mi222eh
91 lines
3.4 KiB
Markdown
91 lines
3.4 KiB
Markdown
# Agent Instructions
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## Python Type Hints (REQUIRED)
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- **All functions must have type hints** for parameters and return types.
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- **All class attributes must have type hints** in `__init__`.
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- **Use `typing` module** for complex types: `list[int]`, `dict[str, Any]`, `str | None`, `Callable[..., Any]`.
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- **Never use bare `list` or `dict`** - always parameterize: `list[str]`, `dict[str, Any]`.
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- **Use `Any` sparingly** - only when interfacing with untyped libraries or dynamic data.
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- **Module-level constants must have type hints**: `VERSION: str = "1.0"`.
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- **Tuple return types**: use `tuple[str, int]` for multiple returns.
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## Dataclasses (REQUIRED for complex data)
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- **Prefer `@dataclass`** for any class representing structured data with multiple fields.
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- **Use `dataclasses.field()`** for default values that are mutable (lists, dicts).
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- **Use `field(default_factory=list)`** instead of `default=[]`.
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- **Use `field(default_factory=dict)`** instead of `default={}`.
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- **Use `kw_only=True`** for dataclasses with many optional fields.
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- **Use `frozen=True`** for immutable dataclasses when appropriate.
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- **Example:**
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```python
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from dataclasses import dataclass, field
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from typing import Optional
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@dataclass
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class IssueInfo:
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number: int
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title: str
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owner: str
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repo: str
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labels: list[str] = field(default_factory=list)
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assignee: Optional[str] = None
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```
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## Follow all instructions provided in the system prompt.
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- Keep responses concise and direct.
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- Minimize output tokens.
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- Use the `Task` tool for complex multi-step tasks.
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- Verify solutions with tests if possible.
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- Run lint and typecheck commands if provided.
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- Do not commit changes unless explicitly asked.
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- Use GitHub-flavored markdown for formatting.
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- Answer concisely with fewer than 4 lines of text.
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- ALWAYS use `uv` to run python commands. Do not use `python3` directly.
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- Always commit and push changes at the end of a task.
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- NEVER push to the master or main branch.
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# Environment Variables
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- `GITEA_URL` — Gitea API base URL (REQUIRED)
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- `GITEA_TOKEN` — Gitea API token (REQUIRED)
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- `GITEA_REPOS_ROOT` — Local path to clone repos to (REQUIRED)
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- `AGENT_MODEL_ID` — LM Studio model ID (default: `qwen/qwen3.6-35b-a3b`)
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- `AGENT_MAX_RETRIES` — Max retries per task (default: `2`)
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# Architecture
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The agent uses a **repo-scoped single-agent dispatch** pattern:
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1. `AgentOrchestrator` polls Gitea for assigned issues/PRs
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2. Tasks are grouped by repo and enqueued in `WorkQueue`
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3. `AgentDispatcher` creates a **fresh `CodingAgent`** per repo batch
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4. Agent processes all tasks for one repo, then is **discarded** (context cleared)
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5. Next repo gets a fresh agent — no context bleed between repos
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```
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main.py (polling loop every 60s)
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└── AgentOrchestrator
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├── WorkQueue (grouped by repo)
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└── AgentDispatcher
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└── CodingAgent (one at a time, discarded after each repo)
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```
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# Running the Agent
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```bash
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# Activate the virtual environment
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uv sync
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# Run the agent
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uv run start-agent
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```
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# Repository Scope
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- **The agent MUST ONLY operate on repos within the `meeks` organization.**
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- `gitea/client.py:48` enforces this with a hardcoded filter: `if r.get("owner", {}).get("login") == "meeks"`
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- **Never change this filter** to include personal accounts (e.g., `meeks-ai`) or other organizations.
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- This filter is the single source of truth for repo scope — do not bypass it.
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