logos-dev-boost/docs/spec.md

logos-dev-boost

Overall Description

logos-dev-boost is a developer acceleration tool for the Logos modular application platform. It provides AI coding agents and human developers with accurate, always-available knowledge of the Logos SDK, build system, module architecture, and development workflows.

The tool solves a fundamental problem: AI agents (Claude Code, Cursor, Copilot, Codex) have no training data about the Logos ecosystem. They hallucinate APIs, use wrong build commands, generate Qt-dependent code where pure C++ is required, and cannot navigate the multi-repo architecture. Human developers face a similar but smaller-scale problem — the onboarding path from "I want to build a Logos module" to a working, packaged, tested plugin is steep.

logos-dev-boost addresses this by operating at three levels:

1. Always-loaded context — AGENTS.md / CLAUDE.md files with a compressed documentation index. Loaded automatically at session start. Based on Next.js research showing 100% AI eval pass rate with bundled docs versus 79% with skills-only approaches.
2. On-demand skills — Detailed, step-by-step task guides that activate when the agent works on a specific task. Follows the Agent Skills specification for cross-tool compatibility.
3. MCP server — Live project introspection tools (project info, documentation search, API reference, build help, scaffolding) via the Model Context Protocol.

Definitions & Acronyms

Term	Definition
Universal Module	A Logos module whose implementation is pure C++ (no Qt types). All Qt glue is generated at build time by logos-cpp-generator --from-header. Identified by "interface": "universal" in metadata.json.
UI App	A QML-based UI component displayed as a tab in Basecamp's MDI workspace. Either pure QML (calls backend modules via logos.callModule()) or QML + process-isolated C++ backend (Qt Remote Objects). Identified by "type": "ui_qml" in metadata.json.
LIDL	Logos Interface Definition Language — a lightweight DSL for declaring module interfaces. Alternative to the --from-header C++ parser path. Both produce identical generated output.
Provider Glue	Generated code (_qt_glue.h, _dispatch.cpp) that wraps a pure C++ implementation class in a LogosProviderObject with callMethod() dispatch and getMethods() introspection.
Client Stub	Generated type-safe C++ wrapper class that callers use to invoke a module's methods without string-based dispatch.
LogosAPI	The runtime API that modules use to call other modules. Provides callModule(name, method, args) which returns a LogosResult.
LogosResult	Structured return type for cross-module calls. Contains success(), data() (QVariant), and errorMessage().
LGX	Logos Package Format — gzip tar archives with platform-specific variants for distributing modules and UI apps.
logoscore	Headless CLI runtime that loads modules and optionally calls their methods. Used for testing modules without the full GUI.
logos_host	Per-module host process spawned by liblogos_core. Each module runs in isolation, communicating via Qt Remote Objects IPC.
MCP	Model Context Protocol — open standard for AI agent tool integration. logos-dev-boost exposes tools via MCP's stdio transport.
Agent Skill	A portable knowledge module (SKILL.md + optional assets) that AI agents activate on demand. Follows the agentskills.io specification.

Domain Model

Two Component Types

This distinction is fundamental to the entire Logos ecosystem and to everything logos-dev-boost teaches:

Logos Modules (core) are process-isolated backend services. The developer writes a plain C++ implementation class using standard types (std::string, int64_t, std::vector<T>, bool). No Qt types appear in user code. The build system runs logos-cpp-generator --from-header to generate all Qt glue: the plugin class, method dispatch table, and introspection metadata. Modules are loaded by logoscore (headless) or logos-basecamp (GUI) via liblogos_core. Each runs in its own isolated logos_host process and communicates via Qt Remote Objects IPC.

Reference implementation: logos-accounts-module — metadata.json has "interface": "universal", src/accounts_module_impl.h is pure C++, flake.nix runs the code generator in preConfigure.

UI Apps ("type": "ui_qml") are QML-based UI components displayed as tabs in Basecamp's MDI workspace. Two subtypes exist: pure QML apps (no C++, call backend modules via logos.callModule()) and QML + C++ backend apps (process-isolated C++ backend communicating via Qt Remote Objects, QML gets a typed replica via logos.module()).

                   Logos Module (universal)             UI App (ui_qml)
                   ─────────────────────────            ──────────────────────────
User writes:       Pure C++ impl header                 QML (pure) or QML + .rep + C++ plugin
                   (std::string, int64_t, etc.)         (Qt types OK in backend)

Generated:         Qt glue, dispatch, plugin class      QTRO source/replica (from .rep)
                   (logos-cpp-generator --from-header)

metadata.json:     "interface": "universal"             "type": "ui_qml"
                   "type": "core"                       "view": "Main.qml"

Loaded by:         logoscore / liblogos_core            Basecamp / standalone runner
Runs in:           Isolated logos_host process           QML in-process, C++ backend in logos_host
Has UI:            No                                   Yes (tab in MDI workspace)

Universal Module Type System

The code generator maps C++ standard types to LIDL types to Qt types:

C++ type	LIDL type	Qt type
std::string / const std::string&	tstr	QString
bool	bool	bool
int64_t	int	int
uint64_t	uint	int
double	float64	double
void	void	void
std::vector<std::string>	[tstr]	QStringList
std::vector<uint8_t>	bstr	QByteArray
std::vector<int64_t>	[int]	QVariantList
std::vector<bool>	[bool]	QVariantList
LogosMap	{tstr: any}	QVariantMap
LogosList	[any]	QVariantList

Module authors only work with the C++ column. The generator handles everything else. LogosMap/LogosList (from <logos_json.h>) are nlohmann::json aliases for returning rich structured data while keeping the impl Qt-free.

How logos-dev-boost Layers Work Together

Layer 1: AGENTS.md / CLAUDE.md        Always loaded at session start
         (compressed docs index)       Every AI tool reads these automatically
                │
Layer 2: Guidelines                    Loaded into AGENTS.md content
         (core, universal-module,      Conventions the agent must always follow
          ui-app, nix-build, etc.)
                │
Layer 3: Skills                        Activated on demand by the AI agent
         (create-module, package,      Detailed step-by-step task guides
          test, wrap-lib, etc.)
                │
Layer 4: MCP Server                    Called by the agent when it needs live data
         (project-info, search-docs,   Parses the actual project on disk
          api-reference, build-help)
                │
Layer 5: Scaffolding                   Generates new projects from templates
         (init command, templates)     Pre-configured with correct AI context

User/Agent Journeys

Journey 1: Create a Universal C++ Module

The primary journey. A developer (or AI agent) creates a pure C++ module with no Qt in user code.

Step 1: Scaffold the project

nix run github:logos-co/logos-dev-boost -- init crypto_utils --type module

Or tell an AI agent in an empty directory: "create a new Logos module called crypto_utils that provides hashing utilities"

The create-universal-module skill activates. Output:

crypto_utils/
├── src/
│   ├── crypto_utils_impl.h       # Pure C++ class (std::string, bool, etc.)
│   └── crypto_utils_impl.cpp     # Implementation stubs
├── metadata.json                 # "interface": "universal", "type": "core"
├── CMakeLists.txt                # logos_module() with generated_code sources
├── flake.nix                     # preConfigure runs logos-cpp-generator --from-header
├── tests/
│   ├── main.cpp                  # LOGOS_TEST_MAIN() entry point
│   ├── test_crypto_utils.cpp     # Unit tests using LOGOS_TEST() and assertions
│   └── CMakeLists.txt            # logos_test() macro (auto-detected by builder)
├── CLAUDE.md                     # Generated: knows this is a universal module
├── AGENTS.md                     # Universal context for any AI tool
└── .mcp.json                     # MCP server registration

Step 2: Implement business logic in pure C++

#pragma once
#include <string>
#include <vector>
#include <cstdint>

class CryptoUtilsImpl {
public:
    std::string hash(const std::string& input);
    bool verify(const std::string& input, const std::string& hash);
    std::string generateKey(int64_t bits);
    std::vector<std::string> listAlgorithms();
};

No Q_OBJECT, no Q_INVOKABLE, no QString. The code generator handles all Qt integration at build time.

Step 3: Build

nix build

The generator runs automatically via preConfigure in flake.nix:

logos-cpp-generator --from-header src/crypto_utils_impl.h \
  --backend qt \
  --impl-class CryptoUtilsImpl \
  --impl-header crypto_utils_impl.h \
  --metadata metadata.json \
  --output-dir ./generated_code

This produces generated_code/crypto_utils_qt_glue.h and generated_code/crypto_utils_dispatch.cpp containing the Qt plugin class, method dispatch, and introspection metadata.

Step 4: Test with logoscore

logoscore -m ./result/lib -l crypto_utils \
  -c "crypto_utils.hash(hello_world)"

Step 5: Unit test (no logoscore needed)

nix build .#unit-tests -L

Unit tests use logos-test-framework (LOGOS_TEST() macros, LOGOS_ASSERT_*) and instantiate CryptoUtilsImpl directly — it is a plain C++ class with no framework dependencies. logos-module-builder auto-detects tests/CMakeLists.txt and creates the unit-tests target.

Step 6: Inter-module communication

Other modules call crypto_utils via LogosAPI:

LogosResult result = api->callModule("crypto_utils", "hash", {"hello"});
if (result.success()) {
    std::string hashValue = result.data().toString().toStdString();
}

Step 7: Package for distribution

lgx create crypto_utils
lgx add crypto_utils.lgx -v linux-x86_64 -f ./result/lib/crypto_utils_plugin.so
lgx add crypto_utils.lgx -v darwin-arm64 -f ./result/lib/crypto_utils_plugin.dylib
lgx verify crypto_utils.lgx

What logos-dev-boost provides at each step:

• Step 1: init command scaffolds from universal module template; generated CLAUDE.md/AGENTS.md teach agents the universal pattern
• Step 2: Guidelines ensure pure C++, no Qt types; the type mapping table is always available
• Step 3: Build help explains the codegen pipeline; troubleshooting for common generator errors
• Steps 4-5: Testing skill covers logos-test-framework unit tests (LOGOS_TEST, LogosTestContext, mocking) and logoscore integration tests
• Step 6: Inter-module comm skill explains LogosAPI patterns and dependency declaration
• Step 7: Packaging skill covers the full LGX workflow

Journey 2: Wrap an External C/C++ Library as a Module

Like logos-accounts-module wrapping go-wallet-sdk, or a module wrapping libsodium.

Step 1: Scaffold with external lib flag

nix run github:logos-co/logos-dev-boost -- init sodium_module --type module --external-lib

Output includes lib/ directory structure and metadata.json with "nix.external_libraries" pre-configured.

Step 2: Configure the external library in metadata.json

{
  "nix": {
    "external_libraries": [{
      "name": "libsodium",
      "build_command": "make",
      "output_pattern": "build/libsodium.*"
    }]
  }
}

Step 3: Write impl header wrapping the C API

#pragma once
#include <string>
#include <vector>

extern "C" {
    #include "lib/sodium.h"
}

class SodiumModuleImpl {
public:
    std::string encrypt(const std::string& plaintext, const std::string& key);
    std::string decrypt(const std::string& ciphertext, const std::string& key);
    std::string generateKey();
};

The external C API is accessed via extern "C" includes. The impl class presents a clean C++ interface that the generator can process.

Steps 4+: Same as Journey 1 (build, test, package).

Journey 3a: Create a Pure QML UI App

A Basecamp UI App with no C++ — QML only, calls backend modules via logos.callModule().

Step 1: Scaffold

nix run github:logos-co/logos-dev-boost -- init notes_ui --type ui-qml

Output:

notes_ui/
├── Main.qml                      # QML entry point
├── metadata.json                 # "type": "ui_qml", "view": "Main.qml"
├── flake.nix                     # mkLogosQmlModule
├── CLAUDE.md
└── AGENTS.md

Step 2: Develop the QML UI

import QtQuick 2.15
import QtQuick.Controls 2.15

Item {
    Button {
        text: "Save Note"
        onClicked: {
            var result = logos.callModule("storage_module", "save", [noteField.text])
            console.log("Saved:", result)
        }
    }
}

Step 3: Build and run

nix build
nix run .    # standalone app with QML Inspector on localhost:3768

The QML Inspector MCP server starts automatically. AI agents can use qml_screenshot, qml_find_and_click, qml_get_tree, etc. to interact with and verify the UI. Write .mjs test files in tests/ for headless CI testing via nix build .#integration-test.

Journey 3b: Create a QML + C++ Backend UI App

A Basecamp UI App with process-isolated C++ backend and QML frontend.

Step 1: Scaffold

nix run github:logos-co/logos-dev-boost -- init notes_app --type ui-qml-backend

Output:

notes_app/
├── src/
│   ├── notes_app.rep             # Qt Remote Objects interface
│   ├── notes_app_interface.h     # extends PluginInterface
│   ├── notes_app_plugin.h        # SimpleSource + ViewPluginBase
│   ├── notes_app_plugin.cpp      # implementation
│   └── qml/
│       └── Main.qml              # QML frontend (logos.module() replica)
├── metadata.json                 # "type": "ui_qml", "main": "notes_app_plugin"
├── CMakeLists.txt                # REP_FILE
├── flake.nix                     # mkLogosQmlModule
├── CLAUDE.md
└── AGENTS.md

Step 2: Define the backend interface (.rep file)

class NotesApp
{
    PROP(QString status READWRITE)
    PROP(QVariantList notes READWRITE)
    SLOT(void addNote(const QString& title))
    SLOT(void deleteNote(int index))
}

Step 3: Implement the C++ backend

class NotesAppPlugin : public NotesAppSimpleSource,
                       public NotesAppInterface,
                       public NotesAppViewPluginBase
{
    Q_OBJECT
    Q_PLUGIN_METADATA(IID NotesAppInterface_iid FILE "metadata.json")
    Q_INTERFACES(NotesAppInterface)

public:
    Q_INVOKABLE void initLogos(LogosAPI* api) {
        m_logosAPI = api;
        setBackend(this);
    }

    void addNote(const QString& title) override { /* ... */ }
    void deleteNote(int index) override { /* ... */ }
};

Step 4: Develop the QML frontend

import QtQuick
import QtQuick.Controls

Item {
    id: root

    readonly property var backend: logos.module("notes_app")
    property bool ready: false

    Connections {
        target: logos
        function onViewModuleReadyChanged(moduleName, isReady) {
            if (moduleName === "notes_app")
                root.ready = isReady && root.backend !== null;
        }
    }
    Component.onCompleted: {
        root.ready = root.backend !== null && logos.isViewModuleReady("notes_app");
    }

    ListView {
        model: backend ? backend.notes : []
        delegate: Text { text: modelData.title }
    }

    Button {
        text: "Add Note"
        enabled: root.ready
        onClicked: logos.watch(backend.addNote("New Note"),
            function() { console.log("Added") },
            function(err) { console.log("Error:", err) }
        )
    }
}

Step 5: Build and test

nix build
nix run .    # standalone app with QML Inspector on localhost:3768

AI agents can test the running UI via MCP tools (qml_screenshot, qml_find_and_click, etc.). Write .mjs test files in tests/ for headless CI via nix build .#integration-test.

The C++/QML boundary (taught by guidelines):

Concern	Goes in C++	Goes in QML
Data models, state	PROP() in .rep file	Bind to backend.property
Business logic	SLOT() in .rep + implement in plugin	Never — no JS business logic
Module calls	LogosAPI* in initLogos()	logos.callModule() (pure QML only)
File I/O, networking	Always C++	Never
UI layout, styling	Never	Always — Logos.Theme, Logos.Controls
User interactions	SLOT() methods	logos.watch(backend.doX())
Plugin lifecycle	initLogos() + setBackend(this)	N/A

Journey 4: AI Agent Building a Module from Scratch

What happens when a developer tells an AI agent "create a module that provides encryption utilities":

1. Agent reads AGENTS.md (always loaded) — knows about universal interface, Logos ecosystem, type system, build pipeline. This is the critical difference from not having logos-dev-boost.

2. Agent activates create-universal-module skill — gets step-by-step template with correct file structure, metadata.json schema, flake.nix pattern with preConfigure.

3. Agent writes pure C++ impl header — guidelines ensure it uses std::string not QString, int64_t not int, returns meaningful types from the type mapping table.

4. Agent writes flake.nix — skill provides exact template with logos-cpp-generator --from-header in preConfigure and correct logos-module-builder input.

5. Agent builds with nix build — build-help guidelines explain the pipeline. If errors occur, agent knows common fixes: generator type mapping issues, missing find_package, metadata.json/header class name mismatch.

6. Agent runs unit tests with nix build .#unit-tests -L — the scaffolded tests/ directory uses logos-test-framework (LOGOS_TEST(), LOGOS_ASSERT_*). Tests are auto-detected by logos-module-builder. Agent also tests with logoscore for integration testing — testing skill provides exact commands and expected output patterns.

Without logos-dev-boost: Agent would write Q_INVOKABLE methods, use QString everywhere, try cmake --build instead of nix build, hallucinate a LogosPlugin base class that doesn't exist, and have no idea about the code generator pipeline.

Journey 5: Installing logos-dev-boost for an Existing Project

For a developer with an existing Logos module who wants AI assistance:

Step 1: Run the installer

nix run github:logos-co/logos-dev-boost -- install

Step 2: Interactive configuration

Detected: Universal C++ module (accounts_module)
SDK version: logos-cpp-sdk 0.3.0

Which AI tools do you use?
  [x] Claude Code
  [x] Cursor
  [ ] Codex
  [ ] Gemini CLI

Generated:
  CLAUDE.md           (always-loaded context for Claude Code)
  AGENTS.md           (universal context for any AI tool)
  .cursor/rules/logos.mdc  (Cursor-specific rules)
  .claude/skills/     (8 skills for Claude Code)
  .mcp.json           (MCP server registration)
  .logos-dev-boost/   (pre-built MCP server binary)

Step 3: AI tools auto-detect configuration

• Claude Code reads CLAUDE.md automatically, discovers .claude/skills/, connects to MCP server via .mcp.json
• Cursor reads AGENTS.md automatically, loads .cursor/rules/logos.mdc, connects to MCP server
• Manual fallback if auto-detection fails:
  • Claude Code: claude mcp add -s local -t stdio logos-dev-boost node .logos-dev-boost/mcp-server/index.js
  • Cursor: Command Palette -> "/open MCP Settings" -> toggle on logos-dev-boost
  • Codex: codex mcp add logos-dev-boost -- node .logos-dev-boost/mcp-server/index.js

Features & Requirements

Phase 1: Foundation (MVP)

• Always-loaded context files (AGENTS.md, CLAUDE.md) with compressed documentation index
• 7 guideline files covering core conventions, universal modules, UI apps, Nix build, testing, metadata.json, and code generation
• 8 on-demand skills for common development tasks
• Scaffolding templates for universal modules, external library modules, and UI apps
• Context file generators (AGENTS.md, CLAUDE.md, .cursor/rules, llms.txt)
• Nix flake with init, install, and generate commands

Phase 2: MCP Server

• Live project introspection via 5 MCP tools (project-info, search-docs, api-reference, build-help, scaffold)
• Full-text documentation search over bundled docs
• Context-aware build commands with troubleshooting
• Interactive installer that detects AI tools and generates per-tool configuration

Phase 3: Rich Features

• Semantic documentation search with local ONNX embeddings
• Cross-repo dependency graph tool
• LIDL language validation and preview
• Integration with logos-qt-mcp for combined dev-time and runtime introspection

Phase 4: Ecosystem

• Third-party module skills (module authors ship skills in their repos)
• Hosted documentation API with centralized semantic search
• CI integration (logos-dev-boost check validates project configuration)
• Auto-update for context files when dependencies change

Success Metrics

1. Module creation time — An AI agent can scaffold, build, and test a new universal C++ module in under 5 minutes (currently impossible without deep knowledge)
2. Zero hallucinated APIs — Agents never suggest non-existent Logos APIs or use Qt types in universal module code
3. Build success rate — Agent-generated Nix flakes and C++ impl headers build on first try
4. Correct interface choice — Agents use the universal interface for modules and ui_qml for UI apps, never mixing the two
5. Onboarding time — New human developers can create their first module in under 30 minutes with AI assistance

Supported Platforms

logos-dev-boost runs on any platform with Nix:

• Linux (x86_64, aarch64)
• macOS (x86_64, aarch64)

The generated context files (AGENTS.md, CLAUDE.md, skills) are plain text and work on any platform.