#!/usr/bin/env python3
# MIT License
#
# Copyright (c) 2025 Recursive Companion Contributors
# Based on work by Hank Besser (https://github.com/hankbesser/recursive-companion)
#
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"""
Domain-specific configuration for Recursive Companion MCP Server
Consolidates domain detection and domain-specific prompts
"""
import re
# Domain detection keywords
DOMAIN_KEYWORDS: dict[str, list[str]] = {
"technical": [
"code",
"algorithm",
"api",
"debug",
"performance",
"architecture",
"system",
"database",
"security",
"function",
"python",
"javascript",
"tcp",
"ip",
"protocol",
"optimize",
"queries",
"indexes",
"rest",
"authentication",
"async",
"await",
"implement",
"binary",
"search",
"stack",
"software",
],
"marketing": [
"marketing",
"campaign",
"audience",
"brand",
"conversion",
"engagement",
"market",
"customer",
"segment",
"targeting",
"positioning",
"messaging",
"growth",
"acquisition",
"social media",
"ad",
"copy",
"facebook",
"email",
"launch",
"compelling",
],
"strategy": [
"goal",
"objective",
"plan",
"roadmap",
"vision",
"competitive",
"analysis",
"swot",
"strategic",
"long-term",
"initiative",
"milestone",
"kpi",
"metric",
],
"legal": [
"contract",
"compliance",
"regulation",
"liability",
"agreement",
"terms",
"privacy",
"gdpr",
"legal",
"law",
"policy",
"dispute",
"intellectual property",
"copyright",
"trademark",
"non-disclosure",
"nda",
"service",
"saas",
],
"financial": [
"revenue",
"cost",
"budget",
"forecast",
"investment",
"profit",
"cash flow",
"valuation",
"financial",
"accounting",
"balance sheet",
"income",
"expense",
"roi",
"npv",
"irr",
"calculate",
"portfolio",
"stock",
"allocation",
"statements",
],
}
# Domain-specific system prompts
DOMAIN_PROMPTS: dict[str, str] = {
"technical": (
"You are a technical expert. Focus on accuracy, best practices, "
"and clear technical explanations."
),
"marketing": (
"You are a marketing strategist. Focus on audience engagement, " "brand messaging, and ROI."
),
"strategy": (
"You are a strategic advisor. Focus on long-term vision, "
"competitive advantage, and actionable insights."
),
"legal": (
"You are a legal expert. Focus on compliance, risk mitigation, "
"and precise legal language."
),
"financial": (
"You are a financial analyst. Focus on quantitative analysis, "
"financial metrics, and data-driven insights."
),
"general": (
"You are a helpful assistant. Provide clear, accurate, " "and well-structured responses."
),
}
class DomainDetector:
"""Detects the appropriate domain for a given prompt"""
@staticmethod
def detect_domain(prompt: str) -> str:
"""Auto-detect domain based on keywords and patterns"""
prompt_lower = prompt.lower()
domain_scores = {}
for domain, keywords in DOMAIN_KEYWORDS.items():
score = 0
for keyword in keywords:
# Use word boundaries for single words, exact match for phrases
if " " in keyword:
# Multi-word phrase - exact match
if keyword in prompt_lower:
score += 1
else:
# Single word - use word boundaries
pattern = r"\b" + re.escape(keyword) + r"\b"
if re.search(pattern, prompt_lower):
score += 1
if score > 0:
domain_scores[domain] = score
if not domain_scores:
return "general"
# Return domain with highest score
return max(domain_scores, key=domain_scores.get)
def get_domain_system_prompt(domain: str) -> str:
"""Get domain-specific system prompt"""
return DOMAIN_PROMPTS.get(domain, DOMAIN_PROMPTS["general"])
