Improve dataset card: add metadata, paper link, and usage

Hi! I'm Niels from the community science team at Hugging Face. I'm opening this PR to improve the documentation for the `linear-bench-mini` dataset.

Specifically, this PR:
- Adds the `text-generation` task category to the metadata.
- Links the research paper: [Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation](https://huggingface.co/papers/2602.11224).
- Adds links to the project website and GitHub repository.
- Provides a sample usage code snippet based on the `agent-diff` Python SDK found in the official repository.

README.md

@@ -1,29 +1,68 @@
 ---
-dataset: linear-bench-mini
-tags:
-  - agent-evals
-  - linear
-  - graphql
 license: mit
+task_categories:
+- text-generation
+tags:
+- agent-evals
+- linear
+- graphql
 ---
 
+# Agent-Diff: Linear Bench Mini
 
+This dataset is part of the **Agent-Diff** benchmark, presented in the paper [Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation](https://huggingface.co/papers/2602.11224).
+
+[Website](https://agentdiff.dev) | [GitHub](https://github.com/agent-diff-bench/agent-diff) | [Paper](https://huggingface.co/papers/2602.11224)
 
 ![image](https://cdn-uploads.huggingface.co/production/uploads/68fce8f5dfd2a1e1cae334bb/G8dofl-K8F-V3SxKyBiI_.png)
 
-## Files
+## Context
 
-- `data/train.jsonl`
-- `dataset_infos.json`
-- `seeds/linear_default.json`
+The Linear Bench suite runs inside the Agent Diff isolation engine, with its own Postgres schema replaying the Linear GraphQL API. Agents interact via Linear's public surface area to satisfy CRUD-style tasks (create issues, move states, add labels, etc.). Task success is defined by whether the expected change in environment state was achieved using a novel state-diff contract.
 
-## Context
+## Files
 
-The Linear Bench suite runs inside Agent Diff isolation engine, with
-its own Postgres schema replaying the Linear GraphQL API. Agents interact via
-Linear's public surface area to satisfy CRUD-style tasks (create issues, move
-states, add labels, etc.).
+- `data/train.jsonl`: Training tasks and prompts.
+- `dataset_infos.json`: Dataset configuration.
+- `seeds/linear_default.json`: Initial state configuration for the Linear environment.
 
 ## Usage
 
-Read more here https://github.com/hubert-marek/agent-diff
+To use this benchmark, you can interact with the isolated environment using the `agent-diff` Python SDK.
+
+### Install the SDK
+```bash
+pip install agent-diff
+```
+
+### Initialize and Run
+```python
+from agent_diff import AgentDiff
+
+# Initialise isolated environment from the linear template
+client = AgentDiff()
+env = client.init_env(
+    templateService="linear", 
+    templateName="linear_default",
+    impersonateUserId="U01AGENBOT9", 
+    TTL="3600"
+)
+
+# Take before snapshot
+run = client.start_run(envId=env.environmentId)
+
+# Your agent performs tasks using the environment URL:
+# print(env.environmentUrl)
+
+# Compute diff (changes in the environment) and get results
+diff = client.diff_run(runId=run.runId)
+
+# Inspect changes
+print(diff.diff['inserts'])   # New records added by agent
+print(diff.diff['updates'])   # Modified records
+
+# Clean up
+client.delete_env(envId=env.environmentId)
+```
+
+For more details on evaluations and the state-diff framework, please refer to the [official GitHub repository](https://github.com/agent-diff-bench/agent-diff).