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feature/is
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CLAUDE.md
77
CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Git Workflow Policy
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**CRITICAL: All code changes MUST follow this workflow. Direct pushes to `main` are ABSOLUTELY PROHIBITED.**
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1. **Create Gitea Issue First** — All features, bug fixes, and policy changes require a Gitea issue before any code is written
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2. **Create Feature Branch** — Branch from `main` using format `feature/issue-{N}-{short-description}`
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3. **Implement Changes** — Write code, tests, and documentation on the feature branch
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4. **Create Pull Request** — Submit PR to `main` branch referencing the issue number
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5. **Review & Merge** — After approval, merge via PR (squash or merge commit)
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**Never commit directly to `main`.** This policy applies to all changes, no exceptions.
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## Agent Workflow
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**Modern AI development leverages specialized agents for concurrent, efficient task execution.**
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### Parallel Execution Strategy
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Use **git worktree** or **subagents** (via the Task tool) to handle multiple requirements simultaneously:
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- Each task runs in independent context
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- Parallel branches for concurrent features
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- Isolated test environments prevent interference
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- Faster iteration with distributed workload
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### Specialized Agent Roles
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Deploy task-specific agents as needed instead of handling everything in the main conversation:
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- **Conversational Agent** (main) — Interface with user, coordinate other agents
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- **Ticket Management Agent** — Create/update Gitea issues, track task status
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- **Design Agent** — Architectural planning, RFC documents, API design
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- **Code Writing Agent** — Implementation following specs
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- **Testing Agent** — Write tests, verify coverage, run test suites
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- **Documentation Agent** — Update docs, docstrings, CLAUDE.md, README
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- **Review Agent** — Code review, lint checks, security audits
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- **Custom Agents** — Created dynamically for specialized tasks (performance analysis, migration scripts, etc.)
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### When to Use Agents
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**Prefer spawning specialized agents for:**
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1. Complex multi-file changes requiring exploration
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2. Tasks with clear, isolated scope (e.g., "write tests for module X")
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3. Parallel work streams (feature A + bugfix B simultaneously)
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4. Long-running analysis (codebase search, dependency audit)
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5. Tasks requiring different contexts (multiple git worktrees)
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**Use the main conversation for:**
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1. User interaction and clarification
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2. Quick single-file edits
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3. Coordinating agent work
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4. High-level decision making
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### Implementation
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```python
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# Example: Spawn parallel test and documentation agents
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task_tool(
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subagent_type="general-purpose",
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prompt="Write comprehensive tests for src/markets/schedule.py",
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description="Write schedule tests"
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)
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task_tool(
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subagent_type="general-purpose",
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prompt="Update README.md with global market feature documentation",
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description="Update README"
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)
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```
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Use `run_in_background=True` for independent tasks that don't block subsequent work.
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## Build & Test Commands
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```bash
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@@ -60,8 +134,9 @@ Pydantic Settings loaded from `.env` (see `.env.example`). Required vars: `KIS_A
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## Test Structure
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35 tests across three files. `asyncio_mode = "auto"` in pyproject.toml — async tests need no special decorator. The `settings` fixture in `conftest.py` provides safe defaults with test credentials and in-memory DB.
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54 tests across four files. `asyncio_mode = "auto"` in pyproject.toml — async tests need no special decorator. The `settings` fixture in `conftest.py` provides safe defaults with test credentials and in-memory DB.
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- `test_risk.py` (11) — Circuit breaker boundaries, fat-finger edge cases
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- `test_broker.py` (6) — Token lifecycle, rate limiting, hash keys, network errors
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- `test_brain.py` (18) — JSON parsing, confidence threshold, malformed responses, prompt construction
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- `test_market_schedule.py` (19) — Market open/close logic, timezone handling, DST, lunch breaks
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Reference in New Issue
Block a user