cortex-research-suite 1.1.0

AI Research Operating System — skill testing, synthetic data, evaluation, multi-agent orchestration

Cortex Research Suite

AI Research Operating System — 26 autonomous skills, self-evolving Skill Organism, browser-based experiment arena, and a Python evaluation framework. Covers research workflows, MLOps enforcement, security auditing, agent orchestration, intelligence analysis, and developer tooling. Works natively with Claude Code and integrates with LangChain, CrewAI, and OpenAI via MCP adapters.

Quickstart — 3 entry points

1. Browser (zero install)

Open dashboards/skill_arena_demo.html in any browser. Paste your Anthropic API key. Pick a skill. Click Run Experiment. Watch the organism evolve in real time.

2. Terminal

git clone https://github.com/TECHKNOWMAD-LABS/cortex-research-suite.git
cd cortex-research-suite
pip install -e ".[dev]"

# Run the test suite
pytest

# Generate evaluation datasets for all 26 skills
python datasets/generators/skill_dataset_generator.py --all-skills --n 50

# Run evaluation on any skill
python skills/skill-test-harness/scripts/eval_judge.py \
  --skill security-audit --dataset datasets/synthetic/security-audit/shard_000.json

# Run the multi-agent debate engine
python skills/agent-orchestrator/scripts/debate_engine.py \
  --topic "AI safety in healthcare" --rounds 3

# Start overnight evolution (runs in background)
python skill-organism/enterprise_runner.py --overnight --generations 10

3. Python API

from cortex.synthetic.reasoning_generator import ReasoningGenerator
from cortex.evaluation.judge import LLMJudge
from cortex.agents.orchestrator import AgentOrchestrator

# Generate evaluation prompts
gen = ReasoningGenerator(seed=42)
prompts = gen.generate(100)

# Run multi-agent research pipeline
orchestrator = AgentOrchestrator(provider)
result = orchestrator.run("Analyze the impact of transformer architectures on NLP")

# Evaluate output quality
judge = LLMJudge(provider)
score = judge.score(prompt="...", response=result.final_output)
print(f"Quality: {score.normalized:.0%}")  # e.g., Quality: 87%

Cortex in the intelligence ecosystem

Layer	Component	What it does	Integration
5. Simulation	MiroFish-inspired scenario simulator	Swarm-based what-if analysis with counterfactual injection	skills/scenario-simulator/
4. Intelligence	BettaFish-inspired analysis engine	Multi-source intelligence queries, forum analysis, multimodal	skills/intelligence-query/, skills/forum-intelligence/, skills/multimodal-analyst/
3. Data	MindSpider connector	Live topic feeds from social listening deployments	skills/mindspider-connector/
2. Evolution	Skill Organism + ARENA.md	Autonomous overnight evolution with fitness tracking	skill-organism/, skills/*/ARENA.md
1. Foundation	21 core skills + evaluation lab	Research, security, MLOps, orchestration, quality	skills/, cortex/

Skills (26)

Skill	Category	Description
agent-orchestrator	Agents	Multi-agent coordination with DAG task graphs
agent-output-validator	Validation	Automated validation of agent outputs against quality gates
code-review-engine	Engineering	Automated code review with security checks
context-engineer	Engineering	Context window optimization and prompt management
de-slop	Quality	AI-generated writing pattern detection and removal
design-system-forge	Design	Design system generation and component library scaffolding
dev-lifecycle-engine	DevOps	Development lifecycle management
diff-generator	Engineering	Structured diff generation for code and document changes
forum-intelligence	Intelligence	Forum thread analysis with coordination detection
github-mcp	Integration	GitHub API via Model Context Protocol
intelligence-query	Intelligence	Multi-source intelligence analysis engine
meta-skill-evolver	Meta	Evolutionary skill improvement and mutation engine
mindspider-connector	Data	Live social listening feed connector
mlops-standards	MLOps	ML operations best practices enforcement
multimodal-analyst	Intelligence	Cross-modal content analysis (text + image + video)
persistent-memory	Infrastructure	SQLite-backed memory with FTS5 search
pre-package-pipeline	Packaging	Skill validation and packaging pipeline
prompt-architect	Engineering	Prompt engineering and optimization
repo-publisher	DevOps	Pre-publish pipeline with security scanning
research-workflow	Research	Experiment design and methodology
scenario-simulator	Simulation	MiroFish-inspired swarm scenario simulation
security-audit	Security	Bandit + semgrep + secret scanning pipeline
session-memory	Infrastructure	Session-scoped memory persistence
skill-test-harness	Testing	Automated skill testing framework with LLM-as-Judge
skill-validator	Validation	Skill structure and manifest validation
tdd-enforcer	Testing	Test-driven development enforcement

See AGENTS.md for the full agent manifest with platform-specific integration guides.

Cortex Python Framework

Module	Purpose
cortex.synthetic	Synthetic data generation (reasoning, research, strategy, domain, adversarial)
cortex.evaluation	LLM-as-Judge scoring, benchmark suites, regression detection
cortex.agents	Multi-agent orchestrator, debate arena, DAG task graphs
cortex.models	Model provider abstraction (Anthropic SDK + CLI fallback)
cortex.telemetry	Structured logging, SQLite metrics collector
cortex.config	YAML + env var configuration with thread-safe singleton
cortex.utils	Atomic I/O, input sanitization, prompt injection detection
cortex.experiments	Experiment tracking with comparison and best-run queries

Skill Organism

The skill-organism/ directory contains the evolution engine. Skills are automatically tested and scored. Underperformers get modified via mutation, top performers get replicated via crossbreeding, and the system recovers from population loss by restoring previously successful versions.

Key features:

• ARENA.md per skill — the "program.md" from Karpathy's autoresearch pattern
• ArenaConfig parser with trilogy integration fields
• EvalBudget context manager for time-bounded evaluation
• Git-per-experiment branching (branch, mutate, evaluate, merge or discard)
• Overnight runner with asyncio.Semaphore(4) parallel generations
• Crash-safe JSONL evolution log for dashboard consumption

See OVERNIGHT_USAGE.md for autonomous evolution setup.

Cross-Platform Support

Platform	Adapter Type	Status
Claude Code	Native Skills	Primary
MCP (Model Context Protocol)	FastMCP Servers	Generated
LangChain	Tool Classes	Generated
CrewAI	Tool Wrappers	Generated
OpenAI GPT Actions	Action Schemas	Generated
VS Code / Copilot / Cursor / Windsurf / JetBrains	MCP via Extension	Compatible

Project Structure

cortex-research-suite/
├── cortex/                    # Python framework (pip install -e .)
├── skills/                    # 26 autonomous skills (SKILL.md + ARENA.md + scripts/)
├── skill-organism/            # Skill evolution engine
├── knowledge/                 # Knowledge store (FTS5 + GraphRAG)
├── experiments/               # Experiment tracker (SQLite)
├── datasets/                  # Synthetic datasets + MindSpider feed
├── benchmarks/                # Baselines for all skills
├── dashboards/                # Browser dashboards (evolution, benchmark, arena)
├── cross-platform/            # Generated adapters (MCP, LangChain, CrewAI, OpenAI)
├── packages/                  # Standalone packages (de-slop-cli)
├── docs/                      # Documentation site (GitHub Pages)
├── scripts/                   # CLI entry points and utilities
├── tests/                     # Test suite
└── .github/workflows/         # CI/CD (lint, test, security, eval, release)

Security

All code passes automated security scanning on every push:

• Bandit Python SAST with zero HIGH/MEDIUM findings
• CodeQL semantic code analysis
• Secret scanning with push protection enabled
• Dependabot automated dependency updates
• Prompt injection detection (7 compiled regex patterns)
• Path traversal protection across all I/O operations
• Browser arena: CSP, sessionStorage key isolation, rate limiting, input sanitization

Report vulnerabilities to [e-mail]. See SECURITY.md.

Legal

Cortex Research Suite is MIT licensed. Trilogy integration skills are inspired by the architectural patterns of MindSpider, BettaFish, and MiroFish. No code has been copied. See LEGAL_NOTES.md.

Contributing

See CONTRIBUTING.md for the full guide including how to add new skills. All PRs require:

• Passing CI checks (bandit, lint, tests)
• One approving review
• No leaked secrets or credentials

License

MIT — see LICENSE.