by QVeris
Use this skill when an agent needs a source-backed alt data demand signal workflow. It uses QVeris to discover, inspect, and call the required data capabilities, then returns an auditable brief with evidence strength, missing data, estimated usage, and risk notes.
Use public demand proxies, search, social, app, review, traffic, news, and price reaction to monitor demand shifts.
Investor research agents, portfolio monitors, and analyst workflows
A source-backed research brief with evidence, risk notes, QVeris calls used, and estimated credits.
Each article or tutorial is treated as a reusable workflow source: content, copied prompt, QVeris API recipe, and expected output.
Product article
Turn structured financial data into callable agent capabilities.
Use QVeris to find alternative data demand signals for consumer AI apps. Include data sources discovered, capabilities inspected, paid Calls used, estimated credits, evidence gaps, and risks.
Analyst memo · Markdown memo
Product article
Add market data coverage for global research and screening workflows.
Use QVeris to run alt data demand signal for AAPL, NVDA, MSFT, and TSLA. Rank by evidence quality and market relevance.
Signal table · Table
Tutorial
A practical workflow for source-backed A-share monitoring.
Use QVeris to find alternative data demand signals for consumer AI apps. Include data sources discovered, capabilities inspected, paid Calls used, estimated credits, evidence gaps, and risks.
Audit appendix · JSON / appendix
Starter prompts that turn the skill into executable agent work.
Run the alt data demand signal workflow with QVeris data.
Use QVeris to find alternative data demand signals for consumer AI apps. Include data sources discovered, capabilities inspected, paid Calls used, estimated credits, evidence gaps, and risks.
Run the workflow across a bounded ticker list.
Use QVeris to run alt data demand signal for AAPL, NVDA, MSFT, and TSLA. Rank by evidence quality and market relevance.
The formats an agent should return after the workflow runs, with enough structure for reuse and auditing.
A concise research memo with conclusion, evidence, dissent, and caveats.
A scan-friendly table for tickers, sources, events, or watchlist items.
A provenance record of capabilities, sources, windows, and estimated cost.
The concrete Discover, Inspect, and Call sequence this skill expects the agent to run.
POST /searchFind search, social, app, review, traffic, news, and quote data capabilities needed for this workflow.
Sample query: alternative data demand search social app review traffic news API
POST /tools/by-idsCheck coverage, parameters, freshness, latency, success signals, and billing_rule before paid calls.
POST /tools/executeExecute bounded QVeris calls and compose results into a traceable finance brief.
A planning estimate before execution. Discover and Inspect are free; successful Call execution follows the selected provider billing rule.
This workflow usually needs 6-24 paid Calls after free Discover and Inspect preflight. Cost depends on provider billing, ticker count, and time window.
Ask for explicit approval before paid Calls. Reduce tickers, windows, or source count if the estimate is above budget.
Install the skill in the target agent environment. Agents must ask before running commands or changing local configuration.
This is the source of record for QVeris skills. Inspect or fork the skill folder before installing it in an agent environment.
QVerisAI/open-qveris-skills/qveris-alt-data-demand-signalgit clone https://github.com/QVerisAI/open-qveris-skills.git && cd open-qveris-skills/qveris-alt-data-demand-signalopenclaw skills install qveris-alt-data-demand-signalThe visible chain the agent should expose after the user copies a prompt.
Define market, tickers, time window, output format, and maximum paid-call budget.
Use QVeris Discover and Inspect to choose sources before paid execution.
Call only the required data capabilities and preserve source traceability.
Return findings, evidence strength, missing proof, usage trace, and not-investment-advice language.
Read manifest and agent.md first. Explain the install command, API actions, and possible credit usage. Wait for explicit approval before making local changes.