checkr

# checkr Real-time X/Twitter attention intelligence for Base chain tokens. **Base URL:** `https://api.checkr.social` **Docs:** `https://api.checkr.social/docs` **Payment:** x402 — USDC on Base mainnet, pay-per-call, no account needed. ## Endpoints | Endpoint | Price | What it returns | |---|---|---| | `GET /v1/leaderboard` | $0.02 | Top Base tokens ranked by social attention share | | `GET /v1/spikes` | $0.05 | Tokens currently velocity-spiking (the radar sweep) | | `GET /v1/token/{symbol}` | $0.50 | Deep dive: ATT deltas, price, divergence, narrative | | `GET /v1/bankr` | $0.02 | Attention leaderboard for the bankr agent ecosystem | Full response schemas and field definitions: `https://api.checkr.social/docs` ## How to Call (x402) x402 is pay-per-call. No API key or account. Wallet + USDC on Base is all you need. **Python:** ```python from x402.client import x402_client client = x402_client(wallet=YOUR_WALLET) # What's spiking right now — $0.05 spikes = client.get("https://api.checkr.social/v1/spikes").json() # Top tokens by attention — $0.02 leaderboard = client.get("https://api.checkr.social/v1/leaderboard").json() # Deep dive on a token — $0.50 token = client.get("https://api.checkr.social/v1/token/BNKR").json() ``` **TypeScript:** ```typescript import { withPaymentInterceptor } from "x402-axios"; import axios from "axios"; const client = withPaymentInterceptor(axios.create(), walletClient); const { data } = await client.get("https://api.checkr.social/v1/spikes"); ``` Payment is handled automatically by the x402 client — it intercepts the 402, signs and sends payment, then retries with the receipt. ## Practical Flow Use spikes as your radar. Drill into token for context. ```python # 1. What's moving? spikes = client.get("https://api.checkr.social/v1/spikes").json() # → [{ symbol: "TIBBIR", velocity: 3.9, ATT_pct: 11.4, divergence: false, hawkes: {...} }] # 2. Deep dive on the top spike top = spikes["spikes"][0]["symbol"] detail = client.get(f"https://api.checkr.social/v1/token/{top}").json() # → full price, divergence, spike history, narrative ``` ## Key Fields **On every response:** - `data_age_minutes` — how fresh the data is. Use before acting. **On spikes:** - `velocity` — momentum multiplier vs baseline. 3.0+ = meaningful spike. - `divergence` — `true` = attention up, price flat/down. The alpha pattern. - `hawkes.viral_class` — `BUILDING` / `SUSTAINED` / `FADING`. Is this self-reinforcing? - `rotating_from` — tokens losing attention as this one gains. - `narrative_summary` — AI-generated 180-char brief. `null` if signal below confidence threshold. **On token deep dive:** - `ATT_delta_1h` / `ATT_delta_4h` — attention share movement over time. - `spike_history.hit_rate` — % of past spikes with confirmed price follow-through. - `narrative.type` — `infrastructure` / `ecosystem` / `fud_defense` / `meme` / `launch_hype`. ## Query Params ``` GET /v1/leaderboard?limit=10&sort_by=ATT_pct&min_mentions=5 GET /v1/spikes?min_velocity=3.0&min_mentions=10&divergence_only=false ``` ## Requirements - USDC on Base mainnet - Python: `pip install x402` - TypeScript: `npm install x402-axios` - Base gas for payment (~$0.01)