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.
npx skills add QVerisAI/open-qveris-skills --skill qveris-a-share-factor-screengit clone --depth 1 https://github.com/QVerisAI/open-qveris-skillsWrote 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.
[](https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-a-share-factor-screen)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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.
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 andQVERIS_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/querydirectly from the workflow. - Default natural-language output to a Markdown user report, not a JSON object.
- Accept
dry_run,max_calls,max_age, andbudget_note; if omitted, default todry_run=false, no hardmax_callslimit,max_age=P1D, and a conservative budget note, then echo those controls. - Read
references/qveris-finance-data-quality-rubric.mdbefore using QVeris payloads as factor evidence. - Use
references/qveris-finance-retry-policy.mdfor 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; useexecution_id=nullwhen 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, andmissing_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 callqveris_finance.news_fin_taggedorqveris_finance.sentiment_text_signals; use its audited Web lane in every run mode, including benchmark and replay.
What ships with it
22 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.
- agents/openai.yaml 400 B
- examples/default-markdown-report.md 2.3 KB
- examples/historical/2026-07-08-codex-clean.md 5.0 KB
- examples/historical/2026-07-08-codex-fresh.md 4.0 KB
- examples/historical/2026-07-08-natural-language.md 2.6 KB
- examples/live-e2e-output-2026-07-13.md 1.2 KB
- examples/live-e2e-output-2026-07-13.observed-calls.json 3.9 KB
- examples/natural-language-prompts.md 651 B
- examples/natural-language-test-output-2026-07-08.md 1.7 KB
- fixtures/qveris/budget-limited-output.json 1.5 KB
- fixtures/qveris/fallback-output.json 1.9 KB
- fixtures/qveris/sample-output.json 3.0 KB
- references/qveris-finance-cap-registry-snapshot-2026-07-07.md 10 KB
- references/qveris-finance-data-quality-rubric.md 14 KB
- references/qveris-finance-retry-policy.md 5.9 KB
- references/qveris-tool-map.md 5.4 KB
- references/qveris-web-news-sentiment-policy.md 3.3 KB
- schemas/output.schema.json 5.4 KB
- scripts/qveris_finance_adapter.mjs 48 KB runs code
- scripts/qveris_finance_client.mjs 3.0 KB runs code
- scripts/qveris_finance_tool.mjs 5.0 KB runs code
- scripts/qveris_sanitize.mjs 3.2 KB runs code
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.
- 12d ago First seen · 110 lines · 69 tokens per session scan A 2bfc93c19298
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.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.
reading-receipt
An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.