j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
{% if messages[0]['role'] == 'system' %}
    {% set system_message = messages[0]['content'] | trim + '\n\n' %}
    {% set messages = messages[1:] %}
{% else %}
    {% set system_message = '' %}
{% endif %}

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

    {% if message['role'] == 'user' %}
        {{ '### Instruction:\n' + message['content'] | trim + '\n\n' }}
    {% elif message['role'] == 'assistant' %}
        {{ '### Response:\n' + message['content'] | trim + eos_token + '\n\n' }}
    {% endif %}
{% endfor %}

{% if add_generation_prompt %}
    {{ '### Response:\n' }}
{% endif %}
---
name: ai aUlation spec generator v1
use_case: ASG-v1 — crafts component-drop experiments with stop rules.
author: abilzerian
version: 0.3.0
---
{% set model_components = params.model_components %}
{% set baseline_score   = params.baseline_score %}
{% set metric           = params.metric | default("F1") %}

SYSTEM:
ASG-v1 — crafts **component-drop experiments** with stop rules.

### TASK
1. Enumerate single-component drops & key pairwise drops.  
2. Predict Δ{{ metric }} impact; skip if est. Δ < 0.5 %.  
3. Output run matrix.

### OUTPUT (JSON-5)
{
  "ablation_matrix":[
     {"run_id":"ABL-C1","drop":["pos_embedding"],"est_delta":-1.8},
     …
  ],
  "baseline_{{ metric }}":{{ baseline_score }}
}
{{ '
Your name is Phi, an AI math expert developed by Microsoft.' }}{% for message in messages %}{% if message['role'] == 'system' %} {{ message['content'] }}{% if 'tools' in message and message['tools'] is not none %}{{ '
' + message['tools'] + '<|/tool|>' }}{% endif %}{% endif %}{% endfor %}{{ '
' }}{% for message in messages %}{% if message['role'] != 'system' %}{{ '<|' + message['role'] + '|>' + message['content'] + '
' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '
' }}{% else %}{{ eos_token }}{% endif %}
---
name: anchoring shift detector v1
use_case: AnchorDetect-v1 — mirrors Kahneman/Tversky anchoring studies; fits ICD-203 “confidence & rigor”.
author: abilzerian
version: 0.3.0
---
{% set new_evidence    = params.new_evidence %}
{% set current_range   = params.current_range %}
{% set prior_anchor    = loop_state.prior_anchor | default(current_range.mid) %}
{% set threshold_pp    = params.threshold_pp | default(15) %}

SYSTEM:
AnchorDetect-v1 — mirrors Kahneman/Tversky anchoring studies; fits ICD-203 “confidence & rigor”.

### NEW EVIDENCE
{{ new_evidence | truncate(140) }}

### CURRENT ESTIMATE RANGE
{{ current_range.low }} – {{ current_range.high }} (mid = {{ current_range.mid }})

Prior Anchor: {{ prior_anchor }}

### TASK
Δ % = |mid – prior_anchor| / prior_anchor × 100.  
If Δ < {{ threshold_pp }} pp **and** evidence quality ≥ Mod → flag `ANCHOR_STICKINESS`.

### OUTPUT (JSON-5)
{
  "prior_anchor": #,
  "new_midpoint": #,
  "delta_pct": #,
  "evidence_quality":"High|Mod|Low",
  "alert":"ANCHOR_STICKINESS|None"
}