Use this skill when an agent needs to turn a market theme into a source-backed supply-chain research workflow. It discovers finance, filings, company, news, and social capabilities through QVeris, inspects coverage and billing rules, then calls selected providers before ranking scarce layers and candidate companies.
Task value
A QVeris-powered investment research workflow that maps value chains, finds scarce layers, ranks public-company research priorities, and cites live data capabilities.
Best for
Investor research agents, portfolio monitors, and analyst workflows
Expected output
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.
Content source
Product article
Use FMP with QVeris
Turn structured financial data into callable agent capabilities for thesis verification.
Copied prompt
Use QVeris to deeply research AI infrastructure supply-chain bottlenecks. Map the value chain, discover and inspect finance, filings, news, and company data capabilities, call the needed sources, rank the top 5 public-company research priorities, cite QVeris capabilities used, estimate paid Call count, and explain what could weaken each view.
A practical workflow for A-share monitoring and source-backed market research.
Copied prompt
Use QVeris to scan the A-share AI semiconductor value chain. Build at least 20 candidates if data coverage allows, rank layers before companies, call QVeris data for announcements, financial statements, company profiles, and news, then return a top 5 research priority list with evidence strength and main risks.
Add market data coverage for global research and candidate screening.
Copied prompt
Challenge the thesis that this company is a core supplier in its supply chain. Use QVeris to call filings, company profile, financials, quote, news, and relevant social or trade signals. Explain the exact value-chain position, evidence strength, missing proof, and what would make the thesis wrong.
Starter prompts that turn the skill into executable agent work.
AI infrastructure bottlenecks
Map scarce layers and rank public-company research priorities.
Use QVeris to deeply research AI infrastructure supply-chain bottlenecks. Map the value chain, discover and inspect finance, filings, news, and company data capabilities, call the needed sources, rank the top 5 public-company research priorities, cite QVeris capabilities used, estimate paid Call count, and explain what could weaken each view.
A-share AI semiconductor scan
Build an A-share candidate universe, verify evidence, and rank scarce-layer exposure.
Use QVeris to scan the A-share AI semiconductor value chain. Build at least 20 candidates if data coverage allows, rank layers before companies, call QVeris data for announcements, financial statements, company profiles, and news, then return a top 5 research priority list with evidence strength and main risks.
Challenge a company thesis
Use QVeris evidence to test whether a company truly controls a scarce layer.
Challenge the thesis that this company is a core supplier in its supply chain. Use QVeris to call filings, company profile, financials, quote, news, and relevant social or trade signals. Explain the exact value-chain position, evidence strength, missing proof, and what would make the thesis wrong.
Expected Outputs
The formats an agent should return after the workflow runs, with enough structure for reuse and auditing.
Markdown memo
Analyst memo
A concise research memo with conclusion, evidence, dissent, and caveats.
Sections
Scope
Key findings
Evidence table
Risks and dissent
QVeris calls and credits
Table
Signal table
A scan-friendly table for tickers, sources, events, or watchlist items.
Sections
Subject
Signal
Evidence strength
Market relevance
Next verification
JSON / appendix
Audit appendix
A provenance record of capabilities, sources, windows, and estimated cost.
Sections
capabilities_used
sources
paid_calls
estimated_credits
QVeris API Recipe
The concrete Discover, Inspect, and Call sequence this skill expects the agent to run.
Recipe step 01DiscoverPOST /search
Find research data capabilities
Search for market data, financial statements, company profiles, filings, transcripts, announcements, news, and social signal capabilities.
Sample query: stock filings financial statements company news API
FMPTwelve DataTHS iFinDCaidaziFinnhubX
Recipe step 02InspectPOST /tools/by-ids
Inspect coverage and billing
Verify market coverage, ticker parameters, output schema, latency, success rate, and billing_rule before the agent calls a provider.
Capability schemasProvider metricsBilling rules
Recipe step 03CallPOST /tools/execute
Call evidence sources
Execute selected data capabilities and compose results into a value-chain bottleneck ranking with source-backed evidence.
Market data providersFinancial data providersNews and social providers
QVeris Usage & Cost
A planning estimate before execution. Discover and Inspect are free; successful Call execution follows the selected provider billing rule.
Typical paid calls8-25
Estimated credits8-250 credits
Free actions
DiscoverInspect
Paid action
Call
Theme scan usage estimate
A single-company challenge typically needs a small set of paid calls; a broad theme scan may call several market, filing, financial, news, and company-profile capabilities after free discovery and inspection.
Ask for user approval before running paid Calls. Inspect each selected capability's billing_rule and reduce scope if the estimated cost is too high.
Installation
Install the skill in the target agent environment. Agents must ask before running commands or changing local configuration.
Official GitHub source
This is the source of record for QVeris skills. Inspect or fork the skill folder before installing it in an agent environment.
The visible chain the agent should expose after the user copies a prompt.
01
Scope the research question
Clarify market, theme, time window, and whether the task is a theme scan, company challenge, or candidate comparison.
02
Discover and inspect QVeris data
Find required data capabilities, inspect coverage and billing, and ask approval before paid execution.
03
Call sources and map layers
Call selected providers, map value-chain layers, and separate strong evidence from leads.
04
Rank and challenge
Rank scarce layers and public-company priorities, then explain risks, missing proof, QVeris calls used, and next verification steps.
Install policy
Read manifest and agent.md first. Explain the install command, API actions, and possible credit usage. Wait for explicit approval before making local changes.
