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 skloxo/TideTrading --skill factor-researchgit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/factor-research)<a href="https://agentmods.dev/skills/skloxo/tidetrading/factor-research"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/factor-research/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/skloxo/tidetrading/factor-research"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/factor-research.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 | $0.00032 | $0.01766 |
| Opus 5.5 | $0.00013 | $0.00706 |
| Sonnet 5.5 | $0.00006 | $0.00353 |
| Haiku 4.5 | $0.00003 | $0.00177 |
Grade A, and why
factor-research 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 1mo 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.
This is a copy
88% identical to factor-research — 73 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Factor Research Framework
Purpose
Systematically evaluates the predictive power of single or multiple factors. Uses IC/IR statistical tests and quantile backtests to determine whether a factor has stock-selection power, and to guide factor screening and combination.
Applicable scenarios:
- Single-factor validity testing (momentum, value, quality, volatility, and more)
- Determining weights for multi-factor combination
- Factor decay analysis (IC changes across different holding periods)
- Comparing factor differences across industries and markets
Workflow
- Calculate factor values: compute factor exposures for each instrument on the cross-section, and output a factor CSV (
index=date,columns=codes) - Calculate returns: compute each instrument's forward N-day return, and output a return CSV (same structure)
- Call the
factor_analysistool: pass in the factor CSV, return CSV, and output directory - Interpret the results: judge factor validity based on IC/IR criteria and quantile backtest results
- Factor screening / combination: keep effective factors and combine them with equal weights or IC-based weights
Key point: the rows (dates) and columns (instrument codes) of the factor CSV and return CSV must align exactly. Returns must be forward returns after the factor-observation date (to avoid look-ahead bias).
factor_analysis Tool Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| factor_csv | string | Yes | - | Path to the factor-value CSV |
| return_csv | string | Yes | - | Path to the return CSV |
| output_dir | string | Yes | - | Output directory for results |
| n_groups | integer | No | 5 | Number of quantile groups |
Output Files
| File | Contents |
|---|---|
| ic_series.csv | Daily IC series |
| ic_summary.json | IC mean, IC standard deviation, IR, proportion of IC > 0 |
| group_equity.csv | Cumulative equity curves for each quantile group |
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.
- 1mo ago First seen · 157 lines · 32 tokens per session scan A df5e90c3fb39
factor-research is a skill published in the GitHub repository skloxo/TideTrading (13 stars, last pushed 9d ago), licensed MIT. It adds 32 tokens to every session and 1,766 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to factor-research, differing in 73 lines, and is treated as a copy.
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