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 inbharatai/claude-skills --skill feature-engineergit clone --depth 1 https://github.com/inbharatai/claude-skillsWrote 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/inbharatai/claude-skills/feature-engineer)<a href="https://agentmods.dev/skills/inbharatai/claude-skills/feature-engineer"><img src="https://agentmods.dev/badge/skills/inbharatai/claude-skills/feature-engineer/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/inbharatai/claude-skills/feature-engineer"><img src="https://agentmods.dev/badge/skills/inbharatai/claude-skills/feature-engineer.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 | $0.00023 | $0.00389 |
| Opus 5 | $0.00012 | $0.00195 |
| Sonnet 5 | $0.00005 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
feature-engineer 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 10d 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 Engineer
Overview
Design and create ML features — encoding, scaling, interaction terms, embeddings, and feature selection.
When to Use This Skill
Use Feature Engineer when you need to:
- Work with feature engineer tasks in your project or workflow
- Automate feature engineer operations at scale
- Generate production-quality feature engineer output quickly
Instructions
When this skill is active, Claude will:
- Understand the full context of your feature engineer request
- Apply best practices and conventions for Data & Analytics
- Produce clean, well-structured, production-ready output
- Explain key decisions and offer alternatives where relevant
Examples
Example 1 — Basic Usage
User: Help me get started with feature engineer.
Claude: I'll walk you through the essential steps for feature engineer in your context...
Example 2 — Advanced Usage
User: I need a production-ready feature engineer setup with full error handling.
Claude: Here's a complete, production-hardened feature engineer implementation...
Guidelines
- Always validate inputs before processing
- Follow the conventions of the target platform or language
- Prefer explicit over implicit — clarity beats cleverness
- Include comments for non-obvious logic
- Suggest tests or validation steps where appropriate
Dependencies
Required: python, pandas, sklearn
Platforms
Available on: claude-code, api
Part of the claude-skills collection — 183+ skills for Claude.
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.
- 10d ago First seen · 68 lines · 23 tokens per session scan A 669ad990fdb4
feature-engineer is a skill published in the GitHub repository inbharatai/claude-skills (33 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 389 once invoked, about $0.0001 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.
Other skills, from other repositories
optimize
Automatically refines every user prompt into a structured, actionable version, then immediately executes the optimized prompt. When triggered explicitly with "/optimize {prompt}", "optimize:", or "optimize prompt:", outputs the refined prompt as text instead.
extremerouter-stt
Speech-to-text via ExtremeRouter /v1/audio/transcriptions using OpenAI Whisper / Groq / Gemini / Deepgram / AssemblyAI / NVIDIA / HuggingFace models. Use when the user wants to transcribe audio, convert speech to text, or get subtitles from audio files.
extremerouter
Entry point for ExtremeRouter — local/remote AI gateway with OpenAI-compatible REST for chat, image, TTS, embeddings, web search, web fetch. Use when the user mentions ExtremeRouter, NINEROUTERURL, or wants AI without writing provider boilerplate. This skill covers setup + indexes capability skills; fetch the relevant…
AIProductManager
Complete AI-native product management — AI feature strategy, model selection, evaluation frameworks, AI UX design, responsible AI, and building products that use LLMs, CV, and ML as core features.
knowledge-graph-builder
Activates KnowledgeGraph — an expert in building, querying, and reasoning over knowledge graphs. Use when you need entity extraction, relationship mapping, ontology design, Neo4j/RDF graph construction, graph-RAG pipelines, or complex multi-hop reasoning over structured knowledge.
rag-architect
Activates the RAG-Architect agent for designing and building Retrieval-Augmented Generation systems. Use this skill when you need to build a document Q&A system, design a knowledge base with semantic search, set up vector stores (Chroma, Pinecone, pgvector), implement hybrid retrieval (dense + BM25 sparse), add…