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 agentii-ai/agentii-investment-intelligence --skill quantitative-screeninggit clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligenceWrote 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/agentii-ai/agentii-investment-intelligence/quantitative-screening)<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/quantitative-screening"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/quantitative-screening/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/agentii-ai/agentii-investment-intelligence/quantitative-screening"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/quantitative-screening.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 | $0.00042 | $0.01714 |
| Opus 5 | $0.00021 | $0.00857 |
| Sonnet 5 | $0.00008 | $0.00343 |
| Haiku 4.5 | $0.00004 | $0.00171 |
Grade A, and why
quantitative-screening 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 5d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Methodology fused from professional trading and investment frameworks; all text is an original paraphrase.
Defaults
| Parameter | Default Value | Rationale |
|---|---|---|
| screening_universe | S&P 500 + Russell 1000 liquid | Broad enough for diversity, liquid enough for execution |
| historical_years | 5 | Minimum years of financial data for trend analysis |
| peg_threshold | 1.0 | PEG < 1.0 suggests undervaluation relative to growth |
| fcf_conversion_min | 70% | FCF/Net Income below 70% flags earnings quality issues |
| earnings_beat_threshold | 70% | Beat frequency above 70% suggests conservative guidance |
Preflight
Run canonical pre-flight per contracts/preflight.md. Propagate X-Agentii-Trace per contracts/x-agentii-trace-header.md.
Data Source Priority
- Quantitative methodology —
references/quant-methodology.md(bundled screening framework) - Financial data — SEC XBRL facts via agentii MCP for historical financials
- Market data —
~~market_dataplaceholder for real-time valuation multiples - Strategy frameworks —
search_investment_strategies(domain=fundamental, kind=screening)
Methodology
Retrieval Scope
structured_only
Retrieval Strategy
Ownership & insider signals: search_institutional_holdings (top-10 holders + whale portfolios, direction=accumulating|reducing|new|exited) and search_insider_trades (Form-4 transactions with SEC URLs) are available as signal inputs.
Branch (a) Structured Data Query from contracts/retrieval.md: primary retrieval via XBRL facts for financial statement data. Supplement with search_investment_strategies for screening methodology validation. Detailed methodology in references/quant-methodology.md.
Temporal Scope
See frontmatter temporal_scope block.
Tool Allowlist
See frontmatter allowed_tools.
Protocol
This skill implements a two-directional screening process: forward-looking valuation discovery and backward-looking financial statement validation. Core principle: the market is mostly efficient. An outlier exists because either the market is wrong (your edge) or you are missing something. Non-participation is always an option.
What ships with it
3 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.
- 5d ago First seen · 130 lines · 42 tokens per session scan A 49ae234b65e4
quantitative-screening is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (203 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 1,714 once invoked, about $0.0002 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 2026-09-05.
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