use serde_json::{json, Value};
pub fn list_prompts() -> Value {
json!([
{
"name": "author_preflight_review",
"description": "Pre-submission diagnostic flight simulator: benchmarks paper draft against crowd distributions, flags reviewer split risks, and provides a prescriptive variance reduction plan.",
"arguments": [
{
"name": "title",
"description": "Title of the research paper or grant proposal.",
"required": true
},
{
"name": "abstract",
"description": "Abstract, executive summary, or proposal body text.",
"required": true
},
{
"name": "boundary",
"description": "Selection or payline threshold (default: 1.2).",
"required": false
}
]
},
{
"name": "pipeline_congestion_audit",
"description": "Audits review pipeline traffic intensity, wait times, and backlog under Kingman's Heavy-Traffic approximation.",
"arguments": [
{
"name": "arrival_rate",
"description": "Proposals arriving per period (lambda).",
"required": true
},
{
"name": "service_rate",
"description": "Review capacity of the system per period (mu).",
"required": true
},
{
"name": "target_utilization",
"description": "Target sustainable utilization ceiling (default: 0.85).",
"required": false
}
]
},
{
"name": "multi_agent_panel_debiasing",
"description": "Debiases multi-LLM reviewer panels by computing effective evaluator sample size (M_eff) under shared training correlation.",
"arguments": [
{
"name": "scores",
"description": "Comma-separated scores from LLM evaluators (e.g. '1.6, 1.8, 1.5').",
"required": true
},
{
"name": "correlation",
"description": "Inter-agent error correlation rho in [0, 1) (default: 0.6).",
"required": false
}
]
}
])
}
pub fn get_prompt(name: &str, args: Value) -> Result<Value, String> {
match name {
"author_preflight_review" => {
let title = args
.get("title")
.and_then(|v| v.as_str())
.unwrap_or("Untitled Proposal");
let text = args.get("abstract").and_then(|v| v.as_str()).unwrap_or("");
let boundary = args
.get("boundary")
.and_then(|v| v.as_str())
.unwrap_or("1.2");
let prompt_text = format!(
"You are evaluating a research or grant proposal draft before official submission:\n\n**Title**: {}\n**Abstract / Summary**:\n\"\"\"\n{}\n\"\"\"\n\nPlease run the `aetre_author_preflight_benchmark` tool with `selection_boundary = {}` to perform empirical crowd benchmarking, evaluate reviewer split risk, and generate a prescriptive epistemic flight plan.",
title, text, boundary
);
Ok(json!({
"description": "Pre-submission benchmark diagnostic and variance reduction flight simulator.",
"messages": [
{
"role": "user",
"content": {
"type": "text",
"text": prompt_text
}
}
]
}))
}
"pipeline_congestion_audit" => {
let arrival_rate = args
.get("arrival_rate")
.and_then(|v| v.as_str())
.unwrap_or("95.0");
let service_rate = args
.get("service_rate")
.and_then(|v| v.as_str())
.unwrap_or("100.0");
let target = args
.get("target_utilization")
.and_then(|v| v.as_str())
.unwrap_or("0.85");
let prompt_text = format!(
"Please evaluate our evaluation pipeline capacity using AETRE's Kingman Heavy-Traffic Governor (`aetre_check_governor`):\n- Arrival Rate (lambda): {} arrivals/period\n- Service Capacity (mu): {} reviews/period\n- Target Utilization Ceiling: {}\n\nPlease analyze whether the system is at risk of delay explosion and recommend the exact automated triage throttling required to stabilize reviewer workload.",
arrival_rate, service_rate, target
);
Ok(json!({
"description": "Kingman heavy-traffic queue congestion audit and throttle recommendations.",
"messages": [
{
"role": "user",
"content": {
"type": "text",
"text": prompt_text
}
}
]
}))
}
"multi_agent_panel_debiasing" => {
let scores_str = args
.get("scores")
.and_then(|v| v.as_str())
.unwrap_or("1.6, 1.8, 1.5");
let corr = args
.get("correlation")
.and_then(|v| v.as_str())
.unwrap_or("0.6");
let prompt_text = format!(
"We collected evaluations from multiple LLM evaluators on a candidate proposal with raw scores: [{}].\nAssuming an inter-agent error correlation rho = {}:\n\nUse `aetre_correlated_posterior_update` to calculate:\n1. The effective evaluator sample size (M_eff)\n2. The redundancy correlation discount percentage\n3. The true debiased posterior mean and epistemic variance.",
scores_str, corr
);
Ok(json!({
"description": "Debiasing multi-LLM reviewer panels against shared correlation.",
"messages": [
{
"role": "user",
"content": {
"type": "text",
"text": prompt_text
}
}
]
}))
}
_ => Err(format!("Prompt with name '{}' not found", name)),
}
}