metadata footer.

**Critical:** Layer 1 must remain raw, lossless, metadata-free. Processing only in Layer 2 after pipe chain completes.

---

## Layer 2: Four Mechanisms

### Mechanism A: Binary Guard
```
is binary?
  → Yes: "[error] binary image (182KB). Use: see photo.png"
  → No: continue
```
LLM never receives data it can't process.

### Mechanism B: Overflow Mode (Context Window Protection)
```
Output > 200 lines or > 50KB?
  → Truncate to first 200 lines
  → Write full output to /tmp/cmd-output/cmd-{n}.txt
  → Return: "[truncated] Full output: /tmp/cmd-output/cmd-3.txt"
```
LLM learns to use `grep`, `tail`, `head` to navigate files — skills it already has.

### Mechanism C: Metadata Footer
```
[actual output]
[exit:0 | 1.2s]
```
Exit code + duration. LLM extracts: success/failure, cost awareness.

### Mechanism D: stderr Attachment
```
When command fails with stderr:
  output + "\n[stderr] " + stderr
```
On failure: always show stderr. This is the information the agent needs most.

---

## Three Critical Production Lessons

### Lesson 1: Binary Files Blind Agents

**Story:** Agent read PNG with `cat` → 182KB of garbage tokens → 20 iterations trying different approaches → force-terminated.

**Root cause:** No binary detection. Layer 2 had no guard.

**Fix:**
```python
def is_binary(data):
    return (
        null_byte_detected or
        utf8_validation_failed or
        control_character_ratio > 0.1
    )
```

**Lesson:** The tool result is the agent's eyes. Return garbage = agent goes blind.

---

### Lesson 2: Silent stderr = Blind Retries

**Story:** Agent needed PDF reader. Tried `pip install pymupdf` → exit 127 (command not found). stderr had the answer. But code dropped stderr because stdout was non-empty.

Agent tried 10 different approaches before success:
```
pip install → 127
python3 -m pip → 1
uv pip install → 1
pip3 install → 127
sudo apt install → 127
...
uv run --with pymupdf python3 script.py → 0 ✓ (10th try)
```

**Lesson:** stderr is the information agents need most, precisely when commands fail. Never drop it.

---

### Lesson 3: Large Context = Attention Collapse

**Story:** 5,000-line log file (198KB) stuffed into context. LLM attention overwhelmed. Earlier conversation pushed out. Response quality collapsed.

**With overflow mode:**
```
[first 200 lines]
--- output truncated (5000 lines, 198.5KB) ---
Full output: /tmp/cmd-output/cmd-3.txt
Explore: cat /tmp/cmd-output/cmd-3.txt | grep <pattern>
[exit:0 | 45ms]
```

Agent saw structure, used grep to pinpoint issue. 3 calls total, <2KB context.

**Lesson:** Give the agent a "map" not the "territory."

---

## Progressive Disclosure (Not All-At-Once)

### Level 0: Tool description (injected at conversation start)
```
Available commands:
 cat — Read a text file. For images use 'see'. For binary use 'cat -b'.
 see — View an image (auto-attaches to vision)
 ls — List files in current topic
 write — Write file
 grep — Filter lines matching pattern
 memory — Search or manage memory
 clip — Operate external environments
 ...
```

### Level 1: Usage (on-demand)
```
→ run(command="memory")
[error] memory: usage: memory search|recent|store|facts|forget
```

### Level 2: Parameters (drilled down)
```
→ run(command="memory search")
[error] memory: usage: memory search <query> [-t topic_id] [-k keyword]
```

**Philosophy:** Don't stuff 3,000 words of documentation in context. Agent discovers on demand.

---

## Three Heuristic Techniques

| Technique | Question | Mechanism |
|-----------|---------|-----------|
| --help | "What can I do?" | Progressive discovery |
| Error message | "What should I do instead?" | Reactive correction |
| Output format | "How did it go?" | Continuous learning |

---

## When CLI Breaks Down

- **Strongly-typed interactions:** DB queries, GraphQL (schema validation > string parsing)
- **High-security environments:** CLI string concatenation has injection risk
- **Native multimodal:** Audio/video where text pipe is bottleneck

---

## Business Applications

This architecture is what separates **production AI agents** from **AI agent demos**.

Companies building real AI agents need:
1. Someone who understands this two-layer architecture
2. Can implement it for their specific use case
3. Can debug when it breaks in production

**Service offering:** "AI Agent Production Readiness Audit" + "Architecture Implementation"

---

## Source

- Post: r/LocalLLaMA "I was backend lead at Manus. After building agents for 2 years..."
- Author: Former Manus backend lead
- GitHub: github.com/epiral/agent-clip (reference implementation in Go)


path: services/ai-ops-playbook.md
content: # AI Agent Operations — Service Playbook

> The real money in AI agents is NOT in building. It's in running.
> Source: Reddit r/Entrepreneur — "The real AI gold rush isn't in building. It's in babysitting."

---

## The Positioning

**NOT:** "I will build you an AI agent"
**YES:** "I will run your AI operations so you never have to think about it"

---

## Two Service Tiers

### Tier 1: AI Agent Setup (One-Time)
- Configure and deploy your first AI agent
- Connect to your tools (email, calendar, CRM, etc.)
- Train on your workflows
- Document everything
- **Price:** $500-$2,000
- **Time:** 1-3 days

### Tier 2: AI Operations Management (Recurring) ⭐ RECOMMENDED
- I run all your AI agents 24/7
- Monitor performance and fix issues
- Update prompts when context shifts
- Swap models when better/cheaper options emerge
- Weekly summary reports
- **Price:** $500-$2,000/month
- **Time:** 2-4 hours/week

---

## What I Actually Do (AI Ops)

Based on Reddit case study — a solo founder's "AI cofounder" does:

1. **Content Operations**
   - Research: audit data sources, find gaps
   - Writing: generate blog posts, social media, email drafts
   - Publishing: schedule and distribute

2. **Research & Intelligence**
   - Monitor competitors (pricing, products, job postings)
   - Track industry news, flag relevant items
   - Due diligence on partners/vendors

3. **Workflow Automation**
   - Connect tools that don't talk to each other
   - Automate repetitive data entry
   - Set up triggers and alerts

4. **Analytics & Reporting**
   - Daily/weekly summaries of key metrics
   - Anomaly detection (traffic drops, conversion changes)
   - Actionable insights from data

5. **Development Support**
   - Write and review code
   - Open pull requests for your review
   - Research technical solutions to problems

---

## Target Clients

1. **Solo founders / indie hackers** — "I need a cofounder who works 24/7"
2. **Small agencies** — automating client reporting, lead gen
3. **Consultants** — automating research and report generation
4. **E-commerce operators** — inventory monitoring, competitor tracking

---

## Outreach Message Template

```
Subject: Your AI operations, handled

Hi [Name],

I run AI agents for [type of business] owners who are tired of:
- Spending hours on tasks an agent could handle in minutes
- Building automations that break and nobody fixes
- Prompts that go stale the moment context shifts

I don't just build agents. I manage them — 24/7 monitoring, prompt updates, model swaps, and weekly summaries.

No setup fees. You pay monthly only if it works.

Would you be open to a 15-minute call to see if this fits?
```

---

## Pricing Examples

| Client Type | Setup | Monthly |
|-------------|-------|---------|
| Solo founder, basic needs | $500 | $300/mo |
| Solo founder, full ops | $1,000 | $800/mo |
| Small team (3-5 people) | $2,000 | $1,500/mo |
| Agency, white-label | $3,000 | $2,500/mo |

---

## Qualification Questions (for discovery call)

1. What are the top 3 tasks you spend time on that feel repetitive?
2. What tools are you currently using that don't work well together?
3. What would being "free from [task X]" mean for your business?
4. How many hours do you currently spend on [identified task] per week?

---

## What to Track Per Client

- Hours saved (per week/month)
- Tasks automated
- Reports delivered
- Issues resolved
- Models/agents running

This builds the case for renewal and upsell.


path: skills/index.md
content: # Beta Skills Index

> Complete catalog of Beta's capabilities.

## By Category

### Trading & Finance
| Skill | Description |
|-------|-------------|
| `trading-bot-framework` | Build automated trading systems |
| `ta-quant-pipeline` | Technical analysis signal generation |
| `signal-alert-system` | Real-time market alerts |
| `spx-analysis` | Pre-market SPX analysis |
| `options-chains` | Options chain analysis |
| `portfolio-monitor` | Portfolio tracking & rebalancing |
| `risk-management` | Position sizing, drawdown protection |
| `trade-journal` | Trading journal & review |
| `backtester` | Strategy backtesting |
| `earnings-whisper` | Earnings announcement analysis |
| `vix-analysis` | VIX volatility analysis |
| `sentiment-news` | News sentiment analysis |
| `flow-analysis` | Market flow analysis |
| `mev-strategy` | DeFi MEV strategies |
| `nft-floor-analysis` | NFT floor price analysis |

### AI & Automation
| Skill | Description |
|-------|-------------|
| `ai-agent-systems` | AI agent architecture |
| `red-team-testing` | AI security testing |
| `prompt-injection-detect` | Prompt injection detection |
| `mcp-security-check` | MCP server security |
| `mcp-tool-builder` | MCP server development |
| `ai-compliance-review` | EU AI Act, SOC2, GDPR |
| `ai-red-team` | AI agent red teaming |

### Content & Growth
| Skill | Description |
|-------|-------------|
| `youtube-automation` | YouTube content pipeline |
| `youtube-seo` | YouTube SEO optimization |
| `youtube-shorts` | Shorts creation |
| `twitter-content` | Twitter content creation |
| `twitter-growth` | Twitter growth strategy |
| `twitter-thread` | Thread writing |
| `reddit-outreach` | Reddit community management |
| `linkedin-growth` | LinkedIn presence |
| `video-script` | Video scripting |
| `video-description` | Video SEO descriptions |
| `podcast-summary` | Podcast transcription & summary |

### Development
| Skill | Description |
|-------|-------------|
| `telegram-bot` | Telegram bot development |
| `browser-extension` | Chrome/Firefox extension |
| `web-scraping` | Data extraction from any site |
| `api-integration` | REST/GraphQL integration |
| `data-pipeline` | Python data pipelines |
| `playwright-automation` | Browser automation |
| `full-stack-dev` | FastAPI + React development |
| `smart-contract-audit` | Solidity security audit |

### Business & Research
| Skill | Description |
|-------|-------------|
| `deep-research` | Research reports any topic |
| `citation-audit` | AI search GEO optimization |
| `lead-gen-automation` | Lead generation systems |
| `market-research` | Competitor/market analysis |
| `pitch-deck-generator` | Investor pitch decks |
| `meeting-notes-ai` | Meeting summarization |
| `competitive-analysis` | Competitive research |
| `saas-metrics` | SaaS business metrics |

### Gig Optimization
| Skill | Description |
|-------|-------------|
| `gig-description-writer` | High-converting gig copy |
| `coinpay-did-integration` | CoinPay DID setup |

## Skills by Count

Total: **139 skills** (as of 2026-03-29)

## Recently Added

- `coinpay-did-integration` — DID registration & SDK integration
- `gig-description-writer` — Gig title & description optimization
- `trading-bot-framework` — Trading automation
- `twitter-content` — Twitter growth
- `reddit-outreach` — Community management

## Knowledge Base

All skills are documented in:
- `knowledge/platforms/` — Platform-specific knowledge
- `knowledge/trading/` — Trading strategies
- `knowledge/outreach/` — Outreach templates
- `knowledge/skills/` — Skill frameworks


path: trading/strategies.md
content: # Trading Knowledge Base

> Beta's accumulated trading research.

## Core Skills Available

| Skill | Path | Description |
|-------|------|-------------|
| `trading-bot-framework` | skills/trading-bot-framework | Build automated trading bots |
| `ta-quant-pipeline` | skills/ta-quant-pipeline | Technical analysis signal pipeline |
| `signal-alert-system` | skills/signal-alert-system | Real-time trading alerts |
| `spx-analysis` | skills/spx-analysis | SPX/market direction analysis |
| `options-chains` | skills/options-chains | Options chain analysis |
| `portfolio-monitor` | skills/portfolio-monitor | Portfolio tracking |
| `risk-management` | skills/risk-management | Position sizing, drawdown protection |
| `backtester` | skills/backtester | Strategy backtesting |

## Trading Gig Services Offered

1. **Pre-Market Trading Plan** — SPX key levels, direction bias, entry/exit zones
2. **Quantitative Trading Research** — Backtest any strategy, discover alpha
3. **Options Trading Strategy Analysis** — Greeks decomposition, optimal setups
4. **AI Trading Bot Development** — Pine Script, Python, TradingView automation
5. **Risk Management System** — Position sizing, drawdown protection
6. **Portfolio Analysis & Rebalancing** — Allocation optimization
7. **TradingView Indicator Development** — Custom Pinescript

## Market Analysis Framework

### Pre-Market Checklist

1. **SPX/ES futures** — direction and overnight gap
2. **VIX** — fear gauge, above 20 = caution
3. **Sector rotation** — who's leading/lagging
4. **Options flow** — unusual call/put activity
5. **Economic data** — calendar events
6. **Support/Resistance** — key levels from previous session

### Technical Indicators Used

- SMA 20/50/200 (trend)
- RSI (momentum, overbought >70, oversold <30)
- MACD (trend momentum)
- Bollinger Bands (volatility)
- Volume profile
- VWAP

## Key Trading Rules

1. **Never risk more than 1-2% per trade**
2. **Always have an exit plan before entry**
3. **Trend is your friend** — trade with trend
4. **Cut losses quickly, let winners run**
5. **Respect the daily chart direction**
6. **Pre-market determines the day's bias**

## Platforms & Tools

- **TradingView** — Charting, Pine Script
- **Tiger API** — Market data
- **Alpaca** — Brokerage for paper/live trading
- **CCXT** — Crypto exchange aggregation

## Research Findings

### CoinMarketCap Data (from previous research)

- CMC API is free for basic data
- Good for: price, market cap, volume, OHLCV
- Rate limit: ~10-30 requests/minute
- Python library: `python-coinmarketcap`

### Trading Bot Architecture

```
Data Feed → Signal Generation → Risk Check → Order Execution → Portfolio Management
    ↓              ↓                  ↓              ↓              ↓
 CCXT/Tiger   TA indicators    Position sizing   Exchange API    P&L tracking
```

## Market Research Topics Done

- [x] SPX futures and market bias
- [x] Options flow analysis basics
- [x] Risk management frameworks
- [x] Backtesting methodology
- [x] MEV and DeFi trading opportunities
- [x] Sentiment analysis sources


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: stale | SkillSpector: suspicious (HIGH)