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/ericrisco/rsc-harness/machine-learningnpx skills add ericrisco/rsc-harness --skill machine-learninggit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/machine-learning)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/machine-learning"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/machine-learning.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 | $0.00110 | $0.04220 |
| Opus 5 | $0.00055 | $0.02110 |
| Sonnet 5 | $0.00022 | $0.00844 |
| Haiku 4.5 | $0.00011 | $0.00422 |
Grade A, and why
machine-learning 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 yesterday.
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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Machine learning — classic/tabular models, done without lying to yourself
Tabular ML is easy to run and easy to fool yourself with. The deliverable is never "the notebook
printed 0.99" — it is an honest estimate of how the model behaves on data it has never seen: a
Pipeline that fits every transform on train only, a metric that survives class imbalance, and a
DummyClassifier baseline it beats. A number you can't reproduce on a sacred test set you touched exactly
once isn't a result — it's a leak you haven't found yet.
Is this the right skill? (decide first)
| Your situation | Reach for |
|---|---|
| Rows of features, predict a column, with trees / linear models / sklearn | machine-learning (this skill) |
| Images, audio, long text, sequences, or you need a neural net / PyTorch | deep-learning |
| The table is still dirty (nulls, dupes, mixed types, bad dates) | data-cleaning first — it hands you a validated table |
| Text/token classification, NER, tokenization, LLM-adjacent NLP metrics | nlp (a TF-IDF + linear/GBDT baseline still lives happily in this skill's pipeline) |
| KPIs, dashboards, "explain the business" | analytics / business-intelligence |
| Forecast a dated series forward (revenue next quarter) | forecasting |
| Building a training corpus of JSONL messages / preference pairs for an LLM | training-data |
This skill starts at a clean, validated table (rows × features + a target) and ends at a fitted,
honestly-scored model with a test-set number and a baseline it beats. Cleaning is upstream — consume the
validated frame data-cleaning produced; don't re-teach it here.
Version reality (verify at author time — this line moves monthly)
Verified 2026-07: scikit-learn current major ~1.9 (1.9.0 shipped 2026-06-02, Python 3.11–3.14) — do
NOT pin from memory; check the current stable at scikit-learn.org, the 1.x line ships every few months.
GBDTs: XGBoost 3.x and LightGBM 4.x (xgboost 3.3, lightgbm 4.6 current), plus sklearn's own
HistGradientBoostingClassifier/...Regressor — a fast native GBDT that eats NaN and (with
categorical_features="from_dtype") categoricals with no preprocessing. Pin what you ship
(python owns the environment and the pinning); state versions as "~X (verify)",
never as frozen fact.
What ships with it
5 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.
- yesterday First seen · 229 lines · 110 tokens per session scan A 30d0ff166a69
machine-learning is a skill published in the GitHub repository ericrisco/rsc-harness (64 stars, last pushed 2d ago), licensed MIT. It adds 110 tokens to every session and 4,220 once invoked, about $0.0006 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 2026-09-03.
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