DeepTutor Agent Skills & AI Rules

# DeepTutor — Agent-Native Architecture

## Overview

DeepTutor is an **agent-native** intelligent learning companion organized
around a two-layer plugin model — single-shot **Tools** invoked by the
LLM, and multi-stage **Capabilities** that take over a turn — exposed
through three entry points: CLI, WebSocket API, and Python SDK. All three
enter the durable turn application service before the shared turn engine
routes a normalized context to the selected capability.

## Architecture

```
Entry Points:  CLI (Typer)  |  WebSocket /ws  |  Python SDK
                    ↓                   ↓                   ↓
              ┌─────────────────────────────────────────────────┐
              │          TurnApplicationService                 │
              │   persists, coordinates, and replays turns      │
              └──────────────────────┬──────────────────────────┘
                                     ↓
              ┌─────────────────────────────────────────────────┐
              │       TurnEngine → ChatOrchestrator              │
              │   routes UnifiedContext → selected Capability    │
              │   (defaults to `chat`)                           │
              └──────────┬──────────────┬───────────────────────┘
                         │              │
              ┌──────────▼──┐  ┌────────▼──────────┐
              │ ToolRegistry │  │ CapabilityRegistry │
              │  (Level 1)   │  │   (Level 2)        │
              └──────────────┘  └────────────────────┘
```

`TurnApplicationService` owns durable turn state and replay through the
session store and runtime coordinator. Each execution uses a per-turn
`StreamBus`; the orchestrator emits events, the turn runtime persists them,
and adapters replay them to consumers. Runtime settings live in
`data/user/settings/*.json` — project-root `.env` files are intentionally
ignored.

### Level 1 — Tools

Single-function tools the LLM picks on demand. Seven user-toggleable tools
surface in `/settings/tools`:

| Tool                 | Description                                   |
| -------------------- | --------------------------------------------- |
| `brainstorm`         | Breadth-first idea exploration with rationale |
| `web_search`         | Web search with citations                     |
| `paper_search`       | arXiv preprint search                         |
| `reason`             | Dedicated deep-reasoning LLM call             |
| `geogebra_analysis`  | Analyze math images into GeoGebra commands    |
| `imagegen`           | Generate images                               |
| `videogen`           | Generate videos                               |

`USER_TOGGLEABLE_TOOL_NAMES` in `deeptutor/tools/builtin/__init__.py` is the
authoritative toggle list. Other built-ins are **context-gated** or
capability-owned: `CONFIGURABLE_BUILTIN_TOOL_NAMES` declares the context-gated
surface, while `deeptutor/agents/_shared/tool_composition.py` owns the mount
rules (`ToolMountFlags`) and the always-available workspace tools. Examples
include `rag`, memory and notebook tools, `read_skill`, deferred MCP/CLI tools,
`exec`, `ask_user`, and mastery navigation. `--tool` selects from the
user-toggleable whitelist; it does not bypass context or capability gates.

### Level 2 — Capabilities

Multi-stage pipelines that own the turn:

| Capability       | Stages                                                |
| ---------------- | ----------------------------------------------------- |
| `chat`           | exploring → responding (single agentic loop, default) |
| `ask_questions`  | responding (chat loop forced through `ask_user`)      |
| `deep_solve`     | responding (chat loop + solve planning tools)         |
| `deep_question`  | ideation → generation                                 |
| `deep_research`  | rephrasing → decomposing → researching → reporting    |
| `visualize`      | analyzing → generating → reviewing (SVG / Chart.js / Mermaid / HTML; or routes to Manim sub-stages via `render_type`) |
| `math_animator`  | concept_analysis → concept_design → code_generation → code_retry → summary → render_output |
| `mastery_path`   | responding (Guided Learning — chat loop + mastery tools, gated per topic type) |
| `immersive_reading` | responding (document- or timestamp-grounded media reading loop) |
| `course_study`   | responding (course-state sensing and hand-off loop)   |

All capabilities converge on `emit_capability_result()` in
`deeptutor/capabilities/_shared.py` so every turn emits the same envelope
(response payload + `cost_summary` from `UsageTracker`). Status copy and
prompts are i18n'd via `capabilities/prompts/{en,zh}/<name>.yaml`.

## CLI Usage

```bash
# Install
pip install deeptutor      # Full app (CLI + Web/API + packaged Web assets)
pip install deeptutor-cli  # CLI-only

# Run any capability
deeptutor run chat "Explain Fourier transform"
deeptutor run deep_solve "Solve x^2=4" -t rag --kb my-kb
deeptutor run visualize "Animate sine wave" --config render_mode=manim_video

# Interactive REPL
deeptutor chat
# (inside the REPL: /regenerate or /retry re-runs the last user message)

# Partners (IM-connected companions)
deeptutor partner list

# Knowledge bases, memory, server
deeptutor kb list
deeptutor kb create my-kb --doc textbook.pdf
deeptutor memory show
deeptutor serve --port 8001       # API server only
deeptutor start                   # backend + frontend together
```

## Key Files

| Path                                       | Purpose                              |
| ------------------------------------------ | ------------------------------------ |
| `deeptutor/runtime/orchestrator.py`        | `ChatOrchestrator` — unified entry   |
| `deeptutor/runtime/launcher.py`            | Backend + frontend lifecycle / port discovery |
| `deeptutor/runtime/registry/`              | Tool + Capability registries         |
| `deeptutor/runtime/bootstrap/builtin_capabilities.py` | Built-in capability class paths |
| `deeptutor/services/config/runtime_settings.py` | JSON settings + process-env overrides |
| `deeptutor/services/subagent/`             | Local/remote agent connectors; register each backend in `registry.py` and its model options in `models.py` (Grok CLI uses native `streaming-json`) |
| `deeptutor/core/stream.py`, `deeptutor/runtime/stream_bus.py` | StreamEvent protocol + async fan-out |
| `deeptutor/core/tool_protocol.py`          | `BaseTool` + `ToolDefinition`         |
| `deeptutor/core/capability_protocol.py`    | `TurnCapability` + `CapabilityManifest` |
| `deeptutor/core/context.py`                | `UnifiedContext` dataclass            |
| `deeptutor/tools/builtin/__init__.py`      | All built-in tool wrappers           |
| `deeptutor/capabilities/`                  | Built-in capability implementations  |
| `deeptutor/app.py`                         | `DeepTutorApp` — Python SDK facade    |
| `deeptutor_cli/main.py`                    | Typer CLI entry point                |
| `deeptutor/api/routers/unified_ws.py`      | Unified WebSocket endpoint           |

## Dependency Layers

Public install paths and source extras are defined in `pyproject.toml`.
Requirements files mirror the same dependency groups for Docker/CI installs.

```
pip install deeptutor      — Full app (CLI + Web/API + packaged Web assets)
pip install deeptutor-cli  — CLI-only (LLM + RAG + providers + document parsing)
pip install -e .           — Source install for development

Source extras (.[ extra ], defined in pyproject.toml):
.[cli]            — CLI-only dependency set
.[server]         — Web/API server dependencies
.[partners]       — Partner channel SDKs  (legacy alias: .[tutorbot])
.[matrix]         — Matrix channel for Partners (matrix-nio; needs libolm)
.[matrix-e2e]     — Matrix with end-to-end encryption (matrix-nio[e2e])
.[math-animator]  — Manim addon (powers `visualize` Manim renders + `deeptutor run math_animator`)
.[dev]            — Test / lint tooling
.[all]            — Everything above
```