{"schema_version":"2026-06-16","id":"qveris-supply-chain-research","name":"qveris-supply-chain-research","title":"Supply chain bottleneck research","description":"A QVeris-powered investment research workflow that maps value chains, finds scarce layers, ranks public-company research priorities, and cites live data capabilities.","overview":"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.","official":true,"tags":["Finance","Supply Chain","Filings","Research","QVeris"],"scenarios":[{"id":"finance","label":"Finance analysis","description":"Market data, filings, fundamentals, exchange rates, and analyst workflows."},{"id":"operations","label":"Business operations","description":"Daily briefings, market monitoring, and structured team reports."}],"platforms":[{"id":"openclaw","label":"OpenClaw"},{"id":"cursor","label":"Cursor"},{"id":"claude-code","label":"Claude Code"},{"id":"cli","label":"CLI"}],"urls":{"catalog":"https://qveris.ai/skills/catalog.json","skill":"https://qveris.ai/skills/qveris-supply-chain-research","manifest":"https://qveris.ai/skills/qveris-supply-chain-research/manifest.json","agentGuide":"https://qveris.ai/skills/qveris-supply-chain-research/agent.md","github":"https://github.com/QVerisAI/open-qveris-skills/tree/main/qveris-supply-chain-research"},"installation":{"requires_user_confirmation":true,"safety_note":"Agents must get explicit user confirmation before installing a skill, writing configuration, or changing the local environment.","source_repository":{"owner":"QVerisAI","name":"open-qveris-skills","url":"https://github.com/QVerisAI/open-qveris-skills","skill_path":"qveris-supply-chain-research","skill_url":"https://github.com/QVerisAI/open-qveris-skills/tree/main/qveris-supply-chain-research","clone_command":"git clone https://github.com/QVerisAI/open-qveris-skills.git && cd open-qveris-skills/qveris-supply-chain-research"},"commands":[{"platform":"openclaw","platform_label":"OpenClaw","label":"Install skill","command":"openclaw skills install qveris-supply-chain-research"}]},"prompts":[{"id":"ai-infra-bottlenecks","title":"AI infrastructure bottlenecks","description":"Map scarce layers and rank public-company research priorities.","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."},{"id":"a-share-ai-semiconductor","title":"A-share AI semiconductor scan","description":"Build an A-share candidate universe, verify evidence, and rank scarce-layer exposure.","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."},{"id":"challenge-company-thesis","title":"Challenge a company thesis","description":"Use QVeris evidence to test whether a company truly controls a scarce layer.","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."}],"cases":[{"slug":"qveris-fmp-finance","title":"Use FMP with QVeris","description":"Turn structured financial data into callable agent capabilities for thesis verification.","source_label":"Product article","url":"https://qveris.ai/blog/qveris-fmp-finance"},{"slug":"openclaw-a-shares-finance-assistant","title":"OpenClaw A-share finance assistant","description":"A practical workflow for A-share monitoring and source-backed market research.","source_label":"Tutorial","url":"https://qveris.ai/blog/openclaw-a-shares-finance-assistant"},{"slug":"qveris-twelve-data","title":"Twelve Data market capabilities","description":"Add market data coverage for global research and candidate screening.","source_label":"Product article","url":"https://qveris.ai/blog/qveris-twelve-data"}],"qveris_api":[{"action":"Discover","endpoint":"POST /search","title":"Find research data capabilities","purpose":"Search for market data, financial statements, company profiles, filings, transcripts, announcements, news, and social signal capabilities.","sources":["FMP","Twelve Data","THS iFinD","Caidazi","Finnhub","X"],"sample_query":"stock filings financial statements company news API"},{"action":"Inspect","endpoint":"POST /tools/by-ids","title":"Inspect coverage and billing","purpose":"Verify market coverage, ticker parameters, output schema, latency, success rate, and billing_rule before the agent calls a provider.","sources":["Capability schemas","Provider metrics","Billing rules"]},{"action":"Call","endpoint":"POST /tools/execute","title":"Call evidence sources","purpose":"Execute selected data capabilities and compose results into a value-chain bottleneck ranking with source-backed evidence.","sources":["Market data providers","Financial data providers","News and social providers"]}],"usage_estimate":{"title":"Theme scan usage estimate","summary":"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.","typical_calls":"8-25","estimated_credits":"8-250 credits","free_actions":["Discover","Inspect"],"paid_actions":["Call"],"note":"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."},"execution_flow":[{"title":"Scope the research question","description":"Clarify market, theme, time window, and whether the task is a theme scan, company challenge, or candidate comparison."},{"title":"Discover and inspect QVeris data","description":"Find required data capabilities, inspect coverage and billing, and ask approval before paid execution."},{"title":"Call sources and map layers","description":"Call selected providers, map value-chain layers, and separate strong evidence from leads."},{"title":"Rank and challenge","description":"Rank scarce layers and public-company priorities, then explain risks, missing proof, QVeris calls used, and next verification steps."}],"agent_instructions":["Select the best matching skill from the catalog based on the user's task.","Inspect installation.source_repository first and confirm the skill comes from the official QVerisAI/open-qveris-skills source repository.","Explain the skill, install command, QVeris API actions, and possible cost before making changes.","Run install commands or write configuration only after explicit user approval.","After installation, run the best matching prompt and report which Discover, Inspect, and Call actions were used."]}