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-quant-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-quant-factor-screen)<a href="https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-quant-factor-screen"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-quant-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-quant-factor-screen"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-quant-factor-screen.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 | $0.00066 | $0.01304 |
| Opus 5 | $0.00033 | $0.00652 |
| Sonnet 5 | $0.00013 | $0.00261 |
| Haiku 4.5 | $0.00007 | $0.00130 |
Grade A, and why
qveris-quant-factor-screen 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
84% identical to qveris-news-sentiment-radar — 17 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quant factor screen
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.
If the user has not authorized paid QVeris calls, stop after dry-run/preflight or ask for approval. If the user authorizes QVeris spend, run live and stay within the stated budget.
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 722 B
- agents/openai.yaml 370 B
- artifacts/codex-cli-live-output.json 2.6 KB
- artifacts/codex-cli-live-trace.json 58 KB
- artifacts/codex-cli-live.md 1.5 KB
- artifacts/codex-cli-natural-e2e-20260703-final.txt 1.4 KB
- artifacts/codex-cli-natural-e2e-20260703-output.json 6.4 KB
- artifacts/codex-cli-natural-e2e-20260703-trace.json 65 KB
- artifacts/codex-cli-natural-e2e-20260703.md 2.0 KB
- artifacts/codex-e2e.md 1.1 KB
- artifacts/dry-run-trace.json 54 KB
- artifacts/dry-run.md 1.1 KB
- artifacts/fixture-output.json 7.1 KB
- artifacts/hardening-live-20260703-output.json 5.2 KB
- artifacts/hardening-live-20260703-trace.json 58 KB
- artifacts/hardening-live-20260703.md 1.5 KB
- artifacts/live-smoke-trace.json 55 KB
- artifacts/live-smoke.md 1.5 KB
- artifacts/openclaw-natural-e2e-20260703-final.txt 4.1 KB
- artifacts/openclaw-natural-e2e-20260703-output.json 6.4 KB
- artifacts/openclaw-natural-e2e-20260703-trace.json 65 KB
- artifacts/openclaw-natural-e2e-20260703.md 2.0 KB
- artifacts/repair-live-20260703-v4-output.json 6.4 KB
- artifacts/repair-live-20260703-v4-trace.json 65 KB
- artifacts/repair-live-20260703-v4.md 2.0 KB
- artifacts/scenario-02-semis-output.json 2.6 KB
- artifacts/scenario-02-semis-trace.json 58 KB
- artifacts/scenario-02-semis.md 1.5 KB
- artifacts/scenario-03-defensive-quality-output.json 2.6 KB
- artifacts/scenario-03-defensive-quality-trace.json 58 KB
- artifacts/scenario-03-defensive-quality.md 1.5 KB
- examples/README.md 1.6 KB
- fixtures/missing-data-factor-screen.json 1.1 KB
- fixtures/normal-factor-screen.json 5.0 KB
- qveris.skill.json 11 KB
- references/methodology.md 1.2 KB
- references/qveris-tool-map.md 7.0 KB
- references/scenario-review.md 3.4 KB
- references/source-review.md 3.3 KB
- schemas/output.schema.json 2.4 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 16 KB runs code
- tests/runner.fixture.test.mjs 5.5 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 · 83 lines · 66 tokens per session scan A 126e456b7279
qveris-quant-factor-screen is a skill published in the GitHub repository QVerisAI/open-qveris-skills (21 stars, last pushed 7d ago), licensed MIT. It adds 66 tokens to every session and 1,304 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 84% identical to qveris-news-sentiment-radar, differing in 17 lines, and is treated as a copy.
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