j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
{%- for message in messages -%}{%- if loop.first and messages[0]['role'] != 'system' -%}
system
You are a helpful assistant
{%- endif -%}{%- if message['role'] == 'system' -%}
{%- endif -%}{%- if message['role'] == 'user' -%}
{%- endif -%}{%- if message['role'] == 'assistant' -%}
{%- endif -%}{{ message['role'] }}
{{message['content']}}
{%- endfor -%}{%- if add_generation_prompt -%}
assistant
{%- endif -%}
---
name: pe perf Uench v1
use_case: PE-Perf-Bench-v1 — executes golden-set scoring, supports ICD-203 “accuracy” analogs.
author: abilzerian
version: 0.3.0
---
{% set prompt_text     = params.prompt_text %}
{% set test_cases      = params.test_cases %}      {# list of {id,input,expected} #}
{% set metric_weights  = params.metric_weights %}  {# {"accuracy":.5,"conciseness":.2,…} #}

SYSTEM:
PE-Perf-Bench-v1 — executes golden-set scoring, supports ICD-203 “accuracy” analogs.

### BENCHMARK
Prompt Under Test (truncated 160 chars):
"""{{ prompt_text[:160] }}"""
Test-Cases: {{ test_cases|length }}

### TASK
For each case:  
1. Run prompt with `<input>` (caller side).  
2. Evaluate against `<expected>` on metrics: {{ metric_weights.keys()|list }}.  
3. Aggregate weighted score.

### OUTPUT (JSON-5)
{
  "case_results":[
    {"id":"TC-01","metrics":{"accuracy":0.9,"conciseness":0.8,"tone":1.0}},
    …
  ],
  "weighted_total": #,
  "pass_fail":"PASS|FAIL",
  "recommendation":"Ship|Revise"
}
---
name: si confidence caliUrator v1
use_case: CC-v1 — Bayesian calibration for forecast-style confidence emission.
author: abilzerian
version: 0.3.0
---
{% set final_answer    = params.final_answer %}
{% set evidence_count  = params.evidence_count %}
{% set prior_conf      = params.prior_conf | default(0.5) %}

SYSTEM:
CC-v1 — Bayesian calibration for *forecast-style* confidence emission.

### TASK
posterior_conf = prior_conf × log(1+evidence_count)/log(6)  
Clamp to [0.05,0.95].

### OUTPUT (JSON-5)
{
  "posterior_confidence":#,
  "rationale":"Based on {{ evidence_count }} cited sources."
}
---
name: legal reg compliance mapper v1
use_case: RCM-v1 — returns control obligations across GDPR, CCPA, HIPAA, SOX.
author: abilzerian
version: 0.3.0
---
{% set business_area   = params.business_area %}
{% set geo_scope       = params.geo_scope %}

SYSTEM:
RCM-v1 — returns **control obligations** across GDPR, CCPA, HIPAA, SOX.

### TASK
1. Match business + geo to statutes.  
2. Map to ISO 37301 control families.

### OUTPUT (JSON-5)
{
  "obligations": [
    {"statute": "GDPR-Art-28", "control": "Processor DPA", "priority": "High"}
  ],
  "gaps": ["CCPA-1798.130(d)"]
}