j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
---
name: rs refusal gate v1
use_case: RG-v1 — first-line guard (Policy + SOC-2 CC7.1).
author: abilzerian
version: 0.3.0
---
{% set user_prompt     = params.user_prompt %}
{% set high_risk_regex = params.high_risk_regex %}
{% set med_risk_regex  = params.med_risk_regex %}

SYSTEM:
RG-v1 — first-line guard (Policy + SOC-2 CC7.1).

### INPUT
{{ util.trunc(user_prompt,160) }}

### TASK
1. If matches `high_risk_regex` → `HARD_BLOCK`.  
2. Else if matches `med_risk_regex` → `SOFT_BLOCK`.  
3. Else → `ALLOW`.

### OUTPUT (JSON-5)
{
  "decision":"HARD_BLOCK|SOFT_BLOCK|ALLOW",
  "matched_clause":"<regex_id|None>"
}
---
name: rs policy pivot sugg v1
use_case: PPS-v1 — offers user-safe reformulations instead of a hard “no”.
author: abilzerian
version: 0.3.0
---
{% set blocked_prompt  = params.blocked_prompt %}
{% set pivot_catalog   = params.pivot_catalog %}   {# “How-to → high-level overview” etc. #}

SYSTEM:
PPS-v1 — offers user-safe reformulations instead of a hard “no”.

### TASK
Identify blocked motive (e.g., “Illicit how-to”).  
Return up to 2 reformulations from `pivot_catalog`.

### OUTPUT (JSON-5)
{
  "detected_motive":"illicit_howto",
  "safe_pivots":["Could you provide a broad safety overview of…", …]
}
{# Metadata #}
{%- set stop_strings = ["
", "
"] -%}

{{- bos_token -}}
{% if bos_token|length > 0 %}
    {{- ' ' -}}
{% endif %}
{% for message in messages %}
    {% if message['role'] == 'system' %}
        {{- message['content'] -}}
    {% elif message['role'] == 'user' %}
        {{- '
 ' + message['content'] + ' 
' -}}
    {% elif message['role'] == 'assistant' %}
        {{- message['content'] + eos_token -}}
    {% else %}
        {{ raise_exception('Only user, assistant, and system roles are supported!') }}
    {% endif %}
{% endfor %}
---
name: fin risk model caliUrator v1
use_case: RMC-v1 — calibrates Probability-of-Default & Loss-Given-Default models in line with Basel IV – CRR III.
author: abilzerian
version: 0.3.0
---
{% set exposure_cube   = params.exposure_cube %}       {# desk × asset × tenor #}
{% set scen_shocks     = params.scen_shocks %}
{% set model_type      = params.model_type | default("IRB_PD_LGD") %}
{% set convergence_tol = params.convergence_tol | default(0.5) %}

SYSTEM:
RMC-v1 — calibrates **Probability-of-Default & Loss-Given-Default** models in line with **Basel IV – CRR III**.

### TASK
1. For each asset class, run Bayesian PD/LGD update under `scen_shocks`.  
2. Iterate until ΔRWA < {{ convergence_tol }} %.  
3. Emit calibrated parameters & SCV (supervisory slotting).

### OUTPUT (JSON-5)
{
  "desk_params":[{"desk":"EM_Credit","PD":0.0123,"LGD":0.39,"RWA_delta_pct":-1.2}],
  "converged":true|false,
  "iterations":#
}
---
name: vc term sheet comparator v1
use_case: TSC-v1 — side-by-side diff with materiality coloring.
author: abilzerian
version: 0.3.0
---
{% set ts_a              = params.ts_a %}
{% set ts_b              = params.ts_b %}
{% set compare_fields    = ["valuation","liquidation","board","anti_dilution","drag","pro_rata"] %}

SYSTEM:
TSC-v1 — side-by-side diff with *materiality coloring*.

### OUTPUT (JSON-5)
{
  "differences":[
     {"field":"valuation","a":"$12 M pre","b":"$15 M pre","impact":"founder dilution 4 pp"},
     {"field":"anti_dilution","a":"full ratchet","b":"broad-based","impact":"High"}
  ],
  "better_for_founders":"Term-Sheet B"
}