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qveris-a-share-data

qveris-a-share-data is a skill for Codex from QVerisAI/open-qveris-skills. It costs 89 tokens per session (3,638 once invoked), scanned A, original, MIT.

A finance-data workflow for China A-shares, which are shares of companies listed on mainland Chinese stock exchanges, using QVeris data and limited web searches for news.

In plain words
What is it for?
Use it for Chinese stock quotes, historical prices, technical indicators, company events, sector information, market news, A+H listings, and A-share-to-Hong-Kong IPO timelines.
Why use it?
It replaces separate market-data packages and source-specific scripts with one structured data approach, while making data quality and evidence explicit.

Skill for Codex

Written for Codex: agents/openai.yaml present.

not rated 21repo 8d ago A scan SkillSpector: pass 89 tokens original MIT

Good fit Use it for Chinese stock quotes, historical prices, technical indicators, company events, sector information, market news, A+H listings, and A-share-to-Hong-Kong IPO timelines.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/qverisai/open-qveris-skills/qveris-a-share-data
View source ↗ QVerisAI/open-qveris-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add QVerisAI/open-qveris-skills --skill qveris-a-share-data
Clone the repo
git clone --depth 1 https://github.com/QVerisAI/open-qveris-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for qveris-a-share-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-a-share-data/github.svg)](https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-a-share-data)
Your own site
<a href="https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-a-share-data"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-a-share-data/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for qveris-a-share-data

Your own site · 80×15
<a href="https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-a-share-data"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-a-share-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,638 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00089 $0.03638
Opus 5 $0.00044 $0.01819
Sonnet 5 $0.00018 $0.00728
Haiku 4.5 $0.00009 $0.00364

Measured 13d ago against content hash 357727fd7b1d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

qveris-a-share-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

The scan reads SKILL.md. This mod also ships 8 executable files (scripts/qveris_finance_adapter.mjs, scripts/qveris_finance_client.mjs, scripts/qveris_finance_tool.mjs, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

qveris-a-share-data/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QVeris A-Share Data

Use this skill to preserve the A-Share Skill candidate's A-share research-data workflows while replacing local market packages, source-specific scripts, and short-term trading modules with QVeris structured-data evidence, a narrow audited Web Search lane for news and qualitative sentiment, and explicit data-quality controls.

Source record:

Field Value
Candidate number 57
Original repository A-Share Skill
GitHub URL https://github.com/shouldnotappearcalm/a-share-skill
License MIT
Evaluation recent activity 2026-06-24
Local source snapshot third_party/source_repos/57-a-share-skill
Snapshot latest commit 8494623 on 2026-06-24

Source Adaptation

  • Preserve the original a-share-data workflows: real-time quote, historical bars, technical indicators, corporate events, A+H list, A-to-HK IPO timeline, hot industry/concept reads, market news, and sector info.
  • Replace original Python scripts and dependencies (akshare, MyTT, pandas, numpy, requests) with QVeris CAP calls plus calculated indicators from validated QVeris bars. Use Web Search only for the temporary news and qualitative-sentiment lane defined below.
  • Treat original source fallbacks such as direct market HTTP endpoints, sector APIs, DangInvest, and source-specific caches as migration context only.
  • Remove the repository's trading modules from this QVeris skill: short-line trading, MACD trading plans, paper trading, accounts, orders, backtests, position discipline, stop-loss, and entry/exit rules.
  • Keep technical indicators descriptive. Do not turn MACD, RSI, MA, or BOLL into a buy/sell/position signal.

Runtime Contract

  • Use only qveris_finance.* CAP tools and QVERIS_API_KEY for identity, quotes, bars, classifications, and events. Web Search is allowed only for issuer news and qualitative sentiment under the Web News And Sentiment Lane.
  • Execute every finance data call through this Skill's scripts/qveris_finance_adapter.mjs, or through a native wrapper that runs the byte-identical adapter; never call /capabilities/query directly from the workflow.
  • Default natural-language output to a Markdown user report, not a JSON object.
  • Accept dry_run, max_calls, max_age, budget_note, and source_mode; if omitted, default to dry_run=false, no hard max_calls limit, max_age=P1D, a conservative budget note, and source_mode=hybrid_web_news_sentiment, then echo those controls. Benchmark and replay runs use the same narrow Web lane for news and qualitative sentiment; replay reads only frozen Web evidence.
  • Read and follow references/qveris-web-news-sentiment-policy.md. It supersedes any older news/sentiment fallback text in this Skill.
  • Read references/qveris-finance-data-quality-rubric.md before using QVeris payloads as evidence.
  • Read references/qveris-workflow-semantic-guards.md before any derived, comparative, ranking, sentiment, or multi-layer workflow. Use scripts/qveris_workflow_guards.mjs for the applicable pre-prose gates.
  • Use references/qveris-finance-retry-policy.md for failed calls, invalid capabilities, payload truncation, and semantic mismatches.
  • Build trace, call counts, retries, and timestamps only from saved observed_calls. Never invent an execution ID or planned call; use execution_id=null when an observed call returned no ID.
  • Record every Web Search and opened page used for news or sentiment with query, final URL, publisher, publication time, access time, issuer-match result, window-match result, and body-content SHA-256. Search-result snippets alone are not evidence.
  • Sanitize every output surface, including Evidence, Sources, prose, params, responses, and Trace. Strip provider names, provider API URLs, raw route/tool IDs, candidates, failover, credentials, and routing metadata recursively; the Trace row remains exactly tool_name, params, status, execution_id, fallback_used, and missing_fields.
  • Strip the original candidate's short-term trading and paper-trading behavior. This skill only supports research data reads.
  • Suppress target prices, upside/downside, ratings, buy/sell wording, rebalancing instructions, and trade execution plans even if present in QVeris payloads.

Read the full file on GitHub · 128 lines

Files

What ships with it

27 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 128 lines · 89 tokens per session scan A 357727fd7b1d

Subscribe to this mod's changes

qveris-a-share-data is a skill published in the GitHub repository QVerisAI/open-qveris-skills (21 stars, last pushed 8d ago), licensed MIT. It adds 89 tokens to every session and 3,638 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Analytics and metricsFinance and fintech
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