qveris-a-share-factor-screen

qveris-a-share-factor-screen is a skill for Codex from QVerisAI/open-qveris-skills. It costs 69 tokens per session (2,523 once invoked), scanned A, original, MIT.

A research-screening skill for China’s A-share stock market, which is the mainland Chinese market for publicly traded shares. It uses QVeris data and records factor scores, filters, evidence, risks, and later results without giving investment advice.

In plain words
What is it for?
Use it to filter Chinese stocks, score candidates, review strategy screens, save research runs, create reports, and evaluate results after a chosen period.
Why use it?
It makes stock-screening decisions traceable by showing the data and evidence behind each candidate and by recording missing or unreliable information.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to filter Chinese stocks, score candidates, review strategy screens, save research runs, create reports, and evaluate results after a chosen period.

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Install with agentmods
npx agentmods add skills/qverisai/open-qveris-skills/qveris-a-share-factor-screen
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-factor-screen
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-factor-screen

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-a-share-factor-screen"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-a-share-factor-screen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,523 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 $0.00069 $0.02523
Opus 5 $0.00034 $0.01262
Sonnet 5 $0.00014 $0.00505
Haiku 4.5 $0.00007 $0.00252

Measured 12d ago against content hash 2bfc93c19298, 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-factor-screen 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 12d ago.

The scan reads SKILL.md. This mod also ships 4 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-factor-screen/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QVeris A-Share Factor Screen

Use this skill to preserve Alphasift's research-screening workflow while replacing local market data packages, model-provider assumptions, and stock-picking language with QVeris structured-data evidence, audited Web news/sentiment evidence, and auditable factor notes.

Source record:

Field Value
Candidate number 32
Original repository Alphasift
GitHub URL https://github.com/ZhuLinsen/alphasift
License Apache-2.0
Evaluation recent activity 2026-07-03
Local source snapshot third_party/source_repos/32-alphasift
Snapshot latest commit 9f52274 on 2026-07-03

Source Adaptation

  • Preserve Alphasift's core shape: strategy catalog, screen, hard filters, factor scoring, risk/source-health fields, saved-run metadata, reports, and T+N post-hoc evaluation.
  • Replace original data packages and provider paths (efinance, akshare, baostock, tushare, yfinance, HTTP source fallbacks) with QVeris CAP evidence.
  • Remove operational LLM-provider requirements and external analyzers from the runtime contract. Original litellm, DSA, and deep-analysis fields are migration context only.
  • Suppress or rename fields that imply actions, such as operation_advice, invalidators used as trading instructions, buy/sell wording, target prices, and position actions.
  • Treat strategy output as a transparent research candidate pool. Only output a rank when the same factor set, price window, fiscal period, and market convention are comparable across at least 3 securities.

Runtime Contract

  • Use only qveris_finance.* CAP tools and QVERIS_API_KEY.
  • 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, and budget_note; if omitted, default to dry_run=false, no hard max_calls limit, max_age=P1D, and a conservative budget note, then echo those controls.
  • Read references/qveris-finance-data-quality-rubric.md before using QVeris payloads as factor evidence.
  • 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, retry, timestamp, per-security call, or result from the intended workflow; use execution_id=null when an observed call returned no ID.
  • 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.
  • Treat screening output as a research candidate pool, not as investment advice or an action list.
  • Suppress target prices, upside/downside, ratings, buy/sell wording, rebalancing instructions, and trade execution plans even if present in QVeris payloads.
  • Read and follow references/qveris-web-news-sentiment-policy.md. Never call qveris_finance.news_fin_tagged or qveris_finance.sentiment_text_signals; use its audited Web lane in every run mode, including benchmark and replay.

Read the full file on GitHub · 110 lines

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. 12d ago First seen · 110 lines · 69 tokens per session scan A 2bfc93c19298

Subscribe to this mod's changes

qveris-a-share-factor-screen is a skill published in the GitHub repository QVerisAI/open-qveris-skills (21 stars, last pushed 8d ago), licensed MIT. It adds 69 tokens to every session and 2,523 once invoked, about $0.0003 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.

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