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skills/product-guide/SKILL.md
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--- name: product-guide description: | Guide to Allium's four core products: Explorer, Realtime APIs, Datastreams, and Datashares. Explains what each product does, who it's for, and how to choose the right one for a customer's use case. Read this skill when a customer asks about Allium's products, wants to understand the difference between them, needs help choosing a product, or asks questions like "which product should I use?" or "what does Allium offer?" It also carries the canonical, dated list of supported blockchains — read it for any "what chains / how many chains does Allium support?" question and cite that list instead of answering from memory. --- # Allium Product Guide Allium offers four core products for accessing blockchain data. Each serves a different access pattern, latency requirement, and integration style. Explorer, Datashares, and Datastreams share one historical data platform; the Realtime APIs serve a smaller, separate set of chains. > **Answering "what chains does Allium support?" (grounding rule)** > > Chain names and counts must come from the canonical list, never from memory — > generating them from memory produces unstable totals and invented chains. > > 1. Fetch the **Allium Supported Chains — Canonical List** reference file (see > "Reference files" below) and quote it. For an up-to-the-minute Realtime > figure, call the `realtime_get_supported_chains` tool. > 2. Always say **which product** a count refers to — the historical data > platform (Explorer / Datashares / Datastreams) and the Realtime APIs have > different totals. > 3. The headline total must equal the number of rows for that product in the > canonical list. Do not round to a vague "80+/130+/150+" — cite the exact > count and state the list's snapshot date in the answer, so a reader can see > how current it is (e.g. "**[N]** chains on the historical data platform, as > of **[snapshot date]**"). Pull both **[N]** and the date from the fetched > reference file — never from this instruction or from memory. > 4. If a chain is not in the canonical list, say it is not currently supported > rather than guessing. ## Product Overview | Product | Access Pattern | Latency | Pricing Model | Best For | | ----------------- | ----------------------------------- | -------------------------------------- | -------------------------- | --------------------------------------------------- | | **Explorer** | SQL queries via web UI or API | ~1 hour freshness, ~4-5s query time | Query compute time | Ad-hoc analysis, dashboards, research | | **Realtime APIs** | REST API (pull) | 50-100ms response, 3-5s data freshness | API call volume | Application backends, wallets, trading UIs | | **Datastreams** | Push via Kafka/PubSub/SNS/WebSocket | 3-5s data freshness | Data destinations & egress | Event-driven architectures, real-time pipelines | | **Datashares** | Native tables in your warehouse | 1-3 hours batch, sub-minute streaming | Number of chains & schemas | Institutional analytics, joining with internal data | ## Explorer **What it is:** A SQL-based analytics workspace at `app.allium.so/explorer` that lets users query, visualize, and share blockchain data across every chain on the historical data platform (see the canonical chain list for the exact count). Powered by Snowflake (OLAP). **Key capabilities:** - Full SQL interface with cross-chain queries (e.g., `crosschain.dex.trades`) - 1,000+ enriched schemas: raw, decoded, DEX, DeFi, NFTs, stablecoins, wallet 360, metrics, bridges, prices, PnL - Interactive chart builder with public sharing and embed links - CSV and API data upload — join your own data with on-chain data - Parameterized queries for reusable, dynamic SQL - AI Assistant for natural-language-to-SQL - Explorer API — programmatic query lifecycle (create, run, fetch results, cancel) - Curated metrics dashboards for stablecoins, DEXs, bridges, etc. **Target users:** - Analytics teams at crypto companies (foundations, protocols, wallets) - Data analysts and researchers - Audit, accounting, and compliance teams (Big 4 firms) - Growth and marketing teams tracking ecosystem metrics **Common use cases:** - User behavior analytics and wallet activity patterns - Ecosystem metrics and competitive intelligence - Sybil detection for airdrop eligibility (used by Jito, Wormhole) - Account reconciliation and auditing (used by Big 4 firms) - DEX adoption dashboards (Uniswap Foundation) - Financial monitoring and tax reporting (TaxBit) **When to recommend Explorer:** - Customer wants to explore data interactively with SQL - Ad-hoc analysis, research, or one-off investigations - Building shareable dashboards and visualizations - Uploading proprietary data to join with on-chain data - Needs the broadest chain coverage (the full historical data platform; see the canonical chain list) and deepest schema library (1,000+) - Hourly data freshness is acceptable ## Realtime APIs **What it is:** Production-grade REST APIs delivering real-time, enriched blockchain data with 50-100ms response times and 3-5 second data freshness. Realtime covers fewer chains than the historical data platform — quote the canonical chain list (or the live `realtime_get_supported_chains` tool) for the exact set and count. **Key capabilities:** - **Prices** — real-time and historical token prices from on-chain DEX trades (not centralized exchanges). OHLC candles, VWAP, z-score outlier filtering. New tokens priced within seconds of first DEX trade (including pump.fun launches) - **Tokens** — metadata, type, price, decimals, FDV, volume, trade count, holders, ATH/ATL, liquidity, creation time. Sortable and searchable - **Wallets** — current balances (native + ERC-20/SPL), historical balances at any point in time, full transaction history with activity labels (swaps, transfers, DEX trades) - **Holdings** — real-time and historical USD portfolio holdings with built-in PnL using average cost basis. Multi-granularity (15s/5m/1h/1d) - **Assets** — batch-fetch asset details by chain + address - **Hyperliquid** — dedicated endpoints for the Hyperliquid protocol - Custom endpoints backed by your own SQL queries **Performance:** - 50-100ms response time - 1-1.2s raw block freshness, 3-5s enriched data freshness - Tested to 90,000 RPS (Phantom/Jupiter airdrop: 477M requests in 4 hours) - 99.9% uptime SLA **Authentication:** API key via `X-API-KEY` header. Generate at Settings > API Keys. **Target users:** - Application developers building crypto products - Wallet teams (Phantom, MetaMask) - DEX/trading platforms (Uniswap, Fomo) - Payment providers (MoonPay, Bridge.xyz) - Fraud detection systems (Cube3.ai, Blowfish) - AI agent builders **When to recommend Realtime APIs:** - Customer is building an application that needs to pull data on demand - Needs sub-second response times for user-facing features - Wallet balances, transaction history, token prices, or portfolio PnL - Request-response pattern fits their architecture - Needs instant coverage of new tokens (long-tail/pump.fun) - Building token screeners, trading interfaces, or portfolio trackers ## Datastreams **What it is:** Real-time push-based data delivery via enterprise message brokers. Enriched, decoded blockchain data from the historical data platform (see the canonical chain list for the exact count) streamed with 3-5 second latency and guaranteed delivery. **Key capabilities:** - Delivery via **Kafka**, **Google Cloud Pub/Sub**, **Amazon SNS** (coming soon), **WebSockets**, and **webhooks** - Guaranteed delivery and message ordering (Kafka/PubSub) - Historical replay via retention policies - **Stream Transformations** — managed filter-and-route pipelines: - Data source filters (dynamic address/contract lists, updateable without restart) - Declarative JSON filters with `=`, `!=`, `>`, `<`, `in`, `exists`, `AND`/`OR` - Workflows: `source → filter → destination` - **Beam** (custom transforms) — JavaScript v8 transforms and redis set filters on any stream, with Kafka/SNS sinks. See `beam-pipelines` skill for details - Compression (lz4, zstd, gzip) - WebSocket streaming of all Kafka topics for simpler integration **Data entities:** Raw (blocks, transactions, logs, traces), decoded logs/traces, DEX trades, token transfers, balances — across the historical data platform (see the canonical chain list for the exact count). **Target users:** - Teams building event-driven blockchain infrastructure - Wallet backends (Phantom) - Market intelligence platforms (Messari) - Fraud detection engines (Blowfish) - Back-office reconciliation systems (Bridge) - Trading platforms needing real-time token/trade feeds **When to recommend Datastreams:** - Customer needs push-based, event-driven data delivery - Building real-time alerts, monitoring, or anomaly detection - Wants guaranteed delivery with replay capability - Needs to filter high-volume streams to specific contracts, addresses, or events - Architecture is built around Kafka, PubSub, or message queues - Needs custom transformations on streaming data (→ Beam) - Wants data pushed rather than polling an API ## Datashares **What it is:** Managed delivery of production-ready blockchain data as native tables directly into your own data warehouse or data lake. Allium handles bulk ingestion, incremental updates, schema migrations, reorg handling, and data quality. **Key capabilities:** - **Snowflake** — native Data Shares (zero-copy). Primary region: GCP US Central 1. Worldwide delivery with 3-hour freshness - **BigQuery** — via Google Analytics Hub. Regions: US Central 1, EU West 2 - **Databricks** — via Delta Sharing. Sub-minute streaming available. Recommended: AWS us-east-2 - **Amazon S3** / **Google Cloud Storage** — Iceberg format data dumps - Apache Iceberg table format with backward-compatible schema evolution - SOC 1 & SOC 2 (Type I & II) certified pipelines - Full privacy — Allium cannot see your queries, joins, or results - No query metering — you control and pay for your own compute - Native BI tool connectors: Tableau, Looker, Power BI, Hex, Sigma, Omni **Data coverage:** the full historical data platform (see the canonical chain list for the exact count), 1,000+ enriched schemas (raw, decoded, DEX, DeFi, NFTs, stablecoins, wallet 360, metrics, bridges, staking). **Target users:** - Institutional analytics teams needing data in their own environment - Audit, accounting, and compliance teams at regulated institutions - Data science teams building models on blockchain data - Companies that must join on-chain data with proprietary internal data **Notable customers:** Visa, Coinbase, Grayscale, Paradigm, Stripe, Uniswap Foundation, MoonPay, Electric Capital. **When to recommend Datashares:** - Customer already has a data warehouse (Snowflake, BigQuery, Databricks) - Needs to join blockchain data with internal/proprietary data - Privacy and data sovereignty are requirements (regulated industries) - Running heavy analytical workloads where controlling compute costs matters - Building ML/AI models on blockchain data - Needs petabyte-scale historical data for backtesting or research - Compliance, audit, or accounting use cases at institutions ## How to Choose the Right Product ### Decision Framework **Start with the access pattern:** 1. **"I want to explore and analyze data interactively"** → **Explorer** 2. **"I'm building an app and need to fetch data on demand"** → **Realtime APIs** 3. **"I need data pushed to my systems in real-time"** → **Datastreams** 4. **"I want blockchain data in my own warehouse"** → **Datashares** ### By Use Case | Customer Need | Recommended Product | | --------------------------------------------------- | -------------------- | | Ad-hoc SQL analysis and dashboards | Explorer | | Research and data exploration | Explorer | | Shareable charts and visualizations | Explorer | | Wallet balances and transaction history for an app | Realtime APIs | | Token prices for a trading interface | Realtime APIs | | Portfolio PnL in a consumer product | Realtime APIs | | Real-time alerts on smart contract events | Datastreams | | Streaming DEX trades to an analytics pipeline | Datastreams | | Custom filtered feeds (specific wallets, contracts) | Datastreams (+ Beam) | | Institutional-grade historical analytics | Datashares | | Joining on-chain + internal data for compliance | Datashares | | ML model training on blockchain data | Datashares | | BI dashboards in Tableau/Looker/Power BI | Datashares | ### By Latency Requirement | Requirement | Product | | ------------------------- | ---------------------------------------------------------------------------------- | | Sub-second response time | Realtime APIs (50-100ms) | | Low-second streaming | Datastreams (3-5s) | | Near-real-time analytics | Explorer (~1 hour) or Datashares (1-3 hour batch, sub-minute Databricks streaming) | | Batch/historical analysis | Explorer or Datashares | ### By Team Profile | Team | Start With | | ---------------------------------- | -------------------------------------------------------------------------------------------- | | Data analysts writing SQL | Explorer | | Backend engineers building APIs | Realtime APIs | | Infrastructure/platform engineers | Datastreams | | Data engineering / warehouse teams | Datashares | | Compliance / audit teams | Datashares (for privacy + joining internal data) or Explorer (for interactive investigation) | ### Common Multi-Product Patterns Many customers use multiple products together: - **Explorer + Datashares**: Explore and prototype queries in Explorer, then productionize with Datashares in their warehouse - **Realtime APIs + Datastreams**: APIs for user-facing request-response, Datastreams for backend event processing - **Datashares + Explorer**: Datashares for heavy analytics in their warehouse, Explorer for ad-hoc investigation and sharing - **Datastreams + Datashares**: Datastreams for real-time event processing, Datashares for historical backfill and batch analytics ### Pricing Comparison | Product | Model | Implication | | ------------- | --------------------- | -------------------------------------------------- | | Explorer | Query compute time | Cost scales with query complexity and frequency | | Realtime APIs | API call volume | Cost scales with request volume | | Datastreams | Destinations & egress | Cost scales with number of streams and data volume | | Datashares | Chains & schemas | Predictable cost based on data coverage selected | All products require contacting Allium for specific pricing. Customers can sign up for a free trial at `app.allium.so/join` for API access, or book a demo for enterprise needs.
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