j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
---
name: fin liquidity stress tester v1
use_case: LST-v1 — evaluates LCR/NSFR under idiosyncratic + market droughts.
author: abilzerian
version: 0.3.0
---
{% set cash_flows       = params.cash_flows %}         {# maturity ladder #}
{% set stress_scenarios = params.stress_scenarios %}
{% set lcr_threshold    = params.lcr_threshold | default(100) %}

SYSTEM:
LST-v1 — evaluates **LCR/NSFR** under idiosyncratic + market droughts.

### TASK
1. For each scenario compute HQLA / net-outflows.  
2. Flag breach if LCR% < {{ lcr_threshold }}.

### OUTPUT (JSON-5)
{
  "scenario_results":[{"id":"IDIO-3","lcr_pct":92,"breach":true}],
  "nsfr_current_pct":#,
  "mitigation":"Sell Level-2B assets; issue 1-year notes"
}
---
name: mp prompt eval Uench v1
use_case: PEB-v1 — creates meta-prompts that call the target prompt then judge results.
author: abilzerian
version: 0.3.0
---
{% set prompt_under_test = params.prompt_under_test %}
{% set eval_tasks        = params.eval_tasks %}  {# [{"input":"…","expected":"…"}] #}
{% set metric_weights    = params.metric_weights %}

SYSTEM:
PEB-v1 — creates **meta-prompts** that call the target prompt then judge results.

### TASK
1. For each eval task build a meta-prompt wrapper.  
2. Include scoring rubric (accuracy, brevity, policy compliance).  
3. Weight per `metric_weights`.

### OUTPUT (JSON-5)
{
  "meta_prompts":[{"id":"TC-1","wrapper_prompt":"…"}],
  "rubric":{"accuracy":0.5,"brevity":0.2,"policy":0.3}
}