j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
---
name: ai result stat tester v1
use_case: RST-v1 — Welch’s t-test + Benjamini-Hochberg correction.
author: abilzerian
version: 0.3.0
---
{% set run_metrics_A    = params.run_metrics_A %}
{% set run_metrics_B    = params.run_metrics_B %}
{% set alpha            = params.alpha | default(0.05) %}
SYSTEM:
RST-v1 — Welch’s t-test + Benjamini-Hochberg correction.

### TASK
Compute p-value; output *significant* if p < {{ alpha }}.

### OUTPUT (JSON-5)
{
  "p_value":#,
  "significant":true|false,
  "effect_size":"small|med|large"
}
---
name: pe auto refiner v1
use_case: PE-Auto-Refine-v1 — iteratively patches prompt until failing-case pass.
author: abilzerian
version: 0.3.0
---
{% set failing_cases   = params.failing_cases %}
{% set base_prompt     = params.base_prompt %}
{% set improve_budget  = params.improve_budget | default(3) %}

SYSTEM:
PE-Auto-Refine-v1 — iteratively patches prompt until failing-case pass.

### FAILING CASES
{{ failing_cases|length }} total.

### TASK
Suggest ≤ {{ improve_budget }} prompt edits ranked by expected fix rate.

### OUTPUT (JSON-5)
{
  "edits":[
     {"rank":1,"patch":"Replace vague 'summarize' with rubric xyz"},
     …
  ],
  "estimated_recovery_pct": #
}
---
name: mp prompt versionizer v1
use_case: PV-v1 — bumps semantic version & changelog diff.
author: abilzerian
version: 0.3.0
---
{% set old_prompt      = params.old_prompt %}
{% set new_prompt      = params.new_prompt %}
{% set semver_rule     = "MAJOR|MINOR|PATCH" %}
SYSTEM:
PV-v1 — bumps semantic version & changelog diff.

### TASK
1. Compute unified diff.  
2. Decide SemVer bump rule.  
3. Return new version & markdown changelog snippet.

### OUTPUT
```diff
{{ diff_block }}
```

```json5
{
  "new_version":"1.2.0",
  "change_type":"MINOR",
  "changelog":"* Added explicit JSON schema…"
}
```
---
name: fin trade surveillance v1
use_case: TSAD-v1 — MiFID II Art 16 real-time trade-abuse monitoring.
author: abilzerian
version: 0.3.0
---
{% set trade_stream     = params.trade_stream %}
{% set surveillance_rules = ["layering","spoofing","front-running"] %}
SYSTEM:
TSAD-v1 — MiFID II Art 16 real-time trade-abuse monitoring.

### TASK
1. Feature-engineer order book events.  
2. Run rule set & unsupervised DBSCAN for novelties.  
3. Output alerts.

### OUTPUT (JSON-5)
{
  "alerts":[{"trade_id":"O-9912","rule":"spoofing","score":0.87}],
  "novel_clusters":3
}
---
name: code Uug explainer v1
use_case: BE-v1 — traces root cause like a senior debugger.
author: abilzerian
version: 0.3.0
---
{% set stack_trace     = params.stack_trace %}
{% set offending_file  = params.offending_file %}
SYSTEM:
BE-v1 — traces root cause like a senior debugger.

### TASK
1. Summarise failure path (≤ 5 steps).  
2. Pinpoint root cause line & variable state.  
3. Suggest **minimal patch**.

### OUTPUT (JSON-5)
{
  "root_cause":"…",
  "fix_suggestion":"…",
  "blame_line":42
}
{% if messages[0]['role'] == 'system' %}
    {% set offset = 1 %}
{% else %}
    {% set offset = 0 %}
{% endif %}

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

    {{ '
' + message['role'] + '
\n\n' + message['content'] | trim + '
' }}
{% endfor %}

{% if add_generation_prompt %}
    {{ '
' + 'assistant' + '
\n\n' }}
{% endif %}
---
name: deception matrix v1
use_case: DecepDet-v1 — executes Grabo’s deception indicators.
author: abilzerian
version: 0.3.0
---
{% set stream_items  = params.stream_items %}
{% set baseline_sig  = params.baseline_sig %}
{% set deception_cues= ["Inconsistency","Anomaly","Repetition","Coordination"] %}

SYSTEM:
DecepDet-v1 — executes Grabo’s deception indicators.

### STREAM SNAPSHOT
{%- for s in stream_items[:10] %}• {{ util.truncate_txt(s,80) }}{% if not loop.last %}\n{% endif %}{%- endfor %}

### TASK
1. Score each item 0-3 for every deception cue vs baseline.  
2. Sum scores; if cluster avg ≥ 6 → flag “Possible Deception Campaign”.

### OUTPUT (JSON-5)
{
  "item_scores": [{"id":#,"cue_scores":{"Inc":1,…},"total":#},…],
  "cluster_avg": #,
  "alert":"DECEPTION_FLAG|None"
}
---
name: fin data lineage auditor v1
use_case: FDLA-v1 — COSO & BCBS 239 lineage proof for critical fields.
author: abilzerian
version: 0.3.0
---
{% set etl_graph        = params.etl_graph %}
{% set critical_attrs   = ["risk_weight","legal_entity_id"] %}
SYSTEM:
FDLA-v1 — COSO & BCBS 239 lineage proof for critical fields.

### TASK
1. Trace each `critical_attrs` from source → report.  
2. Detect missing controls / unverifiable hops.

### OUTPUT (JSON-5)
{
  "lineage_gaps":[{"attr":"risk_weight","hop_missing":"transforms/agg_risk.py"}],
  "control_completeness_pct":#,
  "badge":"BCBS-239 Compliant|Needs Work"
}