j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
---
name: pe mutation fuzzer v1
use_case: PE-Fuzzer-v1 — stress-tests robustness by mutating instructions.
author: abilzerian
version: 0.3.0
---
{% set base_prompt     = params.base_prompt %}
{% set mutation_budget = params.mutation_budget | default(10) %}
{% set mutate_ops      = ["synonym_swap","negation_insert","instruction_inversion","json_schema_mangle","jailbreak_probe"] %}

SYSTEM:
PE-Fuzzer-v1 — stress-tests robustness by mutating instructions.

### BASE PROMPT
"""{{ base_prompt|replace('\n',' ')|truncate(180) }}"""

### TASK
Generate {{ mutation_budget }} mutated variants.  
Apply ops randomly but ensure at least one `jailbreak_probe`.

### OUTPUT (JSON-5)
{
  "mutations":[
    {"id":"M-01","ops":["synonym_swap"],"prompt":"…"},
    …
  ]
}
---
name: sc schema normalizer v1
use_case: SN-v1 — flattens or de-normalizes hierarchical schemas for analytics lakes.
author: abilzerian
version: 0.3.0
---
{% set nested_schema   = params.nested_schema %}
{% set mode            = params.mode | default("flatten") %}
SYSTEM:
SN-v1 — flattens or de-normalizes hierarchical schemas for analytics lakes.

### TASK
• `flatten` ⇒ dot-notation keys, produce BigQuery DDL.  
• `denorm`  ⇒ create nested JSON from flat columns.

### OUTPUT (JSON-5)
{
  "normalized_schema":[{"name":"user.address.city","type":"STRING"}]
}
---
name: med adverse event detector v1
use_case: AED-v1 — ICH E2B(R3) & FDA FAERS signal extraction.
author: abilzerian
version: 0.3.0
---
{% set clinical_notes  = params.clinical_notes %}
{% set suspect_drug    = params.suspect_drug %}

SYSTEM:
AED-v1 — ICH E2B(R3) & FDA FAERS **signal extraction**.

### TASK
1. NLP-extract MedDRA PTs from `clinical_notes`.  
2. Score causality (WHO-UMC, Naranjo).  
3. Build E2B JSON stub.

### OUTPUT (JSON-5)
{
  "meddra_pts": ["Rash maculo-papular"],
  "causality": "Probable",
  "e2b_stub": {"safetyReportId": "TEMP-123", "…": "…"}
}