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docs(CONTRIBUTING): update test count and add details for skill-creator licensing

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X-iZhang
2026-03-06 21:35:49 +00:00
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CONTRIBUTING.md
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@@ -20,7 +20,7 @@ EvoScientist is a multi-agent AI system for automated scientific experimentation
| License | MIT |
| Framework | [DeepAgents](https://github.com/langchain-ai/deepagents) + [LangChain](https://python.langchain.com/) + [LangGraph](https://langchain-ai.github.io/langgraph/) |
| Default model | `claude-sonnet-4-6` (Anthropic) |
| Tests | ~830 across 35 files, no API keys needed |
| Tests | ~837 across 35 files, no API keys needed |
| Config file | `~/.config/evoscientist/config.yaml` |
### Sub-Agents (defined in `EvoScientist/subagent.yaml`)
@@ -218,10 +218,15 @@ EvoScientist/EvoScientist/
|
+-- skills/ # Built-in skills (read-only to agent)
|-- find-skills/ # Skill discovery
+-- skill-creator/ # Skill creation wizard
+-- skill-creator/ # Skill creation wizard (Apache 2.0 licensed, see LICENSE.txt)
|-- scripts/
| |-- run_eval.py # Single-skill trigger evaluation via LLM tool-calling
| |-- run_loop.py # Iterative description optimization (train/test split)
| +-- improve_description.py # LLM-based description improvement
+-- eval-viewer/ # HTML eval result viewer
```
Additional built-in skills (`agent-swarm-protocol`, `paper-planning`, `paper-review`, `paper-writing`) can be installed as user skills under `workspace/skills/`.
10 additional research-lifecycle skills are available in the [EvoSkills repo](../EvoSkills/skills/) covering ideation, experimentation, writing, and support phases. Install via `/install-skill ../EvoSkills/skills` (batch) or `/install-skill ../EvoSkills/skills/<name>` (single).
### Tests
@@ -295,7 +300,7 @@ Two variants exist:
### Configuration System
`config/settings.py`:
- **`EvoScientistConfig`** — Dataclass with all settings: API keys (Anthropic, OpenAI, Google, NVIDIA, Tavily, SiliconFlow, OpenRouter, custom, Ollama), LLM settings (provider, model), workspace settings (default_mode, default_workdir), UI settings (show_thinking, ui_backend), and channel-specific settings.
- **`EvoScientistConfig`** — Dataclass with all settings: API keys (Anthropic, OpenAI, Google, NVIDIA, Tavily, SiliconFlow, OpenRouter, ZhipuAI, custom, Ollama), LLM settings (provider, model), workspace settings (default_mode, default_workdir), UI settings (show_thinking, ui_backend), and channel-specific settings.
- **`get_effective_config(cli_overrides)`** — Merges 4 sources in priority order (CLI > env > file > defaults).
- **`apply_config_to_env(config)`** — Sets API keys as env vars for downstream libraries (LangChain, Tavily).
- **`load_config()` / `save_config()`** — YAML file I/O at `~/.config/evoscientist/config.yaml`.
@@ -642,16 +647,53 @@ class EvoScientistConfig:
### Adding a New LLM Provider
1. **Add model entries** to `_MODEL_ENTRIES` in `llm/models.py`:
There are three levels of provider integration, from simplest to most involved:
#### A. Adding models to an existing provider
Just add entries to `_MODEL_ENTRIES` in `llm/models.py`:
```python
_MODEL_ENTRIES = [
...
("my-model", "my-provider/my-model-id", "myprovider"),
("my-model", "my-model-id", "existing-provider"),
]
```
2. **Update `get_chat_model()`** in `llm/models.py` — Add provider-specific initialization if needed (API key handling, special kwargs).
No config, dependency, or onboard changes needed.
#### B. Adding a new third-party provider (routes through OpenAI)
Third-party providers that expose an OpenAI-compatible API use the `_THIRD_PARTY_PROVIDERS` pattern (e.g., SiliconFlow, OpenRouter, ZhipuAI). This avoids adding a new `langchain-*` dependency.
1. **Add provider routing** to `_THIRD_PARTY_PROVIDERS` in `llm/models.py`:
```python
_THIRD_PARTY_PROVIDERS = {
...
"myprovider": ("https://api.myprovider.com/v1", "MYPROVIDER_API_KEY"),
}
```
2. **Add model entries** to `_MODEL_ENTRIES`:
```python
("my-model", "my-model-id", "myprovider"),
```
3. **Add config fields** — Add `myprovider_api_key` to `EvoScientistConfig` in `config/settings.py`, the `_ENV_MAP`, and `apply_config_to_env()`.
4. **Update onboard wizard** — Add API key prompt in `config/onboard.py`.
5. **Test** — Add provider tests in `tests/test_llm.py`. Mock the chat model constructor.
#### C. Adding a new native provider (new LangChain package)
For providers that require their own `langchain-*` package (e.g., Anthropic, Google GenAI, NVIDIA):
1. **Add model entries** to `_MODEL_ENTRIES` in `llm/models.py`.
2. **Update `get_chat_model()`** in `llm/models.py` — Add provider-specific initialization if needed (API key handling, special kwargs, auto-config in `_apply_auto_config()`).
3. **Add config fields** — Add `myprovider_api_key` to `EvoScientistConfig` and the env var mapping.
@@ -723,7 +765,7 @@ Prefer inline `# noqa: RULE` for individual exceptions over `per-file-ignores` i
### Running Tests
```bash
# All tests (~830 tests, no API keys needed)
# All tests (~837 tests, no API keys needed)
pytest -v
# Single file
@@ -838,7 +880,7 @@ Ask your agent to confirm:
Never commit real API keys or secrets. Configure via:
- `EvoSci onboard` (interactive wizard)
- `EvoSci config set <key> <value>`
- Environment variables (`ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, `GOOGLE_API_KEY`, `NVIDIA_API_KEY`, `TAVILY_API_KEY`, etc.)
- Environment variables (`ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, `GOOGLE_API_KEY`, `NVIDIA_API_KEY`, `TAVILY_API_KEY`, `ZHIPU_API_KEY`, etc.)
Config file: `~/.config/evoscientist/config.yaml`
MCP config: `~/.config/evoscientist/mcp.yaml`