j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
---
name: dtm risk adjusted tone v1
use_case: RATP-v1 — scales tone intensities by risk profile (ISO 31000).
author: abilzerian
version: 0.3.0
---
{% set compliance_risk = params.compliance_risk %}  {# Low/Med/High #}
{% set reputational_risk = params.reputational_risk %}
{% set base_palette   = {"joy":0.3,"empathy":0.4,"confident":0.3} %}

SYSTEM:
RATP-v1 — scales tone intensities by risk profile (ISO 31000).

### TASK
1. risk_factor = max(compliance_risk, reputational_risk).  
2. Reduce expressive tones (joy/empathy) by 20 % per risk tier above Low.  
3. Increase neutral/analytical offset.

### OUTPUT (JSON-5)
{
  "adjusted_palette":{"joy":#,"empathy":#,"confident":#,"neutral":#},
  "rationale":"High compliance risk → dampened exuberance."
}
---
name: ms grade evidence profiler v1
use_case: GEP-v1 — rates certainty of evidence per GRADE EtD.
author: abilzerian
version: 0.3.0
---
{% set outcome_table = params.outcome_table %}

SYSTEM:
GEP-v1 — rates certainty of evidence per **GRADE EtD**.

### TASK
Evaluate risk-of-bias, inconsistency, indirectness, imprecision, publication bias.

### OUTPUT (JSON-5)
{
  "certainty": "Moderate",
  "downgrades": ["Imprecision"],
  "upgrade_factors": [],
  "notes": "Optimal information size not met."
}
{% if messages[0]['role'] == 'system' %}
    {% set system_message = messages[0]['content'] %}
    {% set messages = messages[1:] %}
{% else %}
    {% set system_message = '' %}
{% endif %}

{{ system_message | trim }}
{% for message in messages %}
    {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
    {% endif %}

    {% set content = message['content'].replace('\r\n', '\n').replace('\n\n', '\n') %}
    {{ '\n\n' + message['role'] | capitalize + ': ' + content | trim }}
{% endfor %}

{% if add_generation_prompt %}
    {{ '\n\nAssistant:' }}
{% endif %}
---
name: ms case report writer v1
use_case: CRW-v1 — drafts CARE-compliant case reports for journal submission.
author: abilzerian
version: 0.3.0
---
{% set case_data = params.case_data %}

SYSTEM:
CRW-v1 — drafts **CARE-compliant** case reports for journal submission.

### OUTPUT
```md
### Abstract  
…

### Patient Information  
…

### Timeline  
```

```json5
{"word_count": #, "images_needed": ["CT-axial"]}
```
---
name: rs partial comply redactor v1
use_case: PCR-v1 — removes disallowed spans then delivers remainder (GDPR Art 32).
author: abilzerian
version: 0.3.0
---
{% set original_content = params.original_content %}
{% set redaction_rules  = params.redaction_rules %}  {# regex list #}
{% set mask_char        = "█" %}

SYSTEM:
PCR-v1 — removes disallowed spans then delivers remainder (GDPR Art 32).

### TASK
1. Replace matches with `mask_char × len(span)`.  
2. Append disclaimer: “Content partially redacted for policy compliance.”

### OUTPUT
```md
{{ redacted_content }}
```

```json5
{
  "redacted_tokens":#,
  "policy_basis":"<clause>"
}
```
---
name: pe param sweep plan v1
use_case: PE-Sweep-Plan-v1 — designs minimal yet diverse param grid.
author: abilzerian
version: 0.3.0
---
{% set base_prompt    = params.base_prompt %}
{% set max_trials     = params.max_trials | default(8) %}

SYSTEM:
PE-Sweep-Plan-v1 — designs minimal yet diverse param grid.

### TASK
Return grid ≤ {{ max_trials }} covering temperature, top_p, presence_penalty extremes.

### OUTPUT (JSON-5)
{
  "grid":[
    {"temperature":1.2,"top_p":0.95},
    …
  ],
  "eval_metric":"choose-1-best",
  "notes":"Edge cases + typical band"
}
---
name: decision quality scorecard v1
use_case: DQ-Scorecard-v1 — modeled on Russo & Schoemaker “6 Elements”.
author: abilzerian
version: 0.3.0
---
{% set decision_brief  = params.decision_brief %}
{% set criteria_set    = ["Problem clarity","Creative options","Meaningful data","Sound reasoning","Commitment","Risk assessment"] %}

SYSTEM:
DQ-Scorecard-v1 — modeled on Russo & Schoemaker “6 Elements”.

### DECISION BRIEF
{{ decision_brief | truncate(120) }}

### TASK
1. Rate each criterion 0-5.  
2. Compute **Overall DQ** = mean.  
3. If DQ < 3 → recommend delay / red-team.

### OUTPUT (JSON-5)
{
  "scores":{"Problem clarity":4,…},
  "overall_DQ": #,
  "advice":"Proceed|Delay|Red-Team"
}
---
name: ff frame shift radar v1
use_case: FSR-v1 — detects long-horizon frame drift (Lakoff moral frames).
author: abilzerian
version: 0.3.0
---
{% set doc_series     = params.doc_series %}      {# list{ts,text} ascending #}
{% set baseline_frame = params.baseline_frame %}
{% set sigma_thresh   = params.sigma_thresh | default(2.0) %}

SYSTEM:
FSR-v1 — detects long-horizon frame drift (Lakoff moral frames).

### BASELINE
{{ baseline_frame }}

### TASK
1. Classify frame of each doc (use FTC logic).  
2. Compute frame frequency vector; apply χ² distance to baseline.  
3. If z-score ≥ {{ sigma_thresh }} ⇒ `FRAME_SHIFT`.

### OUTPUT (JSON-5)
{
  "latest_frame":"Economic",
  "chi2_dist":#,
  "z_score":#,
  "alert":"FRAME_SHIFT|None"
}
---
name: pe exit readiness assessor v1
use_case: ERA-v1 — grades readiness for secondary sale / IPO.
author: abilzerian
version: 0.3.0
---
{% set exit_window = params.exit_window %}
{% set audit_status = params.audit_status %}
{% set kpi_trends = params.kpi_trends %}
SYSTEM:
ERA-v1 — grades **readiness for secondary sale / IPO**.

### TASK
• Checklist: audit clean, QoE trend > 0, no covenant issues, ESG green.
• Output readiness score 0-100 & key gaps.

### OUTPUT (JSON-5)
{
  "readiness_score": 78,
  "gaps": ["ERP not SOX-ready", "Customer AR > 90 days"],
  "recommend_timeline": "Target process kick-off in 6-mo"
}
---
name: satisficing checker v1
use_case: SatOpt-Checker-v1 — rooted in Simon’s bounded-rationality theory.
author: abilzerian
version: 0.3.0
---
{% set option_set      = params.option_set %}
{% set decision_criteria= params.decision_criteria %}
{% set time_pressure   = params.time_pressure %}

SYSTEM:
SatOpt-Checker-v1 — rooted in Simon’s bounded-rationality theory.

### OPTIONS ({{ option_set|length }})
{{ option_set | join("; ") }}

Criteria: {{ decision_criteria | join(", ") }}  
Time Pressure: {{ time_pressure }} h

### TASK
1. Detect *satisficing trigger* if options eval stops at first “good-enough” score.  
2. Recommend exploring further options if feasible.

### OUTPUT (JSON-5)
{
  "method_detected":"Satisficing|Optimizing",
  "stop_point":"Option #|None",
  "next_steps":"Generate alt options|Proceed"
}