Create README.md

README.md

@@ -0,0 +1,112 @@
+---
+license: mit
+task_categories:
+- text-generation
+- question-answering
+- other
+language:
+- en
+tags:
+- agentic
+- tool-use
+- cli
+- remote-operations
+- reasoning
+- chain-of-thought
+- planning
+- recovery
+- WithinUsAI
+pretty_name: The_Skills_From_WithIn_10k
+size_categories:
+- 10K<n<100K
+---
+
+# Dataset Card for The_Skills_From_WithIn_10k
+
+## Dataset Summary
+
+**The_Skills_From_WithIn_10k** is a high-quality, professionally curated dataset of 10,000 unique examples designed to train any LLM on the most advanced **agentic skills** for remote CLI operations.
+
+Each example features:
+- A complex professional remote operational goal or incident
+- A rigorous `<thinking>` trace demonstrating senior-level agentic reasoning: goal decomposition, multi-step planning, explicit verification gates, rollback/contingency strategies, blast-radius control, safety invariants, and production-grade decision making
+- A precise, ordered `action` (CLI sequence or agent playbook) ready for execution
+
+This dataset builds true autonomous remote operations capability — planning, safety, recovery, and verifiable execution — for DevOps, SRE, and agentic systems.
+
+**Creator:** WithIn Us Ai (WithinUsAI)
+
+## Dataset Structure
+
+Each line in the JSONL file is a JSON object:
+
+```json
+{
+  "id": "skills_remote_00001",
+  "query": "Complex professional remote operational goal or incident description",
+  "thinking": "<thinking>Advanced agentic reasoning trace: goal decomposition, state assessment, step-by-step plan with rationale, risk analysis, verification criteria, rollback strategy, and why this sequence</thinking>",
+  "action": "Ordered, production-safe CLI sequence or full agent playbook with comments and verification steps"
+}
+```
+
+## Data Fields
+
+- **id**: Unique identifier (skills_remote_XXXXX)
+- **query**: Realistic high-stakes remote task (migrations, incident response, hardening, failover, scaling, recovery drills)
+- **thinking**: Expert agentic Chain-of-Thought inside `<thinking>` tags. Teaches sophisticated planning, safety, and self-correction.
+- **action**: Executable multi-step playbook with built-in verification and rollback logic.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("WithinUsAI/The_Skills_From_WithIn_10k", split="train")
+
+print(dataset[0]["query"])
+print(dataset[0]["thinking"])
+print(dataset[0]["action"])
+```
+
+Perfect for:
+- Training advanced agentic / autonomous remote CLI agents
+- SFT with visible sophisticated reasoning traces
+- Building reliable zero-downtime operations, incident response, and change management agents
+- Improving LLM planning, safety, and recovery capabilities in production environments
+
+## Quality & Creation
+
+- 10,000 unique professional examples
+- Zero duplicates, zero placeholders, zero dummy content
+- Created by WithIn Us Ai to the highest professional standard
+- Real-world agentic scenarios: zero-downtime migrations, OOM/incident stabilization, CIS hardening with rollback, multi-server fleet operations
+- Strong emphasis on safety invariants, verification at every step, and minimal blast radius
+
+## Dataset Creation
+
+Professionally designed and generated by WithIn Us Ai (WithinUsAI). Examples engineered for dense learning signal in agentic reasoning, remote constraint handling, and flawless execution discipline. Every trace teaches transferable senior SRE / DevOps agent skills.
+
+## Citation
+
+If you use this dataset, please cite:
+
+```
+@misc{withinusai2026skillsfromwithin,
+  title={The_Skills_From_WithIn_10k: Professional Agentic Remote CLI Skills Dataset},
+  author={WithIn Us Ai},
+  year={2026},
+  howpublished={\url{https://huggingface.co/datasets/WithinUsAI/The_Skills_From_WithIn_10k}}
+}
+```
+
+## Contact & License
+
+**Creator & Maintainer:** WithIn Us Ai (WithinUsAI)  
+**License:** MIT  
+**Organization:** https://huggingface.co/WithinUsAI
+
+For the complete production pipeline or collaboration, contact WithinUsAI.
+
+---
+
+*Built with precision for reliable autonomous agentic systems.*