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 minihellboy/factorminer --skill factor-evaluationgit clone --depth 1 https://github.com/minihellboy/factorminerWrote 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/minihellboy/factorminer/factor-evaluation)<a href="https://agentmods.dev/skills/minihellboy/factorminer/factor-evaluation"><img src="https://agentmods.dev/badge/skills/minihellboy/factorminer/factor-evaluation/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/minihellboy/factorminer/factor-evaluation"><img src="https://agentmods.dev/badge/skills/minihellboy/factorminer/factor-evaluation.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 | $0.00090 | $0.00596 |
| Opus 5.5 | $0.00036 | $0.00238 |
| Sonnet 5.5 | $0.00018 | $0.00119 |
| Haiku 4.5 | $0.00009 | $0.00060 |
Grade A, and why
factor-evaluation 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.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Factor Evaluation
Mining proposes factors; evaluation decides whether to believe them. This skill recomputes a library's metrics on a chosen split and exposes overfitting.
See references/metrics.md for precise metric definitions (IC vs. paper-IC, ICIR, redundancy correlation).
Workflow
1. Recompute metrics
factorminer evaluate output/run1/factor_library.json \
--data path/to/market_data.csv \
--period test
--period selects the split: train, test, or both. Always lead with test — in-sample IC is not evidence.
2. Read the table
The output table reports, per factor: IC Mean, Paper IC, Abs IC, Paper ICIR, Win%, and Turnover. The summary block gives library-level means and the IC range.
3. Check decay
factorminer evaluate output/run1/factor_library.json --data market_data.csv --period both
--period both adds a decay table (train Paper IC → test Paper IC → delta). A large negative delta is the signature of an overfit factor. Report decay honestly; do not quote the train number as the headline.
4. Rank the survivors
To shortlist the strongest signals only:
factorminer evaluate output/run1/factor_library.json --data market_data.csv --period test --top-k 10
The top-K-by-IC table is the signal shortlist — the natural handoff to a research-idea workflow that wants to know which quantitative signals are currently working. The MCP screen_factors tool returns this same shortlist directly.
Interpreting the numbers
- IC ≈ 0.03–0.05 out of sample is a respectable single factor on liquid universes.
- ICIR matters more than IC: a small but stable IC beats a large erratic one.
- High turnover quietly erases IC once costs are applied — carry it into
factor-backtest.
Guardrails
- Never present
trainmetrics as the result. The deliverable is thetestnumber. - If every factor decays to ~0 on test, the library failed — say so. Do not search for a split that flatters it.
What ships with it
1 file 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.
- 1mo ago First seen · 56 lines · 90 tokens per session scan A f3be4331109c
factor-evaluation is a skill published in the GitHub repository minihellboy/factorminer (123 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 596 once invoked, about $0.0004 per session on Opus 5.5. 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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