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-macro-policy-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-macro-policy-monitor)<a href="https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-macro-policy-monitor"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-macro-policy-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-macro-policy-monitor"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-macro-policy-monitor.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 | $0.00096 | $0.01480 |
| Opus 5 | $0.00048 | $0.00740 |
| Sonnet 5 | $0.00019 | $0.00296 |
| Haiku 4.5 | $0.00010 | $0.00148 |
Grade A, and why
qveris-macro-policy-monitor 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QVeris Macro Policy Monitor
Build an evidence-first macro and rates monitor adapted from LLMQuant's macro dashboard and policy-preview taxonomy. Use only standardized qveris_finance.* CAPs; never call upstream APIs or raw provider routes.
Execute In Five Gates
1. Fix Scope
Require an explicit geography. Select one workflow:
macro_policy: broad macro, policy/rates, optional FX and index context.growth_inflation: macro indicators, employment, real estate, and commodities.rates_fx: policy, government/interbank rate proxies, FX, and index context.
Fix an as_of time and UTC observation window. Treat data dates as observation dates unless the payload explicitly identifies release or revision dates.
2. Plan And Enforce Budget
Run the bundled planner before transport:
node qveris-macro-policy-monitor/scripts/macro_policy_workflow.mjs --workflow macro_policy --geography US --max-calls 9 --dry-run
max_calls counts observed capabilities/query attempts, including retries. Catalog/detail reads are control-plane operations. The first macro and rates anchors run before optional sublayers. If both anchors do not fit, make zero data calls and return budget_limited.
Never plan or call:
EVENT.CALENDAR.MACRO: test payload omitted its required date.MACRO.ACTUAL_VS_FORECAST: test environment returned invalid capability/404.
3. Execute Through The Shared Adapter
Prefer the bundled runner:
node qveris-macro-policy-monitor/scripts/macro_policy_workflow.mjs --workflow macro_policy --geography US --max-calls 9 --artifact macro.observed-calls.json --output macro.json
The runner reads the live capability catalog and detail, dynamically resolves CAP IDs, filters unsupported parameters, coerces supported types, retries one transient fetch failed within budget, recursively removes routing metadata, and records exact observed attempts. Use only QVERIS_API_KEY.
4. Validate Evidence And Claims
Read references/qveris-macro-data-quality.md before interpretation and references/qveris-tool-map.md before changing the plan.
What ships with it
16 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 265 B
- examples/default-markdown-report.md 890 B
- examples/live-e2e-output-2026-07-24.md 5.2 KB
- examples/live-e2e-output-2026-07-24.observed-calls.json 170 KB
- examples/natural-language-prompts.md 560 B
- fixtures/qveris/budget-limited-output.json 1.8 KB
- fixtures/qveris/fallback-output.json 1.8 KB
- fixtures/qveris/sample-output.json 1.7 KB
- references/qveris-macro-data-quality.md 2.7 KB
- references/qveris-retry-policy.md 1.3 KB
- references/qveris-tool-map.md 2.1 KB
- schemas/output.schema.json 6.1 KB
- scripts/macro_policy_evidence.mjs 16 KB runs code
- scripts/macro_policy_workflow.mjs 16 KB runs code
- tests/macro_policy_workflow.test.mjs 8.8 KB runs code
- THIRD_PARTY_NOTICES.md 1.3 KB
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 · 122 lines · 96 tokens per session scan A 5b585e9e4581
qveris-macro-policy-monitor is a skill published in the GitHub repository QVerisAI/open-qveris-skills (21 stars, last pushed 8d ago), licensed MIT. It adds 96 tokens to every session and 1,480 once invoked, about $0.0005 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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