Goals Extract Skill
Intent
Derive the business goal, success criteria, constraints, and KPIs from requirements input.
Inputs
- •requirements[] (from requirements folder)
Outputs
json
{
"goal_statement": "string",
"success_criteria": ["string"],
"kpis": ["string"],
"constraints": ["string"],
"assumptions": ["string"],
"open_questions": ["string"]
}
Tools
- •LLM with goal extraction prompt
- •Glossary lookup to normalize terms
Prompt Stub
Summarize the business goal in one sentence. List success criteria, KPIs, constraints, assumptions, and open questions as bullet arrays.
Quality
- •Enforce SMART wording on KPIs where possible (target, unit, timeframe)
Example Output
json
{
"goal_statement": "Enable efficient and accurate ingestion of requirements into the AI Slowcooker system to support downstream automation and analysis.",
"success_criteria": [
"All requirements are ingested without loss or misinterpretation.",
"Ingestion process is completed within the defined time window.",
"System supports traceability from ingested requirements to business objectives."
],
"kpis": [
"100% of requirements ingested per batch (unit: percent, timeframe: per ingestion cycle)",
"Ingestion latency less than 5 minutes per batch (unit: minutes, timeframe: per batch)",
"Zero critical errors in requirement mapping (unit: count, timeframe: monthly review)"
],
"constraints": [
"Input requirements must conform to the specified format.",
"System must operate within existing infrastructure and security policies.",
"No manual intervention allowed during ingestion."
],
"assumptions": [
"Requirements provided are complete and up-to-date.",
"Glossary terms are available for normalization.",
"Downstream systems are ready to consume ingested data."
],
"open_questions": [
"What is the process for handling ambiguous or conflicting requirements?",
"How will updates to requirements be managed post-ingestion?",
"Are there scalability limits for batch size or frequency?"
]
}