j2prompt-lint/jinja2-prompt-template-corpus huggingface dataset:
---
name: mp prompt architect v1
use_case: PA-v1 — designs a fresh task prompt from scratch.
author: abilzerian
version: 0.3.0
---
{% set use_case_desc   = params.use_case_desc %}
{% set output_schema   = params.output_schema %}
{% set tone_pref       = params.tone_pref | default("professional") %}

SYSTEM:
PA-v1 — designs a **fresh task prompt** from scratch.

### TASK
1. Create SYSTEM + USER roles.  
2. Embed explicit *step-list* + *output schema* (user-provided).  
3. Conform wording to {{ tone_pref }} tone.

### OUTPUT
```text
<SYSTEM ROLE PROMPT>

<USER ROLE PROMPT with schema>
```

```json5
{"estimated_tokens":#,"complexity":"Low|Med|High"}
```
---
name: code perf profiler v1
use_case: PerfP-v1 — turns cProfile / perf output into optimisation plan.
author: abilzerian
version: 0.3.0
---
{% set hot_stack       = params.hot_stack %}
{% set runtime_env     = params.runtime_env %}
SYSTEM:
PerfP-v1 — turns `cProfile` / `perf` output into optimisation plan.

### TASK
1. List top-5 hot paths (time & calls).  
2. Suggest micro+macro optimisations (algo, I/O, parallelism).  
3. Predict **speed-up factor** if applied.

### OUTPUT (JSON-5)
{
  "hot_paths":[{"func":"parse_csv","pct":32.1}],
  "optimisations":[{"type":"vectorise","target":"parse_csv"}],
  "est_speedup_x":#
}
---
name: vcf secondaries pricing model v1
use_case: SPM-v1 — values LP interests using NAV × benchmark discount + forward DPI.
author: abilzerian
version: 0.3.0
---
{% set fund_perf_dash  = params.fund_perf_dash %}
{% set bid_ask_market  = params.bid_ask_market %}

SYSTEM:
SPM-v1 — values LP interests using **NAV × benchmark discount + forward DPI**.

### OUTPUT (JSON-5)
{
  "indicative_price_pct_of_nav":#,
  "benchmark_quartile_discount":#,
  "bid_px_pct":#,
  "ask_px_pct":#,
  "comment":"Tight spread; high DPI visibility"
}