mantic-thinking 1.1.4

Cross-domain anomaly and opportunity detection using 4-layer hierarchical analysis

Mantic Early Warning & Emergence Detection System

Cross-domain anomaly and opportunity detection using 4-layer hierarchical analysis. Compatible with Claude, Kimi, Gemini, OpenAI, and Ollama.

14 tools total: 7 Friction (divergence detection) + 7 Emergence (confluence detection)

Core Formula (Immutable)

M = (sum(W * L * I)) * f(t) / k_n

Tool Suites

Friction Tools (Divergence Detection)

Detects when layers diverge: if abs(L1 - L2) > 0.5: alert()

Tool	Domain	Description
healthcare_phenotype_genotype	Healthcare	Phenotype-genotype mismatch
finance_regime_conflict	Finance	Market regime conflicts
cyber_attribution_resolver	Cyber	Attribution uncertainty
climate_maladaptation	Climate	Maladaptation prevention
legal_precedent_drift	Legal	Precedent drift alert
military_friction_forecast	Military	Operational friction
social_narrative_rupture	Social	Narrative rupture detection

Emergence Tools (Confluence Detection)

Detects when layers align: if min(L) > 0.6: window_detected()

Tool	Domain	Description
healthcare_precision_therapeutic	Healthcare	Optimal treatment windows
finance_confluence_alpha	Finance	High-conviction setups
cyber_adversary_overreach	Cyber	Defensive advantage windows
climate_resilience_multiplier	Climate	Multi-benefit interventions
legal_precedent_seeding	Legal	Precedent-setting windows
military_strategic_initiative	Military	Decisive action windows
social_catalytic_alignment	Social	Movement-building windows

Installation

pip install mantic-thinking

Quick Start

Native Python

# Friction tool (detect risk)
from tools.friction.healthcare_phenotype_genotype import detect as detect_friction
result = detect_friction(phenotypic=0.3, genomic=0.9, environmental=0.4, psychosocial=0.8)
print(f"Alert: {result['alert']}")  # Warning about mismatch

# Emergence tool (detect opportunity)
from tools.emergence.healthcare_precision_therapeutic import detect as detect_emergence
result = detect_emergence(genomic_predisposition=0.85, environmental_readiness=0.82,
                          phenotypic_timing=0.88, psychosocial_engagement=0.90)
print(f"Window: {result['window_detected']}")  # True - optimal timing

For Kimi Code CLI

from adapters.kimi_adapter import get_kimi_tools, execute, compare_friction_emergence

# Get all 14 tools
tools = get_kimi_tools()

# Compare friction vs emergence for same domain
comparison = compare_friction_emergence(
    "healthcare",
    friction_params={"phenotypic": 0.3, "genomic": 0.9, "environmental": 0.4, "psychosocial": 0.8},
    emergence_params={"genomic_predisposition": 0.85, "environmental_readiness": 0.82,
                     "phenotypic_timing": 0.88, "psychosocial_engagement": 0.90}
)
# High M in friction = risk. High M in emergence = opportunity.

For Claude Code CLI

from adapters.claude_adapter import get_claude_tools, execute_tool, format_for_claude

# Get 14 tools in Computer Use format
tools = get_claude_tools()

# Execute and format for Claude
result = execute_tool("finance_confluence_alpha", {
    "technical_setup": 0.85, "macro_tailwind": 0.80,
    "flow_positioning": 0.75, "risk_compression": 0.70
})
print(format_for_claude(result, "finance_confluence_alpha"))

For Gemini

from adapters.gemini_adapter import get_gemini_tools, execute_tool

# Get tools in Gemini FunctionDeclaration format
tools = get_gemini_tools()

# Or get flat list for simpler SDK usage
from adapters.gemini_adapter import get_gemini_tools_flat
declarations = get_gemini_tools_flat()

result = execute_tool("climate_resilience_multiplier", {
    "atmospheric_benefit": 0.75, "ecological_benefit": 0.80,
    "infrastructure_benefit": 0.78, "policy_alignment": 0.82
})

For Ollama (MiniMax, GPT-OSS, GLM, etc.)

from adapters.openai_adapter import get_openai_tools, execute_tool
import openai

# Ollama's OpenAI-compatible endpoint
client = openai.OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"
)

tools = get_openai_tools()
# Works with: minimax-m2.1:cloud, gpt-oss:20b-cloud, glm-4.7:cloud, etc.

For Codex / OpenAI

from adapters.openai_adapter import get_openai_tools, execute_tool, get_tools_by_type

# Get all 14 tools
tools = get_openai_tools()

# Or filter by type
friction = get_tools_by_type("friction")   # 7 tools
emergence = get_tools_by_type("emergence") # 7 tools

result = execute_tool("cyber_adversary_overreach", {
    "threat_intel_stretch": 0.90, "geopolitical_pressure": 0.85,
    "operational_hardening": 0.80, "tool_reuse_fatigue": 0.88
})

Architecture

mantic-thinking/
├── SKILL.md                    # Universal manifest
├── README.md                   # This file
├── schemas/
│   ├── openapi.json           # OpenAPI spec
│   └── kimi-tools.json        # Kimi native format
├── core/
│   ├── mantic_kernel.py       # IMMUTABLE core formula
│   └── validators.py          # Input validation
├── tools/
│   ├── friction/              # 7 divergence detection tools
│   └── emergence/             # 7 confluence detection tools
├── adapters/                  # Model-specific adapters (Claude/Kimi/Gemini/OpenAI)
├── configs/                   # Domain configurations & framework docs
│   ├── mantic_tech_spec.md    # Technical specification
│   ├── mantic_explicit_framework.md  # Framework protocol
│   ├── mantic_health.md       # Healthcare domain config
│   ├── mantic_finance.md      # Finance domain config
│   └── ...                    # (8 domain configs total)
└── tests/                     # Cross-model validation

Running Tests

# Quick sanity check
python3 -c "from adapters.openai_adapter import get_openai_tools; print(len(get_openai_tools()), 'tools ready')"

# Run all tests
python3 -m pytest tests/test_cross_model.py -v

# Test individual tool
python3 tools/emergence/healthcare_precision_therapeutic.py

Key Principle: Same M-Score, Opposite Meaning

M-Score	Friction (Risk)	Emergence (Opportunity)
0.1-0.3	Low risk	Low opportunity (wait)
0.4-0.6	Moderate friction	Favorable window
0.7-0.9	High risk	Optimal window

The M-score measures intensity. Friction tools interpret high intensity as danger. Emergence tools interpret high intensity as opportunity.

Configuration Files

The configs/ directory contains framework documentation and domain-specific configurations:

• Framework docs: Technical specification, explicit framework mode, reasoning guidelines
• Domain configs: Healthcare, Finance, Cybersecurity, Climate, Legal, Social, Command, Current Affairs

These provide layer mappings and cross-domain coupling patterns for implementing domain-specific tools.

Design Principles

1. Immutable Core: mantic_kernel.py must not be modified
2. Deterministic: Same inputs always return same outputs
3. No External APIs: Pure Python + NumPy only
4. Cross-Model Compatible: Works with Claude, Kimi, Codex, GPT-4o
5. Complementary Suites: Friction for risks, Emergence for opportunities
6. Simple Logic: Each tool <100 lines, threshold-based

Contributing

See CONTRIBUTING.md. We accept contributions from individuals only and require Signed-off-by commits (DCO).

License

Source-Available License (Default)

Elastic License 2.0 — See LICENSE for full text.

tl;dr:

• Free to use, modify, distribute for internal applications
• Can use in production for your own organization
• Cannot offer as a hosted/managed service (SaaS)
• Cannot embed in commercial products without commercial license
• Cannot remove license protections

Commercial License

Want to build a SaaS on top of Mantic? Embed it in your product? Redistribute?

Purchase a commercial license — See COMMERCIAL_LICENSE for pricing and terms.

Tier	Best For	From
Startup	<$1M revenue, internal use	$500/year
Growth	<$50M revenue, internal use	$5,000/year
Enterprise	Unlimited internal, large orgs	$25,000/year
OEM/SaaS	Embed, resell, offer as service	Custom (from $50k)

Contact: [e-mail]

All licenses include updates and email support. OEM includes custom development options.

Version

1.1.4 - Adapter import hygiene (no sys.path mutation) 1.1.3 - Input validation and confluence logic refinement 1.1.2 - PyPI install instructions updated