feature-store-readiness

feature-store-readiness is a skill for Codex from Emily2040/data-science-agent-skills. It costs 116 tokens per session (1,048 once invoked), scanned A, original, no licence file.

A guide for deciding whether a feature store is needed and for planning reusable model inputs. A feature store is a shared system for storing and serving prepared data features.

In plain words
What is it for?
Use it to assess feature-store needs and design features for consistent reuse and timely updates.
Why use it?
It helps avoid differences between the data used to train a model and the data used when it makes predictions, along with stale or confusing features.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to assess feature-store needs and design features for consistent reuse and timely updates.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/emily2040/data-science-agent-skills/feature-store-readiness
Install

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.

Any agent
npx skills add Emily2040/data-science-agent-skills --skill feature-store-readiness
Clone the repo
git clone --depth 1 https://github.com/Emily2040/data-science-agent-skills

Made for: Codex.

Wrote 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.

agentmods badge for feature-store-readiness

README.md
[![agentmods](https://agentmods.dev/badge/skills/emily2040/data-science-agent-skills/feature-store-readiness/github.svg)](https://agentmods.dev/skills/emily2040/data-science-agent-skills/feature-store-readiness)
Your own site
<a href="https://agentmods.dev/skills/emily2040/data-science-agent-skills/feature-store-readiness"><img src="https://agentmods.dev/badge/skills/emily2040/data-science-agent-skills/feature-store-readiness/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.

agentmods 80×15 button for feature-store-readiness

Your own site · 80×15
<a href="https://agentmods.dev/skills/emily2040/data-science-agent-skills/feature-store-readiness"><img src="https://agentmods.dev/badge/skills/emily2040/data-science-agent-skills/feature-store-readiness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 116 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,048 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00116 $0.01048
Opus 5 $0.00058 $0.00524
Sonnet 5 $0.00023 $0.00210
Haiku 4.5 $0.00012 $0.00105

Measured 11d ago against content hash 537a63968c90, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

feature-store-readiness 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 11d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/checklist_score.py, scripts/quick_validate_skill.py, tests/test_skill_contract.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

data-science-agent-skills/feature-store-readiness/SKILL.md · 95 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 11d ago First seen · 95 lines · 116 tokens per session scan A 537a63968c90

Subscribe to this mod's changes

feature-store-readiness is a skill published in the GitHub repository Emily2040/data-science-agent-skills (16 stars, last pushed 3mo ago), with no licence file. It adds 116 tokens to every session and 1,048 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 2026-08-30.

Related

Other skills, from other repositories

pinecone

Managed vector DB for production RAG and search.

NousResearch/hermes-agent · 13 tokens

embeddings

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

ruvnet/ruflo · 62 tokens

data-engineer

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.

davila7/claude-code-templates · 35 tokens

graphjin-env

Use when setting up a training or evaluation loop against a GraphJin agent environment — running the container, reading /health, driving episodes hosted or step-by-step or with your own agent over MCP, splitting train from eval, exporting trajectories, and deciding whether two rewards can be compared.

dosco/graphjin · 61 tokens

ingesting-into-data-lake

Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where…

aws/agent-toolkit-for-aws · 228 tokens

similarity-search-patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

foryourhealth111-pixel/Vibe-Skills · 30 tokens