To develop a fully automated process that gathers raw market data, security analytics, and valuation metrics each week and then compiles everything into a clean, institutional-grade Excel workbook ready for distribution. The workbook must go beyond the usual market, credit or operational statistics: I specifically want Launchpads & calendars treated as a formal risk indicator. Inside that section, the launchpad view should disclose the details, including L1s, L2s and ecosystems such as Ethereum, Solana, Avalanche, BNB, Polkadot, Hedera, XRP, Kaspa, Oraichain, XPLA, Alephium, plus ChainGPT/ChainGBT and other AI‑aligned projects. Scheduled on SeaFi, Spores, CryptoRank, ChainGPT Pad, Virtuals Protocol, and other Generative AI & AI agent launchpads. Scrapers: Scheduled to pull data from COPA, launchpads, Virtuals, CoinSniper (with guardrails), and other intel feeds; parse HTML/JSON and save structured records. Data store: Lightweight DB or files (CSV/Parquet/SQLite) to preserve history and enable• On-chain intel feeds (e.g., Lookonchain, selected innovative money/on-chain tools). Using an Excel builder: Include patent watch: USPTO, WIPO/PCT, EPO (Espacenet), and JPO searches tied to those in-scope AI Assets, with mappings to decentralized AI inference, cross‑chain interoperability, DEX liquidity, model routing, and poisoning/defense topics. Output fields: Patent Watch, Overlap Matrix, Infringement Risk Signal (low/medium/high), and Mitigation Notes for each target and ecosystem. COPA: Clarified as a defensive alliance, not a patent office; used as a qualitative signal (membership, pledges, litigation context) rather than a primary patent registry, w Mailer: SMTP or transactional service to send the report automatically each Monday morning. Data intake: launchpads, on‑chain intel feeds (e.g., Lookonchain‑style), token discovery sites, AI launchpads like Virtuals Protocol, and noisy calendars like CoinSniper used only as lead generators with strict secondary checks. Cross‑reference process: require working sites/docs, real GitHub/GitLab code, verified contracts/on‑chain use, and listings on higher‑quality aggregators before dashboard inclusion; treat CoinSniper specifically as a low‑confidence discovery tag •Normalize and store: •New AI asset launches (IDO/ICO/TGE/agent launches). •Status and metrics of active in-scope assets. •Relevant metadata (AI segment, chain, launchpad/platform, detection source, etc.). My ideal flow looks like this: • A script (Python, Power Query, or a comparable automation stack) pulls data from the relevant APIs, blockchain endpoints, and security feeds. • The data is normalised, scored, and time-stamped, then pushed into a templated .xlsx file. • Charts, conditional-format risk flag,s and summary commentary updated automatically, ready for a Monday-morning distribution. Deliverables Preference: a turn‑key, fully managed subscription‑style setup that, once configured, requires no ongoing intervention and still runs all scrapers, storage, Excel, and mailer reliably. Consider using Hostinger, either VPS or Cloud for SSH, cron, Python environment, long‑running/background tasks, and package installs (requests, beautifulsoup4/selectolax, pandas, openpyxl/xlsxwriter, SMTP/mail libs). Front End URL/Back End Data storage (Repository) to maintain a persistent data store (DB or structured files) Feed with: Asset master table (per AI asset), including identifiers and classification •(Generative AI / AI Agents) •AI Agents / Machine Learning / Infra). •Time series metrics (adoption KPIs, price/volume, security events, patent/IP flags, etc.). •Source metadata (discovery source, detection source for security/malware/scam events). •Support weekly snapshot extraction for reporting. Analytics & risk logic The system must compute, for each in-scope asset: (e.g., legal names behind each asset), •Mass Adoption Potential Indicators (e.g., Y2, active wallets, tx/day and 7/30 day averages, TVL/liquidity, integrations, trend). •Patent Infringement Risk Indicators (screening level signals, not legal conclusions). Smart Contract Security & Audit Indicators, including: • Code-level flags (ownership/upgradeability, library usage, risky patterns, privileged functions, oracle/external dependencies). •Audit presence, date, unresolved findings (by severity). •Potential Scam Asset – High Risk badge when defined criteria are met. •New AI Malware Risk – Security Advisory badge when relevant external advisories apply. •Detection Source field citing where the signal originated. Ethical & Trustworthiness Indicators (Last 30 Days): •Time-bound view on truthfulness/safety/fairness results, where available, explicitly scoped to “under tested conditions.” •Optional: Forward Looking Value Scenarios (7/14/21/30/60 days) using quantitative models, not LLMs, with clearly labeled uncertainty. Report generation (Excel builder) A single Excel workbook with multiple linked sheets (launches, patents, Mass Adoption metrics, risk panels), structured so it looks and feels like an institutional spreadsheet, where any user can apply filters and slice data according to their targets The system must: •Generate a single weekly Excel workbook (e.g., Eyes_On_Glass_YYYY-WW.xlsx) containing at minimum: •Launch/roster sheet. •Launchpad / Virtuals / AI agent crosswalk (“Launchpad Crosswalk”). •COPA & patent risk sheets. •Security & audit indicators sheet (including scam and AI malware badges and Detection Source). •Mass adoption KPIs sheet. •Summary dashboard sheet with key KPIs and risk counts. Ensure: •Frozen headers and filterable tables. •Clear, consistent column naming aligned with the above sections The system must: •Every Monday at 6:30 AM ET: •Build the weekly Excel workbook from the latest data. •Feed the results to a Website, looking at Hostinger, VSP Hosting as the Front End/Back End Platform to •Log successful/failed deliveries for basic operational visibility. Non-functional requirements Automation: No manual intervention required once configured. All components must run via scheduled jobs. Reliability: Jobs must be robust enough to handle typical network/API hiccups with retries and basic error logging. Traceability: Key risk flags (scam, AI malware, patent indicators) must include a The Detection Source field shows the originating tool/advisory. Extensibility: The architecture must allow new sources (e.g., additional launchpads, security feeds) and new indicators to be incorporated with minimal rework.