# Qveris News Sentiment Radar > Monitor market-moving news, social sentiment, filings, and price reaction with QVeris so agents can separate confirmed catalysts from noisy attention. Use when an agent needs QVeris-powered finance research, live market data, filings/news evidence, cost-aware tool calls, or source-backed investment analysis for this workflow. - Skill: `qverisai/qveris-news-sentiment-radar` (Agent Skill, multi-file: 46 files) - Install (CLI): `npx skillmds add qverisai/qveris-news-sentiment-radar` - Raw SKILL.md: https://api.skillmd.com/api/skills/qverisai/qveris-news-sentiment-radar/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: qverisai (https://skillmd.com/u/qverisai) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/qverisai/qveris-news-sentiment-radar --- # News sentiment radar ## Standalone Execution Contract Treat this skill folder as self-contained. When the skill is installed or copied alone, run commands from this directory and use `scripts/run.mjs` for dry-run, fixture, and live execution. Do not hand-write QVeris `curl` or ad hoc API calls for normal operation. Manual QVeris calls are allowed only for debugging provider behavior, must be labelled `manual_debug`, and must not be reported as a successful skill E2E run. The skill E2E path is successful only when `scripts/run.mjs` produces the Markdown report, structured JSON, and trace artifact. `scripts/lib/qveris-runtime.mjs` is bundled runtime plumbing for this skill package. No repository-level shared directory is required when using the skill as an installed package. ## Natural-Language Invocation Contract When this skill is triggered by a user request, treat the skill as responsible for the final artifacts. The user should not need to know or request a command. Produce these canonical outputs whenever the user asks for analysis, a report, or a reusable result: - Markdown report - Schema-valid business JSON - QVeris trace JSON with tool IDs, providers, parameters, execution IDs, costs, skipped calls, and missing-data notes Use `scripts/run.mjs` internally to produce the canonical outputs. Always pass a business JSON output path when producing artifacts. In the final response, link the report, business JSON, and trace, and summarize paid calls, credits, execution status, and missing-data limits. Do not create alternate runners, alternate schemas, or one-off JSON shapes for normal use. If the canonical runner lacks a metric, state the gap in `missing_data` and improve this skill later; do not silently replace the skill with ad hoc code. Manual QVeris calls, web search, or provider-specific debugging may supplement the analysis only when labelled `manual_debug`; they cannot replace the canonical runner output or be reported as successful skill E2E. If the user has not authorized paid QVeris calls, stop after dry-run/preflight or ask for approval. If the user authorizes QVeris spend, run live and stay within the stated budget. ## Internal Deterministic Runner Use the local runner internally before composing a free-form answer: ```bash node scripts/run.mjs --dry-run --ticker NVDA --window-days 7 node scripts/run.mjs --live --ticker NVDA --window-days 7 --max-paid-calls 6 --max-credits 40 --output artifacts/live-smoke.md --json-output artifacts/live-smoke.json --trace artifacts/live-smoke-trace.json ``` The runner performs QVeris Discover / Inspect preflight, enforces paid-call and credit budgets, writes a Markdown report, writes schema-valid business JSON, and writes a JSON trace. ## Workflow 1. Clarify ticker, market, time window, user objective, and maximum paid QVeris Call budget. 2. Use QVeris Discover (POST /search) to find the needed finance, market, filing, news, transcript, or social capabilities. 3. Use QVeris Inspect (POST /tools/by-ids) before paid execution. Show coverage, parameters, latency, success signal, and billing_rule when available. 4. Ask for explicit user approval before paid Call execution. 5. Use QVeris Call (POST /tools/execute) only for the bounded sources needed for the task. 6. Return the requested finance output with evidence strength, missing data, QVeris capabilities used, paid Call count, estimated credits, and a not-investment-advice disclaimer. ## Output Contract Return these sections unless the user asks for a narrower format: - Objective and scope - Data sources discovered and inspected - Evidence table with source type, recency, and confidence - Analysis or ranking - Risks, dissenting evidence, and missing proof - QVeris calls used and estimated credits - Not investment advice The JSON artifact must include `catalyst_confidence_score`, `corroborating_roles`, `confirmation_roles`, role-level `missing_data`, evidence roles, paid-call usage, and traceable execution IDs. A catalyst can be labelled `confirmed_evidence_set` only when news, aggregate sentiment, price reaction, and either filings or issuer/company-news confirmation return usable evidence. ## Cost Guardrails - Discover and Inspect are treated as free preflight actions. - Paid actions are QVeris Call executions. - Provider fallback attempts are also paid actions and must remain inside `--max-paid-calls` and `--max-credits`; fallback attempts are recorded in the trace. - If estimated credits exceed the user's budget, reduce tickers, shorten windows, or ask for approval before continuing. ## Methodology Reference Read `references/methodology.md` when the user asks where the workflow comes from or how to adapt it. Read `references/source-review.md` for GitHub research and `references/qveris-tool-map.md` before changing QVeris data routing. ## Source Inspiration This skill is QVeris-native and does not copy source project text, prompts, code, or branding. It adapts public workflow patterns from permissively licensed finance AI projects: - AI4Finance-Foundation/FinGPT (MIT): Financial news, social sentiment, retrieval-augmented financial text analysis, and forecasting input patterns. - AI4Finance-Foundation/FinRobot (Apache-2.0): Equity research automation, report generation, multi-agent financial analysis, and risk assessment patterns. - cooragent/ClarityFinance (Apache-2.0): Claude-skill style financial workflow, planning-with-files, multi-market coverage, and screening/dashboard patterns.