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 ihatesea69/kiro-kit --skill feature-storegit clone --depth 1 https://github.com/ihatesea69/kiro-kitWrote 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/ihatesea69/kiro-kit/feature-store)<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/feature-store"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/feature-store/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/ihatesea69/kiro-kit/feature-store"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/feature-store.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 | $0.00030 | $0.00294 |
| Opus 5 | $0.00015 | $0.00147 |
| Sonnet 5 | $0.00006 | $0.00059 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
feature-store 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
Feature Store
Activate this skill when working with feature engineering at scale.
When to Use
- Building reusable feature computation pipelines
- Managing feature versioning and lineage
- Serving features for online inference
- Sharing features across ML models
- Ensuring training-serving consistency
Tools
- Feast: Open-source feature store
- Hopsworks: Full-featured platform
- Custom: pandas + SQL + caching
Patterns
from feast import FeatureStore, Entity, FeatureView
store = FeatureStore(repo_path="feature_repo/")
# Define features
user_features = FeatureView(
name="user_features",
entities=[user_entity],
schema=[
Field(name="total_purchases", dtype=Int64),
Field(name="avg_order_value", dtype=Float64),
],
source=user_source,
)
# Retrieve for training
training_df = store.get_historical_features(
entity_df=entity_df,
features=["user_features:total_purchases"],
).to_df()
Rules
- Compute features once, use everywhere
- Version features alongside model versions
- Monitor feature distributions for drift
- Document feature semantics and business logic
- Test feature pipelines with known inputs
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 · 57 lines · 30 tokens per session scan A ed4fa95e1bf6
feature-store is a skill published in the GitHub repository ihatesea69/kiro-kit (18 stars, last pushed 20d ago), licensed MIT. It adds 30 tokens to every session and 294 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 2026-09-03.
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