diff --git a/src/genbench/tasks/slayqa/__init__.py b/src/genbench/tasks/slayqa/__init__.py
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diff --git a/src/genbench/tasks/slayqa/config.jsonnet b/src/genbench/tasks/slayqa/config.jsonnet
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+{
+    name: 'slayqa',
+
+    // @TODO: Add a description of the task
+    description: 'SlayQA aims to measure generalisation in social reasoning where agents are referred to with gender-neutral and/or neo-pronouns.',
+
+    // @TODO: Add a list of keywords that describe the task
+    keywords: [
+        'social reasoning',
+        'neo-pronouns',
+	'inclusivity',
+	'fairness',
+    ],
+
+    authors: [
+        'Bastian Bunzeck',
+        
+    ],
+
+    data_source: {
+        type: 'hf',
+        hf_id: 'bbunzeck/slayqa',
+	git_commit_sha: '63cc322aa7a18e288f95e165cef06850c1b064dc',
+    },
+
+    has_validation_set: false,
+    has_train_set: false,
+
+    task_type: 'multiple_choice',
+
+
+    evaluation_metrics: [
+        {
+            hf_id: 'accuracy',
+            best_score: 1.0,
+            git_commit_sha: '34d6add55811828baef83e0d7c6826e2193f7b6a',
+        },
+    ],
+
+    preparation_strategies: {
+        // A recipe for preparing the model to perform the task by configuring its prompt.
+        // This recipe is suitable for generative LMs such as GPT-3, OPT, T5, etc.
+        // We provide a few options for configuring the prompt. But, the task creator can
+        // also provide a custom prompt preparation in the task's Python class.
+        prompt_based_testing: {
+            prompt_builder: {
+                instruction_zero_shot: '',
+                instruction_few_shot: '',
+                input_prefix: '',
+                output_prefix: '',
+            }
+        },
+    },
+}
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diff --git a/src/genbench/tasks/slayqa/doc.md b/src/genbench/tasks/slayqa/doc.md
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+# SlayQA
+
+We present SlayQA: Social LinguisticsAnalyticsYielding Queer Agents, a novel benchmark set derived from the existing [Social IQa (SiQA) dataset](https://doi.org/10.18653/v1/D19-1454). It contains a situation description (the context), social reasoning questions and three prospective answers, where all context-question-answer pairs include at least two acts of pronoun-based reference and gender-neutral pronouns. Because SlayQA systematically replaces established, gendered pronouns with gender-affirming neo-pronouns, it is a more inclusive dataset that better reflects the diversity of human identities. This also marks a significant linguistic distribution shift in comparison to common pre-training corpora like C4 or Dolma. Consequentially, SlayQA aim to assess how well language models are able to generalize to novel linguistic structure.
+
+## Abstract
+TBD, not yet finalised.
+
+## Examples
+
+The tasks are social reasoning problems that involve the selection of a correct answer:
+
+Context + question:
+- Taylor called Sydney to ask if ae would like to go out. What does Taylor need to do before this?
+
+Answer options:
+- have a phone
+- give Taylor an answer
+- check es email
+
+## Usage
+
+```
+# Load the task
+task = genbench.load_task('slayqa')
+dataset = task.get_datasets_raw()
+
+# dataset['test'] contains the individual entries: 
+# 'input' has combined context and question
+# 'target_options' contains three possible answers
+# 'target' contains index of gold standard answer 
+
+# My predictions should be a list of dicts with predicted 'target' values
+
+# add your own prediction code here :)
+
+# or: naive baseline
+my_predictions = []
+for i in range(len(dataset['test'])):
+    temp_dict = {}
+    temp_dict['target'] = 1
+    my_predictions.append(temp_dict)
+
+# Evaluate
+# The evaluate_predictions method compares the 'target' entry for a list of prediction dicts and the gold standard from the data set
+task.evaluate_predictions(predictions = my_predictions, gold = dataset['test'])
+```
+
+## Data Source
+Data set hosted at [Hugging Face hub](https://huggingface.co/datasets/bbunzeck/slayqa).
+
+## Limitations and Bias
+Our study focuses on neo-pronouns. In addition to these, other gender-inclusive options exist, e.g. nounself pronouns, emojiself pronouns, numberself pronouns or nameself pronouns (see [Lauscher et al. 2022](https://aclanthology.org/2022.coling-1.105)). We opted to focus on neo-pronouns because we wanted to assess a limited and (somewhat) established set of gender-inclusive options, but further studies should also try to evaluate even more diverse options.
+
+## GenBench eval card
+**Motivation**: This test is designed to assess whether LMs can generalise to novel pronouns in human-like ways (cognitive motivation) and whether they succeed in doing so with gender-neutral neo-pronouns (fairness motivation) in a social reasoning setting. 
+
+**Generalisation type**: Because we only included examples with at least two coreference chains, its performance reflects compositional generalisation. 
+
+**Shift type**: As the test samples differ in their pronoun frequencies from the training corpus, we assume that the shift is a covariate shift. 
+
+**Shift source**: The distribution shift was artificially generated by systematically replacing gendered pronouns with neo-pronouns.
+
+**Shift locus**: The test data was altered to differ from the pretraining data, and as we assess the capabilities without further fine-tuning, the shift locus is located between the pretraining corpora and our test data.
+
+
+![GenBench Eval Card](eval_card.png)
diff --git a/src/genbench/tasks/slayqa/eval_card.png b/src/genbench/tasks/slayqa/eval_card.png
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diff --git a/src/genbench/tasks/slayqa/task.py b/src/genbench/tasks/slayqa/task.py
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+from typing import Any, Dict
+
+from genbench import Task
+
+
+# @Task.register("SlayQA")
+class SlayQA(Task):
+    """slayqa"""
+
+    def format_example(self, example: Dict[str, Any]) -> Dict[str, Any]:
+        """Perform preprocessing/formatting on an example-level.
+        Map `context`, `question` to input, 'correct' to target and answerA, answerB and answerCC to target_options.
+
+        Args:
+            example: A dictionary containing key-value pairs for an example from the SlayQA dataset.
+
+
+        Returns:
+            A dictionary containing key-value pairs for the formatted example.
+        """
+        input = f"{example['context']}  {example['question']}"
+        target = int(example["correct"]) - 1
+        answerA = example["answerA"]
+        answerB = example["answerB"]
+        answerC = example["answerC"]
+        return {"input": input, "target": int(target), "target_options": [answerA, answerB, answerC]}