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-tech-earnings-deepdivegit 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-tech-earnings-deepdive)<a href="https://agentmods.dev/skills/qverisai/open-qveris-skills/qveris-tech-earnings-deepdive"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-tech-earnings-deepdive/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-tech-earnings-deepdive"><img src="https://agentmods.dev/badge/skills/qverisai/open-qveris-skills/qveris-tech-earnings-deepdive.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 | $0.00055 | $0.01030 |
| Opus 5 | $0.00028 | $0.00515 |
| Sonnet 5 | $0.00011 | $0.00206 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
qveris-tech-earnings-deepdive 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QVeris Tech Earnings Deepdive
Use this skill for technology-company earnings deep dives adapted from Tech Earnings Deepdive. Preserve the multi-perspective memo shape, but convert subjective or investment-action language into evidence, scenarios, uncertainty, and verification steps backed by QVeris CAP tools.
Source record:
| Field | Value |
|---|---|
| Candidate number | 6 |
| Original repository | Tech Earnings Deepdive |
| GitHub URL | https://github.com/webleon/tech-earnings-deepdive-openclaw-skill |
| License | MIT |
| Evaluation recent activity | 2026-03-24 |
| Local source snapshot | third_party/source_repos/06-tech-earnings-deepdive |
| Snapshot latest commit | 5bff060 on 2026-03-24 |
Runtime Contract
- Use only
qveris_finance.*CAP tools andQVERIS_API_KEY. - Resolve entities with
ref_symbology,ref_security_master, andref_company_profile. - Accept
dry_run,max_calls,max_age, andbudget_note; if omitted in a natural-language request, default todry_run=false,max_calls=12,max_age=P1D, and a conservative budget note, then echo those controls. - Every thesis, counter-thesis, segment trend, management quote, and reaction datapoint must include
qveris_trace. - Show
missing_fieldsand confidence; do not infer missing competitive or segment data as fact. - Treat QVeris
_meta.source_provideras provenance only; never call, request credentials for, or depend on those internal providers directly. - Suppress
analyst_target_price,target_price, price-objective, upside, buy/sell, and recommendation fields even if a QVeris payload contains them. - Sanity-check entity, market, date window, fiscal period, and payload shape before using data; if a payload is stale, cross-period, truncated, or semantically mismatched, mark it in
data_qualityandmissing_fields.
Workflows
- Tech earnings deep dive:
earnings_actual_surprise,fundamentals_segment,estimates_consensus,transcripts_earnings_call,news_fin_tagged. - Competition/moat:
ref_classification_theme,research_analyst_reports,alt_patents,alt_job_postings,alt_supply_chain. - Valuation/reaction:
mkt_l1_rt,mkt_bars_intraday,mkt_after_hours,fundamentals_derived_ratios.
What ships with it
4 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.
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 · 63 lines · 55 tokens per session scan A 7d726239cd74
qveris-tech-earnings-deepdive is a skill published in the GitHub repository QVerisAI/open-qveris-skills (21 stars, last pushed 8d ago), licensed MIT. It adds 55 tokens to every session and 1,030 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.
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