GABBE (Generative Architectural Brain Base Engine)

Agentic Software R&D Engineering Kit

Quick Reference

What is this?

GABBE turns the AI coding agent you already use into a governed software engineering team. It plans, writes specs, designs, tests and ships through quality gates, follows proven engineering methods, and stops for your approval where it matters. Open source, for any language, any project (new or existing) and any team size.

Why use it

• One kit for every agent. Claude Code, Cursor, Windsurf, GitHub Copilot / VS Code, Gemini, OpenAI Codex, Cline, Aider, Devin, Antigravity, OpenCode, Zed, Continue, Roo Code and Kilo Code. Skills are written once and installed in each tool's own format (Cursor .mdc, Copilot SKILL.md, .claude/skills, and .agents/skills/ for agentskills.io tools). Switch agents mid-project: the project state exports and imports.
• Engineering discipline built in. One lifecycle from idea to retirement (S00–S13): strategy and discovery, the S01–S10 build loop, then operate, evolve and decommission. Every phase ends at a gate. Work is spec-driven (spec → evals → tests → code) and grounded in named methods: ADD 3.0, ATAM, Wardley mapping, JTBD, RICE, DORA/SPACE, ADKAR.
• You stay in charge. Human approval for risky or expensive steps; budgets, token limits and hard stops; policy rules; an audit trail and deterministic replay. You manage the agents instead of operating them.
• Safer AI output. Evals (LLM-as-judge, RAG and trajectory evals) and guardrails (prompt-injection defence, output validation) mapped to the OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS, ISO 42001 and the EU AI Act.
• Tests that look for real failures. Property-based, metamorphic, chaos / fault-injection and mutation testing (gabbe verify --chaos).
• See what the agents did and what it cost. Decision and cost traces in the OpenTelemetry GenAI conventions.
• Scales from a bug fix to a large programme. Loki Mode runs a swarm of 30+ specialist roles with episodic and semantic memory, checkpoints and a project history. Brain Mode (experimental) is a meta-cognitive orchestrator, inspired by neuroscience and cognitive psychology (Active Inference), that plans, routes work between local and remote models and learns from past outcomes. Agents pick the right skills and guides for each task and recommend the MCP servers it needs.
• Installs in one command. npx gabbe-kit init (no Python needed) or curl -fsSL …/install.sh | sh; pipx install gabbe adds the CLI. gabbe doctor detects your OS, runtimes and agents; updates and uninstalls are tracked in a manifest.

Honest limits. Budgets, policy rules, the tool gateway (MCP server) and replay are experimental. The self-evolving "genes" model is a conceptual framing: the shipped brain chooses prompts by epsilon-greedy selection on their success rate. Evals and property-based tests sample behaviour and raise confidence; they do not prove correctness.

It contains:

• 214 Skills (specialized capabilities)
• 100 Templates (standardized documents)
• 86 Guides (language & domain expertise)
• 36 Personas (specialized roles)
• 50+ MCP servers (configuration and guides for AI tools)
• Brain Mode (meta-cognitive orchestration)
• Loki Mode (multi-agent swarm engineering personas team for large projects)

214 Skills · 100 Templates · 86 Guides · 36 Personas · 50+ MCPs · Loki / Brain Mode CLI

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Website: andreibesleaga.github.io/GABBE

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Full documentation: README_FULL.md · Full quick guide: QUICK_GUIDE.md · Quick commands: QUICK_COMMANDS.md · CLI reference: CLI_REFERENCE.md · MCP servers: MCP_CONFIGURATIONS.md · Platform Controls: PLATFORM_CONTROLS.md · Verification Guide: VERIFICATION_GUIDE.md · Architecture: ARCHITECTURE.md · Roadmap: ROADMAP.md

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Online articles: Medium, Substack, TechRxiv

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Example project where GABBE is used: Agentic AI Medical Imagery Diagnostic Helper

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⚡ Automated Setup (Recommended)

All install options at a glance (every channel is a single command — pick one):

Channel	One-command install	Notes
npm / Node	npx gabbe-kit init	Python-independent; bundles the kit & wires detected agents
PyPI	pipx install gabbe	adds the gabbe CLI (doctor/brain/route/gateway); install the kit via npx / curl / checkout
Shell bootstrap	curl -fsSL https://raw.githubusercontent.com/andreibesleaga/GABBE/main/install.sh | sh	picks the best available installer
Git checkout	git clone https://github.com/andreibesleaga/GABBE && cd GABBE && python3 scripts/init.py	the interactive wizard

The npm package gabbe-kit installs the kit; the PyPI package gabbe installs the CLI. Full guide: docs/INSTALL.md.

Universal, Python-independent (one command):

npx gabbe-kit init                       # Node installer — bundles the kit, wires every agent
npx gabbe-kit init --agents claude,cursor,antigravity,opencode --yes   # non-interactive
npx gabbe-kit init --force               # re-template even preserved files (each is backed up first)
# or, without npm:
curl -fsSL https://raw.githubusercontent.com/andreibesleaga/GABBE/main/install.sh | sh

Safe on existing projects. Any file the installer would change is backed up first (<name>.gabbe-bak), and your AGENTS.md, CONSTITUTION.md, TASKS.md, policies.yml and config.json are only replaced with --force.

Python / PyPI:

pipx install gabbe            # installs the `gabbe` CLI (doctor / brain / route / gateway)
# the kit is Python-independent — land it into a project with:
npx gabbe-kit init            # (or `curl … | sh`)
python3 scripts/init.py       # …or the wizard from a checkout (equivalent to `gabbe setup`)

The installer is a Universal Skill Compiler — it generates the correct format for each AI tool:

• Cursor: .cursor/rules/*.mdc (agent-requested rules, intelligently selected by description)
• VS Code / Copilot: .github/skills/<slug>/SKILL.md
• Claude Code: .claude/skills/<slug>/SKILL.md
• Gemini: .gemini/settings.json + GEMINI.md
• Antigravity / OpenCode (and any agentskills.io tool): the universal .agents/skills/<slug>/SKILL.md tree (+ opencode.json for OpenCode)
• Zed / Continue / Roo Code / Kilo Code: root AGENTS.md (agents.md standard) + each tool's rules file
• Every install also writes a root AGENTS.md (the agents.md open standard).

New or existing project. The wizard notices an existing codebase, asks whether you are building something new or upgrading it, and pre-fills your language and framework (details).

Steps after running the wizard:

1. Verify Context

   • Open agents/AGENTS.md and check the Tech Stack section and other [PLACEHOLDER] or Optional sections.
   • The wizard fills in what it can detect (commands, repository URL, CI) and lists the fields you still need to fill. Fill them in or re-run gabbe setup.
   • Open agents/CONSTITUTION.md and review project laws.

2. Feed the Mission

   • The script generates BOOTSTRAP_MISSION.md (or SETUP_MISSION.md if dynamic setup is disabled) in your root.
   • Copy its content and paste it into your AI Agent's chat window.
   • This aligns the agent with your project context immediately.

3. Git Tracking

   • If you want to keep the initial structure of agents/memory/ and project/ in your repository but prevent Git from tracking the continuous autonomous modifications your agents will make to them locally, run:

     git ls-files agents/memory/ project/ | xargs git update-index --skip-worktree

Manual Setup:

cp -r GABBE/agents .
chmod +x agents/scripts/setup-context.sh && agents/scripts/setup-context.sh

Install as a Claude Code plugin

The skills also ship as Claude Code plugins in one marketplace, so Claude Code can use them without installing the kit into the project:

claude plugin marketplace add andreibesleaga/GABBE
claude plugin install gabbe@gabbe

gabbe holds the core skills (every skill in agents/skills/core/ and every skill marked core: true). The other skills come as topic plugins from the same marketplace: gabbe-ai, gabbe-architecture, gabbe-brain, gabbe-coding, gabbe-coordination, gabbe-data, gabbe-industry, gabbe-ops, gabbe-product and gabbe-security (for example claude plugin install gabbe-security@gabbe). Each installed skill costs a little context (the core plugin about 2,700 tokens per session), so install only the topics you use (details).

The same skills install one by one with the skills CLI (skills.sh):

npx skills add andreibesleaga/GABBE -a claude-code

Plugin skills are namespaced (/gabbe:code-review, /gabbe:debug). The skills CLI instead writes them to .claude/skills/ in the project, where GABBE's code-review and debug take the place of Claude Code's built-in /code-review and /debug in that project; skip those two there if you want the built-ins.

The plugins are content only (Markdown skills and the templates they name: no hooks, no MCP servers, no executables). They are generated from agents/skills/; after editing a skill run python scripts/build_claude_plugin.py (CI checks the tree is in sync).

🌍 Cross-Platform Support

• Linux / macOS / WSL: Native support.
• Windows (Native):
  • Use python scripts/init.py (Symlinks automatically fallback to file copies if needed).
  • Use agents/scripts/setup-context.ps1 instead of .sh.

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🚀 Common Actions (Copy-Paste Prompts)

Strategy & Ideation (Step 0)

"Use business-case/strategy skills to validate exactly why we are building [description] and who it is for."

New Project from Scratch

"Read AGENTS.md. I want to build [description]. Start with spec-writer skill."

Flow: Strategy → Spec → Design → Tasks → TDD Implementation → Security → Deploy

Resume Existing Project

"Read AGENTS.md and agents/memory/PROJECT_STATE.md. Resume the project."

Fix a Bug

"Read AGENTS.md. Bug: [description]. Use debug skill with TDD."

Flow: Reproduce → Root Cause → Failing Test → Fix → Green → Regression Check

Refactor / Pay Tech Debt

"Use tech-debt skill on [directory]. Then refactor the top-priority item."

Security Audit

"Run security-audit skill on the entire codebase."

Architecture Review

"Run arch-review skill. Check for SOLID violations and coupling."

"Use the performant-nodejs skill to audit the current Node.js architecture for scalability bottlenecks and propose optimizations."

"Use the performant-laravel skill to audit the current Laravel architecture for scalability bottlenecks and propose optimizations."

"Use the performant-python skill to audit the current Python architecture for scalability bottlenecks and propose optimizations."

"Use the performant-go skill to audit the current Go architecture for scalability bottlenecks and propose optimizations."

"Use the performant-ai skill to audit the current AI/LLM architecture for latency and cost bottlenecks."

"Use the time-complexity skill to scan src/ for Big-O complexity hotspots and identify functions worse than O(n)."

Software Engineering & System Architecture

"Act as a Principal Staff Engineer. Review the codebase in [directory] and generate a C4 system architecture diagram (Context and Container levels). Identify any bottlenecks and propose scaling strategies."

"Use the visual-whiteboarding skill. Connect to the Draw.io/Miro MCP and generate a visual spatial architecture diagram for the current microservice layout."

"Use the design-patterns and domain-model skills. We are building a [feature segment]. Propose the optimum architecture pattern (e.g. Event-driven, CQRS, Hexagonal) and define the core domain entities."

Vibe-Coding (Creative Frontend)

"Use the vibe-coding skill. Build a [component/page] using [framework]. I want it to feel [aesthetic, e.g. glassmorphism, cyberpunk, sleek corporate]. Include micro-animations and smooth transitions. Prioritize visual WOW over generic utility."

Activate Brain Mode (Complex Goals)

"Activate Brain Mode. Goal: [build X / migrate Y / solve Z]."

Uses Active Inference to plan, route between local/remote models, and learn from past outcomes.

Activate Loki Mode (Large Projects)

Using Pure Agent Mode (No CLI):

"Activate agents/skills/brain/loki-mode.skill.md. Goal: [build X]. Do not ask me for permission unless you hit a mandatory Human Approval Gate or a task requires True A2A Delegation."

Multi-agent swarm with 30+ specialized personas for projects >5 features or >20 files.

---

End-to-End Workflow & Architecture

Visual Overview (Mermaid)

graph TD
    %% Phase 1: Setup
    subgraph Setup [1. Setup Phase]
        Start([Start]) --> Init[Run init.py]
        Init --> Mission[Feed BOOTSTRAP_MISSION.md or SETUP_MISSION.md]
        Mission --> Config[Edit AGENTS.md]
    end

    %% Phase 2: Definition
    subgraph Definition [2. Definition Phase]
        Config --> Spec[Trigger: spec-writer.skill]
        Spec --> PRD[Artifact: PRD.md]
        PRD --> Review1{Human Approve?}
        Review1 -- No --> Spec
    end

    %% Phase 3: Design
    subgraph Design [3. Design Phase]
        Review1 -- Yes --> Plan[Trigger: arch-design.skill]
        Plan --> Arch[Artifact: PLAN.md + C4]
        Arch --> Review2{Human Approve?}
        Review2 -- No --> Plan
    end

    %% Phase 4: Execution
    subgraph Execution [4. Execution Loop]
        Review2 -- Yes --> Tasks[Trigger: Decompose project/TASKS.md]
        Tasks --> LoopCheck{Tasks Remaining?}
        LoopCheck -- Yes --> Pick[Pick Task]
        Pick --> TDD[Trigger: tdd-cycle.skill]
        TDD --> Red[Test Fails]
        Red --> Green[Implement Pass]
        Green --> Refactor[Refactor]
        Refactor --> Verify[Audit Log]
        Verify --> LoopCheck
    end

    %% Phase 5: Delivery
    subgraph Delivery [5. Delivery Phase]
        LoopCheck -- No --> Integrity[Trigger: integrity-check.skill]
        Integrity --> Security[Trigger: security-audit.skill]
        Security --> HumanRev{Human Review?}
        HumanRev -- No --> Fix[Fix Issues]
        Fix --> Integrity
        HumanRev -- Yes --> Deploy[Deploy]
        Deploy --> End([Done])
    end

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Text Overview (ASCII)

[START]
   |
[INSTALL] python3 scripts/init.py -> Generates BOOTSTRAP_MISSION.md (or SETUP_MISSION.md)
   |
[SETUP]   Feed Mission to Agent -> Edit AGENTS.md (Stack/Rules)
   |
[DEFINE]  "Start new feature" -> spec-writer.skill -> PRD.md
   |      (Human Reviews & Approves PRD)
   v
[DESIGN]  Plan Architecture -> arch-design.skill -> PLAN.md + ADRs
   |      (Human Reviews & Approves Plan)
   v
[TASKS]   Decompose to project/TASKS.md (Atomic 15-min units)
   |
   +---> [IMPLEMENTATION LOOP] ----------------------------------+
   |     1. Pick Task from project/TASKS.md                               |
   |     2. Write Failing Test (Red)                              |
   |     3. Write Code to Pass (Green)                            |
   |     4. Refactor & Clean Up                                   |
   |     5. Verify (Tests + Lint) & Log to AUDIT_LOG.md           |
   |     (Repeat until project/TASKS.md is empty)                         |
   +-------------------------------------------------------------+
   |
[VERIFY]  integrity-check.skill -> security-audit.skill
   |      (Human Final Review)
   v
[DEPLOY]  Merge PR -> Staging -> Production -> [DONE]

---

System Architecture

How the pieces fit together to create a "Cognitive Entity".

Visual Architecture (Mermaid)

graph TB
    subgraph Human ["User (Steering Wheel)"]
        H1[Strategy & Goals]
        H2[Review & Approval]
    end

    subgraph Agent ["Agent / Brain (Engine)"]
        B1[Active Inference Loop]
        B2[Task Router]
    end

    subgraph Context ["Project Context"]
        C1[AGENTS.md]
        C2[CONSTITUTION.md]
    end

    subgraph Tools ["Capability Layer"]
        S[214 Skills]
        T[100 Templates]
        G[86 Guides]
    end

    subgraph Memory ["Memory System"]
        M1[Working Memory]
        M2["Episodic (Logs)"]
        M3["Semantic (Facts)"]
    end

    H1 --> B1
    C1 --> B1
    B1 --> B2
    B2 --> S
    S --> T
    S --> M2
    M3 --> B1
    S --> H2
    H2 -- Feedback --> B1

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Text Architecture (ASCII)

       [HUMAN USER]
            | (Goal/Feedback)
            v
    +-------------------+      +------------------+
    |   AGENT BRAIN     | <--- |  PROJECT CONTEXT |
    | (Active Inference)|      | (AGENTS/Rules)   |
    +--------+----------+      +------------------+
             |
             v
    [ROUTER & ORCHESTRATOR]
             |
    +--------+--------------------------+
    |                                   |
[SKILLS] (Function)               [MEMORY] (Context)
    |                                   |
    +-> [Coding]                  +-> [Episodic Logs]
    +-> [Architecture]            +-> [Semantic Facts]
    +-> [Security]                +-> [Continuity]
    +-> [Ops / SRE]                     |
            |                           |
            v                           v
    [TEMPLATES] (Structured Output) <---+

---

Spec-Driven SDLC Lifecycle

The "Golden Path" for every feature.

Visual SDLC (Mermaid)

flowchart TD
    S0[S00: Strategy] -->|Business Case| S1[S01: Specify]
    
    subgraph Definition
        S1 -->|PRD Draft| Ambiguity{Ambiguous?}
        Ambiguity -- Yes --> Clarify[Clarify Questions]
        Clarify --> S1
        Ambiguity -- No --> S2[S02: Plan]
    end

    subgraph Design
        S2 -->|PLAN.md + C4| Review1{Approved?}
        Review1 -- No --> S2
        Review1 -- Yes --> S3[S04: Tasks]
    end

    subgraph Execution
        S3 -->|project/TASKS.md| Decomp{Task < 15m?}
        Decomp -- No --> S3
        Decomp -- Yes --> S4[S05: Implement]
        
        S4 --> TDD[TDD Cycle]
        TDD --> RARV[RARV: Reason/Act/Reflect/Verify]
        RARV --> Audit[Audit Log]
    end

    Audit --> Done([Feature Complete])

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Text SDLC (ASCII)

0. STRATEGY
   -> Why are we building this? (Value/ROI)
   v
1. SPECIFY (S01)
   -> spec-writer.skill -> PRD.md (EARS Syntax)
   -> (Optional) visual-specs.skill -> Visual Spec Package (UI/Arch from scans)
   -> Human Review & Approval
   v
2. PLAN (S02)
   -> arch-design.skill -> PLAN.md + C4 Diagrams
   -> adr-writer.skill -> Architectural Decisions
   v
3. DECOMPOSE (S03/S04)
   -> project/TASKS.md -> Atomic steps (<15 mins each)
   v
4. IMPLEMENT (S05)
   -> One task at a time
   -> TDD Loop: Red -> Green -> Refactor
   -> RARV Loop: Reason -> Act -> Reflect -> Verify
   v
5. VERIFY & SHIP (S06-S10)
   -> Integrity Check -> Security Audit -> Deploy

6. COGNITIVE ORCHESTRATION & HEALING
   -> gabbe brain activate -> Predict bottlenecks & route complexity
   -> gabbe brain evolve -> Meta-optimize failing skills into Semantic Memory
   -> gabbe brain heal -> Recover from environment/DB corruption

---

Agents Kit Structure Map

agents/
├── AGENTS.md                 # Universal config (edit per project)
├── CONSTITUTION.md           # Immutable project law
├── skills/                   # 214 .skill.md files
│   ├── 00-index.md           # Full skills registry
│   ├── coding/               # tdd, review, debug, refactor, git...
│   ├── architecture/         # arch-design, patterns, api-design...
│   ├── security/             # audit, threat-model, privacy...
│   ├── ops/                  # sre, docker, k8s, deploy, cost...
│   ├── product/              # spec-writer, req-elicitation...
│   ├── core/                 # research, self-heal, lifecycle...
│   ├── data/                 # data-engineering, db-migration
│   ├── coordination/         # multi-agent-orch, agent-protocol
│   └── brain/                # active-inference, consciousness, memory...
├── templates/                # 100 fill-in-the-blank documents
│   ├── 00-index.md           # Full templates registry
│   ├── coding/               # test plans, checklists, devcontainer
│   ├── architecture/         # ADR, C4, domain model, integration
│   ├── security/             # threat model, safety case, ethics
│   ├── ops/                  # incident, deploy, capacity, benchmark
│   ├── product/              # PRD, spec, user story, business case
│   ├── core/                 # plan, tasks, audit log, traceability
│   ├── coordination/         # agent profiles, swarm config
│   └── brain/                # inference loop, episodic memory, OODA
├── guides/                   # 86 language & domain guides
├── personas/                 # 36 specialized agent roles
├── memory/                   # Episodic + semantic + project state
└── scripts/                  # setup-context, validators, state export/import

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🧠 Brain Mode (Meta-Cognitive Layer)

Note

Experimental. Brain Mode, Loki Mode, Active Inference, Evolutionary Prompt Optimization, the self-healing loop, and forecasting are experimental research runtimes. They are gated behind explicit gabbe brain … subcommands (never on by default), covered by the test suite, and deterministically replayable (gabbe replay). See ADR-0002 for the design rationale and docs/VERIFICATION_GUIDE.md for how to reproduce each one. Treat outputs as advisory, not production guarantees.

Sits above Loki. Decides how to execute, not just what to execute.

Feature	Description
Active Inference	Predict → Act → Observe → Compare → Adapt loop
Cost Routing	Simple tasks → local free models, complex → remote SOTA
Episodic Memory	Recalls past project outcomes to avoid repeated mistakes
System 2 Thinking	Strategic planning before execution

These are the behaviours of the Brain Mode skill. The gabbe brain CLI implements a smaller part of them (ADR-0002).

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🔧 Setup by Project Type

JavaScript / TypeScript / Node.js

Guide: guides/languages/js-ts-nodejs.md
Stack: Vitest, Zod, Prisma, Playwright, Hono
Config in AGENTS.md: test_cmd="npx vitest", lint_cmd="npx eslint ."

Go (Golang)

Guide: guides/languages/go-lang.md
Stack: Echo/Gin, Ent, Testify, Testcontainers
Config in AGENTS.md: test_cmd="go test ./...", lint_cmd="golangci-lint run"

PHP / Laravel

Guide: guides/languages/php-laravel.md
Stack: DDD, Actions, Pest PHP, PHPStan L9, Enlightn
Config in AGENTS.md: test_cmd="vendor/bin/pest", lint_cmd="vendor/bin/pint"

Python / FastAPI

Guide: guides/languages/python-fastapi-ai.md
Stack: Pydantic, Pytest, Ruff, FastAPI
Config in AGENTS.md: test_cmd="pytest", lint_cmd="ruff check ."

---

📋 SDLC Phases (10 Gates)

The table below enumerates the 10 SDLC gates (S01–S10). These are bracketed by the Day-0 phase S00 (Strategy & Discovery) and the Day-2 phases S11–S13 (Operate / Evolve / Decommission) that complete the cradle-to-grave ADLC (S00–S13).

Phase	Gate	Key Artifact
S01	Requirements	PRD_TEMPLATE.md (EARS syntax)
S02	Design	ADR_TEMPLATE.md + C4 diagrams
S03	Specification	SPEC_TEMPLATE.md + API contracts
S04	Tasks	TASKS_TEMPLATE.md (15-min rule)
S05	Implementation	TDD Red→Green→Refactor + RARV
S06	Testing	Unit >99% + integration + E2E
S07	Security	SECURITY_CHECKLIST.md + audit
S08	Review	Human code review
S09	Staging	Smoke tests + benchmarks
S10	Production	Rollback plan + monitoring

---

🛠️ Skills Summary (by Category)

Category	Count	Key Skills
Coding	10+	tdd-cycle, debug, refactor, code-review, git-workflow
Architecture	15+	arch-design, microservices, systems-architecture, system-scalability, blockchain-dlt
Operations	15+	reliability-sre, production-health, dev-environments, cost-optimization
Security	15+	security-audit, secure-architecture, privacy-data-protection, api-security
Product	10+	spec-writer, req-elicitation, visual-specs, green-software
Core	10+	research, self-heal, knowledge-gap, meta-optimize
Data	5+	data-engineering, db-migration, semantic-web
Coordination	5+	multi-agent-orch, agent-protocol
Brain	10+	active-inference, consciousness-loop, cost-benefit-router
AI/Swarm	5+	multi-agent-systems, agent-communication, beyond-llms
Industry	5+	healthcare-fhir, telecom-networks, industrial-iot, global-standards, engineering-standards
Loki Modes	2+	brain-mode, loki-mode

Full catalog: agents/skills/00-index.md

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📝 Templates Summary (by Category)

Category	Count	Examples
Coding	5+	Clean Code Checklist, Test Plan, E2E Suite
Architecture	10+	ADR, C4, Domain Model, Scalability Plan, Smart Contract
Ops	5+	Incident Postmortem, Deploy Config, Capacity Plan, FinOps
Security	5+	Threat Model, Safety Case, Ethical Impact
Product	10+	PRD, Spec, User Story Map, Visual Spec, Green Software
Core	5+	Plan, Tasks, Audit Log, Project State, Continuity
Coordination	5+	Agent Profile, Swarm Config, Handshake, Swarm Architecture
Brain	5+	Active Inference Loop, Episodic Memory, OODA Trace
Data	3	Data Pipeline, Database Schema, OWL Ontology
Industry	5+	FHIR Interop, IoT Telemetry, Telecom API, Global SDLC Audit

Full catalog: agents/templates/00-index.md

---

🔌 Essential MCP Servers

Server	Purpose
Context-7	Up-to-date SDK docs (prevents hallucination)
Sequential Thinking	Chain-of-thought reasoning
GitHub MCP	PR review, code search
GitHits	Navigate open-source dependency source/docs (no clone, no key)
PostgreSQL MCP	Live schema introspection
Playwright MCP	Browser automation / visual TDD
Brave Search	Authoritative web research
Time Complexity	Local Big-O static analysis via tree-sitter
Excalidraw	Programmatic Excalidraw diagram creation

Config: templates/core/MCP_CONFIG_TEMPLATE.json · Full guide: MCP_CONFIGURATIONS.md

---

🔄 Self-Healing Loop

Task → Knowledge gap? → research.skill → Execute → Verify
                                          ↓ FAIL
                                   self-heal.skill (max 5×)
                                          ↓ STILL FAIL
                                   Human escalation report

---

📊 Memory Architecture

Layer	Location	Purpose
Project State	agents/memory/PROJECT_STATE.md	Current SDLC phase
Audit Log	agents/memory/AUDIT_LOG.md	Append-only decision history
Continuity	agents/memory/CONTINUITY.md	Past failures (read every session)
Episodic	agents/memory/episodic/	Per-session decision logs
Semantic	agents/memory/semantic/	Crystallized project knowledge

---

🚨 Troubleshooting

Problem	Fix
Agent ignores AGENTS.md	Run agents/scripts/setup-context.sh to create symlinks
Agent repeats mistakes	Check CONTINUITY.md — tell agent to read it
Tests pass immediately	False positive — test is broken, fix it first
Uses deprecated APIs	Activate Context-7 MCP
Session lost	"Use session-resume skill to load all memory"
Context too large	Use context_cost: low skills or activate Loki Mode

---

📚 Guides by Stack

Stack	Guide
JS/TS/Node.js	guides/languages/js-ts-nodejs.md
Node.js Advanced & TS	guides/languages/nodejs-advanced.md
Go	guides/languages/go-lang.md
PHP/Laravel	guides/languages/php-laravel.md
Python/FastAPI	guides/languages/python-fastapi-ai.md
SQL/NoSQL	guides/data/sql-nosql.md
Microservices	guides/architecture/microservices.md
Architecture	guides/architecture/systems-architecture.md
Testing	guides/principles/testing-strategy.md
Design Patterns	guides/patterns/design-patterns.md
Agentic AI	guides/ai/ai-agentic.md
Multi-Agent Systems	guides/ai/multi-agent-systems.md
Security/Compliance	guides/ops/compliance-audit.md
API Security	guides/security/api-security.md
Cryptography	guides/security/cryptography-standards.md
Data Protection	guides/security/privacy-data-protection.md
Secure Arch	guides/security/secure-architecture.md
Threat Modeling	guides/security/threat-modeling.md
DevOps/Environments	guides/ops/dev-environments.md
Developer Workflow	guides/ops/dev-workflow.md
Legacy/COBOL	guides/principles/legacy-tech.md
Future Tech 2030	guides/principles/future-tech.md
C-Sharp / .NET	guides/languages/c-sharp.md
Self-Healing	guides/ai/self-healing-summary.md
Time Complexity	guides/patterns/time-complexity-analysis.md
Troubleshooting	guides/ops/troubleshooting-guide.md
Enterprise Migration	guides/patterns/enterprise-migration-scenario.md

---

🛡️ Security & Guardrails

All 214 skills in the GABBE kit feature a heavily enforced "Security & Guardrails" section that binds agents to the project's CONSTITUTION.md. The 3-layer security constraints include:

1. Skill Security: Tool-specific protection (e.g., preventing command injection or sandbox escapes).
2. System Integration Security: Safe external integration (e.g., verifying boundary enforcement or ensuring test coverage).
3. LLM/Agent Guardrails: Protection from AI-specific failures (e.g., hallucinated metrics, confirmation bias, or prompt injection).

Agents are explicitly configured to Fail-Closed—they must wait for human approval rather than bypassing a guardrail.

---

🚀 GABBE CLI (Experimental)

GABBE also has an optional, experimental Python CLI (gabbe) that keeps project state in Markdown files plus a SQLite database. The kit works fully without it.

Prerequisites

• Python 3.9+
• LLM API Key: For Brain/Route features, set GABBE_API_KEY (OpenAI-compatible).

Environment Variables (full reference in CLI_REFERENCE.md):

Variable	Default	Description
GABBE_API_URL	https://api.openai.com/v1/chat/completions	OpenAI-compatible endpoint
GABBE_API_KEY	(required for LLM features)	Bearer token for the LLM API
GABBE_API_MODEL	gpt-4o	Model name sent in API requests
GABBE_LLM_TEMPERATURE	0.7	Sampling temperature (0.0–1.0)
GABBE_LLM_TIMEOUT	30	HTTP timeout in seconds
GABBE_LLM_MAX_RETRIES	3	Number of LLM retry attempts on transient errors
GABBE_LLM_CACHE	false	Opt-in: cache identical LLM calls locally (0 tokens on a hit; only for deterministic calls)
GABBE_LOG_LEVEL	INFO	Logging verbosity (DEBUG, INFO, WARNING, ERROR)
GABBE_ROUTE_THRESHOLD	50	Complexity score above which prompts route REMOTE
GABBE_MAX_COST_USD	5.0	Maximum cost (USD) budget per run
GABBE_MAX_TOKENS_PER_RUN	100000	Maximum token limit per run
GABBE_MAX_TOOL_CALLS_PER_RUN	50	Maximum tool calls allowed per run
GABBE_MAX_ITERATIONS	25	Maximum active-inference iterations per run
GABBE_MAX_WALL_TIME	300	Maximum wall-clock time (seconds) per run
GABBE_MAX_RECURSION_DEPTH	5	Maximum agent recursion depth
GABBE_MAX_RETRIES_PER_TOOL	3	Maximum retries for a single tool call
GABBE_POLICY_FILE	project/policies.yml	Path to the YAML policy file
GABBE_ESCALATION_MODE	cli	Escalation mode: cli, file, or silent
GABBE_SUBPROCESS_TIMEOUT	300	Timeout (seconds) for verify sub-commands
GABBE_OTEL_ENABLED	false	Enable OpenTelemetry tracing

Installation

The CLI is a Python package, published on PyPI as gabbe.

# 1a. Install from PyPI (recommended)
pipx install gabbe          # or: pip install gabbe  /  uvx gabbe

# 1b. …or install locally from a checkout (for development)
pip install -e .

# 2. Verify installation
gabbe --help

Core Commands

Command	Description
gabbe init	Initialize the SQLite Database (Run this after python scripts/init.py).
gabbe sync	Hybrid Sync: Bidirectional sync between project/TASKS.md and SQLite DB.
gabbe verify	Enforcer: programmable integrity check (files, tests, lint).
gabbe status	Dashboard: Visualizes project phase and task progress.
gabbe brain	Meta-Cognition: Activates Active Inference loop or Evolutionary Prompt Optimization (Requires API Key).
gabbe route	Cost Router: Arbitrates between Local and Remote LLMs based on task complexity (prompts of 100+ characters or with code blocks are rated by the LLM and need GABBE_API_KEY; without a key a fallback heuristic is used).
gabbe forecast	Strategic Forecast: Projects remaining work cost and tokens based on historical run data.
gabbe serve-mcp	MCP Gateway: Zero-dependency JSON-RPC Model Context Protocol server for standalone agents to access tools safely.
gabbe runs	Run History: List recent agent runs with status, cost, and timestamps.
gabbe audit <run-id>	Audit Trace: Display structured span-level trace for a past run (--format json|table).
gabbe replay <run-id>	Deterministic Replay: Replay a past run from its checkpoints (--from-step N).
gabbe resume <run-id>	Escalation Resume: Approve or reject pending escalations for a paused run.
gabbe registry publish	Publish Skills: Export the kit's skills as a publish-ready agentskills.io bundle (manifest + agent-card) for universal registries.
gabbe registry add <source>	Import Skills: Draw an external skill/bundle (path, .tar.gz, or URL) — validated + security-scanned + landed namespaced for review.
gabbe setup	Install Wizard: Run the interactive installer to wire the kit into your coding agents (see also npx gabbe-kit init).
gabbe doctor	Autodetect: OS/arch, runtimes, installed agents + post-install MCP next-steps.
gabbe eval	Skill evals: deterministic self-check; --live scores skill outputs via the model.
gabbe update / gabbe uninstall	Reversible install: additive refresh / manifest-backed removal (--dry-run, --purge, --global, --dir).

Platform Control Layer

The experimental gabbe CLI supports a platform control layer. It covers budget enforcement, cost and token controls, hard stops, policy rules, the tool gateway, audit tracing, human escalation, and deterministic replay. Detailed documentation is available in PLATFORM_CONTROLS.md.

Architecture

GABBE uses a Hybrid Architecture where agents and humans interact via Markdown, but the system of record is SQLite.

graph TD
    subgraph User["User (Legacy Flow)"]
        Edit[Edit project/TASKS.md]
    end

    subgraph CLI["GABBE CLI (pip installed)"]
        Sync[gabbe sync]
        Verify[gabbe verify]
        Brain[gabbe brain]
        Router[gabbe route]
        Forecast[gabbe forecast]
        MCP[gabbe serve-mcp]
    end

    subgraph Storage["Hybrid Memory"]
        MD[Markdown Files]
        DB[(SQLite state.db)]
    end

    User -->|Manual Edits| MD
    MD <-->|Bi-Directional| Sync
    Sync <--> DB
    Brain -->|Read/Write| DB
    Verify -->|Check| MD
    Verify -->|Check| DB
    Forecast -->|Analyze| DB
    MCP -->|Write Telemetry| DB

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How to Use

Setup

# 1. Generate Context Configs
python3 scripts/init.py

# 2. Initialize Database
gabbe init

Daily Workflow

# Check status
gabbe status

# Sync tasks (manual edits)
gabbe sync

# Optimize a skill (Requires GABBE_API_KEY)
gabbe brain evolve --skill tdd-cycle

Verification

gabbe verify

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License

GABBE is dual-licensed so the executable engine and the knowledge content can each use the license appropriate to it:

Part	License	Files
Code	Apache-2.0	gabbe/, scripts/, agents/scripts/ (all .py)
Content	CC BY-SA 4.0	agents/ Markdown (skills, templates, guides, personas), docs/

SPDX expression: Apache-2.0 AND CC-BY-SA-4.0. Apache-2.0 adds an explicit patent grant and is OSI-approved, making the CLI safe to embed in downstream (including proprietary) projects; the curated Markdown corpus stays copyleft under CC BY-SA 4.0. Existing users lose no rights — this is purely additive to the prior CC-BY-SA-4.0-only declaration.

© 2026 Andrei Nicolae Besleaga.