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-portfolio-risk-monitorgit 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-portfolio-risk-monitor)<a href="https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-portfolio-risk-monitor"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-portfolio-risk-monitor/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-portfolio-risk-monitor"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-portfolio-risk-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00068 | $0.01306 |
| Opus 5 | $0.00034 | $0.00653 |
| Sonnet 5 | $0.00014 | $0.00261 |
| Haiku 4.5 | $0.00007 | $0.00131 |
Grade A, and why
qveris-portfolio-risk-monitor scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
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 sk This is a copy
89% identical to qveris-news-sentiment-radar — 18 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portfolio risk monitor
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.
What ships with it
45 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.
- agent.md 792 B
- agents/openai.yaml 372 B
- artifacts/codex-cli-live-output.json 2.5 KB
- artifacts/codex-cli-live-trace.json 39 KB
- artifacts/codex-cli-live.md 1.6 KB
- artifacts/codex-cli-natural-e2e-20260703-final.txt 1.5 KB
- artifacts/codex-cli-natural-e2e-20260703-output.json 3.8 KB
- artifacts/codex-cli-natural-e2e-20260703-trace.json 56 KB
- artifacts/codex-cli-natural-e2e-20260703.md 1.9 KB
- artifacts/codex-e2e.md 1.1 KB
- artifacts/dry-run-trace.json 39 KB
- artifacts/dry-run.md 1.3 KB
- artifacts/fixture-output.json 3.4 KB
- artifacts/hardening-live-20260703-output.json 3.3 KB
- artifacts/hardening-live-20260703-trace.json 55 KB
- artifacts/hardening-live-20260703.md 1.8 KB
- artifacts/live-smoke-trace.json 39 KB
- artifacts/live-smoke.md 1.6 KB
- artifacts/openclaw-natural-e2e-20260703-final.txt 3.6 KB
- artifacts/openclaw-natural-e2e-20260703-output.json 3.8 KB
- artifacts/openclaw-natural-e2e-20260703-trace.json 56 KB
- artifacts/openclaw-natural-e2e-20260703.md 1.9 KB
- artifacts/repair-live-20260703-output.json 3.8 KB
- artifacts/repair-live-20260703-trace.json 56 KB
- artifacts/repair-live-20260703.md 1.9 KB
- artifacts/scenario-02-concentrated-nvda-output.json 2.3 KB
- artifacts/scenario-02-concentrated-nvda-trace.json 43 KB
- artifacts/scenario-02-concentrated-nvda.md 1.4 KB
- artifacts/scenario-03-defensive-stocks-output.json 2.4 KB
- artifacts/scenario-03-defensive-stocks-trace.json 43 KB
- artifacts/scenario-03-defensive-stocks.md 1.5 KB
- examples/README.md 1.8 KB
- fixtures/missing-data-portfolio-risk.json 1.3 KB
- fixtures/normal-portfolio-risk.json 1.9 KB
- qveris.skill.json 10 KB
- references/methodology.md 1.2 KB
- references/qveris-tool-map.md 7.4 KB
- references/scenario-review.md 3.2 KB
- references/source-review.md 3.5 KB
- schemas/output.schema.json 5.4 KB
- scripts/lib/fixture-loader.mjs 141 B runs code
- scripts/lib/qveris-runtime.mjs 20 KB runs code
- scripts/lib/schema-validator.mjs 1.8 KB runs code
- scripts/run.mjs 20 KB runs code
- tests/runner.fixture.test.mjs 4.6 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 · 82 lines · 68 tokens per session scan A acc1c860350d
qveris-portfolio-risk-monitor is a skill published in the GitHub repository QVerisAI/open-qveris-skills (21 stars, last pushed 8d ago), licensed MIT. It adds 68 tokens to every session and 1,306 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 89% identical to qveris-news-sentiment-radar, differing in 18 lines, and is treated as a copy.
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.