Merge pull request #16 from FlowLLM-AI/dev/0626

feat(utils): add DingTalk messaging and Tushare API utilities

Author: jinliyl

README.md

@@ -21,6 +21,13 @@
 FlowLLM is a configuration-driven LLM application framework that organizes workflows, service entrypoints, and
 long-lived components with **Service, Job, Step, and Component**.
 
+## 🧠 FlowLLM Development Skill
+
+FlowLLM ships with a development Skill for coding agents. It explains the framework conventions, repository map,
+extension points, testing workflow, and review checklist for building or extending FlowLLM applications.
+
+Start here when changing the codebase: [FlowLLM Development Skill](skills/flowllm_dev/SKILL.md).
+
 ## ✨ Core Features
 
 - **Configuration-driven**: Starts from `flowllm/config/default.yaml`, with config files and dot-notation overrides.
@@ -33,37 +40,24 @@ long-lived components with **Service, Job, Step, and Component**.
   <img src="docs/figure/flowllm-architecture.svg" alt="FlowLLM Architecture" width="92%">
 </p>
 
-## 🆕 Minimal CLI Flow
-
-FlowLLM also includes `flowllm.lite`, a tiny local CLI flow runner for scripts that do not need the full service
-framework.
-It maps `fl --action --field value` to a Pydantic config and a small ordered `BaseFlow`.
-See [FlowLLM Lite](flowllm/lite/README.md) for the full minimal CLI flow design.
-
 ## 🚀 Quick Start
 
 ### Installation
 
 FlowLLM requires Python 3.11+.
 
-Install development dependencies from source:
+Install from pip:
 
 ```bash
-git clone https://github.com/flowllm-ai/flowllm.git
-cd flowllm
-pip install -e ".[dev]"
-```
-
-Install the optional Claude Code wrapper when needed:
-
-```bash
-pip install -e ".[claude-code]"
+pip install flowllm
 ```
 
-Install all optional dependencies:
+Install from source:
 
 ```bash
-pip install -e ".[full]"
+git clone https://github.com/flowllm-ai/flowllm.git
+cd flowllm
+pip install -e .
 ```
 
 ### Start the Service
@@ -72,16 +66,8 @@ pip install -e ".[full]"
 flowllm start
 ```
 
-The default service address is `127.0.0.1:2333`, and the default workspace is `.flowllm/`. Startup automatically
-creates:
-
-```text
-.flowllm/
-├── metadata/
-└── session/
-```
-
-You can override configuration from the command line:
+The default service address is `127.0.0.1:2333`, and the default workspace is `.flowllm/`.
+Override configuration with dot notation:
 
 ```bash
 flowllm start service.port=8181 enable_logo=false
@@ -90,9 +76,7 @@ flowllm start workspace_dir=/tmp/flowllm-demo service.host=127.0.0.1 service.por
 
 See the [Quick Start](docs/en/quick_start.md) for more startup and invocation examples.
 
-## 🧩 Calling Jobs
-
-After the service starts, CLI commands other than `start` call server-side Jobs with the same name through the client:
+## 🧩 Use FlowLLM
 
 ```bash
 flowllm version
@@ -102,144 +86,43 @@ flowllm demo query="Hello FlowLLM" min_score=0.8
 flowllm add a=1 b=2
 ```
 
-The HTTP entry point is `POST /<job_name>`:
+CLI commands other than `start` call server-side Jobs with the same name. HTTP uses `POST /<job_name>`:
 
 ```bash
 curl -s http://127.0.0.1:2333/add \
   -H 'Content-Type: application/json' \
   -d '{"a":1,"b":2}'
 ```
 
-Streaming Jobs return SSE:
-
-```bash
-flowllm stream_demo query="Hi" repeat=3 interval=0.05
-
-curl -N http://127.0.0.1:2333/stream_demo \
-  -H 'Content-Type: application/json' \
-  -d '{"query":"Hi","repeat":3,"interval":0.05}'
-```
-
-Built-in Jobs:
-
-| Job            | Backend  | Description                                    |
-|----------------|----------|------------------------------------------------|
-| `version`      | `base`   | Returns the FlowLLM package version.           |
-| `health_check` | `base`   | Returns a component health-check summary.      |
-| `help`         | `base`   | Lists registered Jobs and their metadata.      |
-| `demo`         | `base`   | Two-step echo example.                         |
-| `add`          | `base`   | Adds two numbers.                              |
-| `stream_demo`  | `stream` | Streams the input text character by character. |
-
-## ⚙️ Configuration
-
-The default configuration is located at `flowllm/config/default.yaml`. Root configuration sections include application
-parameters, service, Jobs, and Components:
-
-```yaml
-service:
-  backend: http
-
-jobs:
-  add:
-    backend: base
-    description: "add two numbers"
-    steps:
-      - backend: add_step
-```
-
-You can override model and embedding variables through `.env`:
-
-```bash
-cat > .env <<'EOF'
-LLM_BACKEND=openai
-LLM_MODEL_NAME=qwen3.7-plus
-LLM_API_KEY=your_api_key
-LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
-
-EMBEDDING_BACKEND=openai
-EMBEDDING_MODEL_NAME=text-embedding-v4
-EMBEDDING_API_KEY=your_api_key
-EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
-EOF
-```
-
-You can also specify a YAML or JSON configuration file:
-
-```bash
-flowllm start config=/path/to/app.yaml
-```
-
-Configuration parsing supports `${VAR}`, `${VAR:-default}`, booleans, numbers, JSON lists and dictionaries, and
-automatic `null` conversion.
-
-## 🏗️ Code Framework
-
-FlowLLM's core layering is:
-
-```text
-CLI / Client -> Service -> Application -> Job -> Step -> Component
-```
-
-| Layer         | Role                                                                                                                            |
-|---------------|---------------------------------------------------------------------------------------------------------------------------------|
-| `Application` | Loads configuration, creates the workspace, assembles Service, Components, and Jobs, and starts lifecycles in dependency order. |
-| `Service`     | Exposes servable Jobs as HTTP routes or MCP tools.                                                                              |
-| `Job`         | Orchestrates externally callable capabilities or background processes by executing Steps in order.                              |
-| `Step`        | Atomic workflow operation that reads and writes `RuntimeContext`, `Response`, and streaming queues.                             |
-| `Component`   | Long-lived infrastructure such as LLMs, Embeddings, Embedding Stores, and Agent Wrappers.                                       |
-| `Registry`    | Global registry `R`, which looks up implementations by `(component_type, backend_name)`.                                        |
+See [Quick Start](docs/en/quick_start.md) for service, CLI, HTTP, and streaming examples.
 
-Minimal example for adding a Step:
+## ⚙️ Build Applications
 
-```python
-from flowllm.components import R
-from flowllm.steps import BaseStep
-
-
-@R.register("reverse_step")
-class ReverseStep(BaseStep):
-    async def execute(self):
-        text = self.context.get("text", "")
-        self.context.response.answer = text[::-1]
-        return self.context.response
-```
-
-Then declare the Job in configuration:
+FlowLLM applications are configured from `flowllm/config/default.yaml` or your own YAML/JSON config. Add a capability by
+registering a Step or Component, then exposing it as a Job:
 
 ```yaml
 jobs:
   reverse:
     backend: base
     description: "reverse text"
-    parameters:
-      type: object
-      properties:
-        text:
-          type: string
-      required:
-        - text
     steps:
       - backend: reverse_step
 ```
 
-After adding an implementation, make sure the module is imported in the package `__init__.py`; otherwise, the
-registration decorator will not run. See the [code framework](docs/en/framework.md) for details.
+Configuration supports `.env`, `${VAR}`, `${VAR:-default}`, dot-notation overrides, and direct config files:
 
-## 🔌 MCP Service
+```bash
+flowllm start config=/path/to/app.yaml
+```
 
-When the service backend is set to `mcp`, FlowLLM exposes non-streaming Jobs with `enable_serve: true` as MCP tools.
-`StreamJob` is not exposed by MCP Service.
+For implementation rules and examples, use the [FlowLLM Development Skill](skills/flowllm_dev/SKILL.md). For the compact
+architecture reference, see [Code Framework](docs/en/framework.md).
 
-```yaml
-service:
-  backend: mcp
-  transport: sse
-  host: 127.0.0.1
-  port: 2333
-```
+## 🆕 Minimal CLI Flow
 
-MCP transport supports `stdio`, `sse`, and `streamable-http`.
+For scripts that do not need the full service framework, `flowllm.lite` maps `fl --action --field value` to a Pydantic
+config and a small ordered `BaseFlow`. See [FlowLLM Lite](flowllm/lite/README.md).
 
 ## 📚 Documentation
 
@@ -251,7 +134,15 @@ MCP transport supports `stdio`, `sse`, and `streamable-http`.
 ## 🤝 Open Source and Contributing
 
 FlowLLM is licensed under Apache 2.0. Before contributing, read the [contribution guide](docs/en/contributing.md)
-and [development skill](skills/flowllm_dev/SKILL.md), then run:
+and [development skill](skills/flowllm_dev/SKILL.md).
+
+Install development dependencies from source:
+
+```bash
+pip install -e ".[dev]"
+```
+
+Before submitting changes, run:
 
 ```bash
 pre-commit run --all-files