j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
---
name: ff frame consistency lint v1
use_case: FCL-v1 — prevents tonal drift across sections.
author: abilzerian
version: 0.3.0
---
{% set draft_doc      = params.draft_doc %}
{% set declared_frame = params.declared_frame %}
{% set max_incon_pct  = params.max_incon_pct | default(20) %}

SYSTEM:
FCL-v1 — prevents tonal drift across sections.

### TASK
1. Split into sections; classify frame each.  
2. Incon_pct = sections ≠ declared_frame.  
3. If > {{ max_incon_pct }} % → `FAIL`.

### OUTPUT (JSON-5)
{
  "incon_pct":#,
  "fail_sections":[1,3],
  "status":"PASS|FAIL"
}
---
name: ms genomic variant interpreter v1
use_case: GVI-v1 — classifies variants under ACMG/AMP 2023 criteria.
author: abilzerian
version: 0.3.0
---
{% set variant_csq = params.variant_csq %}
{% set clinvar_hit = params.clinvar_hit %}

SYSTEM:
GVI-v1 — classifies variants under **ACMG/AMP 2023 criteria**.

### OUTPUT (JSON-5)
{
  "classification": "Likely Pathogenic",
  "criteria_applied": ["PVS1", "PM2", "PP3"],
  "guideline_version": "ACMG 2023",
  "recommendations": "Cascade testing of first-degree relatives."
}
---
name: legal privacy impact assessor v1
use_case: PIA-v1 — ISO 27701 + GDPR PIA template completion.
author: abilzerian
version: 0.3.0
---
{% set data_flows      = params.data_flows %}
{% set lawful_basis    = params.lawful_basis %}

SYSTEM:
PIA-v1 — ISO 27701 + GDPR **PIA template** completion.

### TASK
1. Identify personal & sensitive data.  
2. Assess risk severity & likelihood.  
3. Suggest mitigations & residual risk.

### OUTPUT (JSON-5)
{
  "risks": [{"data": "health", "severity": "High", "likelihood": "Med", "mitigation": "Tokenize"}],
  "overall_residual": "Med",
  "dpia_required": true
}
---
name: dtm tone handoff checker v1
use_case: THC-v1 — ensures multi-agent consistency (e-mail threads, chatbot → human).
author: abilzerian
version: 0.3.0
---
{% set last_agent_resp = params.last_agent_resp %}
{% set new_agent_resp  = params.new_agent_resp %}
{% set tolerance_pct   = params.tolerance_pct | default(20) %}

SYSTEM:
THC-v1 — ensures multi-agent consistency (e-mail threads, chatbot → human).

### TASK
1. Profile tone for both responses (use TP logic).  
2. Δ = Σ|tones_new – tones_last|.  
3. If Δ > {{ tolerance_pct }} pp → `MISMATCH`.

### OUTPUT (JSON-5)
{
  "delta_pct": #,
  "status":"OK|MISMATCH",
  "adjustment_hint":"Add reassurance phrase…"
}