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 agentmods add skills/xingwudao/open-xquant/evaluate-factornpx skills add xingwudao/open-xquant --skill evaluate-factorgit clone --depth 1 https://github.com/xingwudao/open-xquantWrote 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/xingwudao/open-xquant/evaluate-factor)<a href="https://agentmods.dev/skills/xingwudao/open-xquant/evaluate-factor"><img src="https://agentmods.dev/badge/skills/xingwudao/open-xquant/evaluate-factor.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00333 |
| Opus 5 | $0.00014 | $0.00167 |
| Sonnet 5 | $0.00006 | $0.00067 |
| Haiku 4.5 | $0.00003 | $0.00033 |
Grade A, and why
evaluate-factor 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.
What it actually says
Factor Evaluator
You decide which factor evaluation workflow to use.
Ask First
Confirm:
- factor definition
- symbols
- date range
- forward return horizons
- whether the question is stock selection or timing
- data source and missing-data treatment
Route
Use agent/skills/evaluate-cross-sectional/SKILL.md when:
- the user ranks many assets on each date
- the goal is IC, Rank IC, ICIR, decay, or turnover
- there are enough symbols for cross-sectional statistics
Use agent/skills/evaluate-time-series/SKILL.md when:
- the user evaluates one asset or a small rotation set
- the question is directional timing
- hit rate, P/L ratio, decay curve, or tearsheet is more relevant
Rule of thumb:
- fewer than 10 symbols: avoid cross-sectional IC as primary evidence
- 10 to 30 symbols: use IC cautiously
- more than 30 symbols: cross-sectional IC is more defensible
Data Requirements
Build factor values and forward returns with aligned indexes. Do not let same-day execution leak into forward returns. For formal reports, state the horizon, date alignment, and excluded rows.
Red Lines
- Do not evaluate a factor without forward-return alignment.
- Do not run only one horizon when the user is making a research claim.
- Do not hide low sample size or high turnover.
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 · 54 lines · 28 tokens per session scan A 9320567db0c4
evaluate-factor is a skill published in the GitHub repository xingwudao/open-xquant (126 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 333 once invoked, about $0.0001 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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