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-stock-data-layergit 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-stock-data-layer)<a href="https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-a-stock-data-layer"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-a-stock-data-layer/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-stock-data-layer"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-a-stock-data-layer.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 | $0.00077 | $0.03145 |
| Opus 5.5 | $0.00031 | $0.01258 |
| Sonnet 5.5 | $0.00015 | $0.00629 |
| Haiku 4.5 | $0.00008 | $0.00314 |
Grade A, and why
qveris-a-stock-data-layer 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 2mo 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QVeris A-Stock Data Layer
Use this skill to preserve the A-Stock Data candidate's broad A-share data-layer intent while replacing legacy endpoints, scraping, and provider-specific dependencies with QVeris finance CAP calls plus a narrow audited Web lane for news and qualitative sentiment.
Source record:
| Field | Value |
|---|---|
| Candidate number | 58 |
| Original repository | A-Stock Data |
| GitHub URL | https://github.com/simonlin1212/a-stock-data |
| License | Apache-2.0 |
| Evaluation recent activity | 2026-06-28 |
| Local source snapshot | third_party/source_repos/58-a-stock-data |
| Snapshot latest commit | bcda405 on 2026-06-28 |
Source Adaptation
- Preserve the original ten-layer research/data intent: market data, research reports, signals, capital/ownership, news, fundamentals, filings, limit-up/limit-down, ETF options, and investor interaction.
- Replace the original embedded Python and direct source routing with QVeris CAP calls. The source repo's endpoints, headers, anti-ban rules,
mootdx,requests,stockstats, and optional iwencai key are migration context only. - Convert original valuation flows to evidence-gated trailing or consensus-input notes; do not output target prices, upside/downside, trade setups, or option strategies.
- Convert limit-up, LHB, unlock, capital-flow, ETF-option, hot-list, and investor-Q&A layers to conditional capabilities: call them only after current
cap-detailconfirms a QVeris finance CAP and matching fields. - Keep broad data-layer reports concise for users; put full call details and rejected payloads in the trace appendix.
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 evidence. - Use
references/qveris-finance-retry-policy.mdfor 5xx, fetch failures, 404s, payload truncation, and semantic mismatches. - Read
references/qveris-finance-capability-fallbacks.jsonbefore replacing a failed CAP. Use only a listed same-claim fallback, preserve its declared evidence downgrade, and keep unlisted capabilities missing. - Build trace, call counts, retries, and timestamps only from saved
observed_calls. Never invent an execution ID or a call that was only planned; 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. - Sanity-check entity, market, exchange, asset type, currency, date window, fiscal period, and payload shape before using data.
- Treat a successful transport response with wrong A-share identity, wrong date window, wrong fiscal period, or too-thin bars as rejected evidence.
- Suppress target prices, upside/downside, ratings, buy/sell wording, rebalancing instructions, and trade execution plans even if present in a QVeris payload.
- 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
25 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 378 B
- examples/default-markdown-report.md 2.4 KB
- examples/historical/2026-07-08-codex-clean.md 5.4 KB
- examples/historical/2026-07-08-codex-fresh.md 5.9 KB
- examples/historical/2026-07-08-natural-language.md 3.5 KB
- examples/live-e2e-output-2026-07-13.md 1.2 KB
- examples/live-e2e-output-2026-07-13.observed-calls.json 4.0 KB
- examples/natural-language-prompts.md 615 B
- examples/natural-language-test-output-2026-07-08.md 1.6 KB
- fixtures/qveris/budget-limited-output.json 1.4 KB
- fixtures/qveris/fallback-output.json 1.9 KB
- fixtures/qveris/sample-output.json 2.5 KB
- references/qveris-finance-cap-registry-snapshot-2026-07-07.md 10 KB
- references/qveris-finance-capability-fallbacks.json 4.2 KB
- references/qveris-finance-data-quality-rubric.md 14 KB
- references/qveris-finance-retry-policy.md 5.9 KB
- references/qveris-tool-map.md 7.1 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
- tests/business_adapter_copies.test.mjs 3.8 KB runs code
- tests/qveris_finance_adapter.test.mjs 38 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.
- 2mo ago First seen · 118 lines · 77 tokens per session scan A 2f1baf035f75
qveris-a-stock-data-layer is a skill published in the GitHub repository QVerisAI/open-qveris-skills (20 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 3,145 once invoked, about $0.0003 per session on Opus 5.5. 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.
qveris-a-stock-data-layer compared with skills from other repositories
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|---|---|---|---|---|---|---|---|
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