dgf1988/codewhale

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T

Hunter Bown ab2c708ca7 feat: runtime API, task manager, and extensive improvements (v0.3.16)

Major Features:
- Runtime API for external integrations and turn management
- Task manager with persistence and recovery
- Shell output streaming and improved tool execution
- Error taxonomy and audit logging
- Command palette and UI enhancements

Documentation:
- Runtime API documentation
- Operations runbook
- Architecture updates

Fixes:
- Auto-compaction threshold and triggering logic
- Doctor command API key validation
- Clippy and formatting compliance

2026-02-16 10:51:39 -06:00

3.4 KiB

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Project Instructions

This file provides context for AI assistants working on this project.

Project Type: Rust

Commands

Build: cargo build
Test: cargo test
Run: cargo run
Check: cargo check
Format: cargo fmt
Lint: cargo clippy

Project: deepseek-tui

Documentation

See README.md for project overview.

Version Control

This project uses Git. See .gitignore for excluded files.

Advanced Capabilities

Model Context Protocol (MCP)

This CLI supports MCP for extending tool access.

Use mcp_read_resource to read context from external servers.
Use mcp_get_prompt to leverage pre-defined expert prompts from servers.
You can connect to HTTP/SSE servers by adding their URL to mcp.json.

Multi-Agent Orchestration

For complex, multi-step tasks, you should delegate work:

Sub-agents: Use agent_spawn (or its alias delegate_to_agent) to launch a background assistant for a specific sub-task. Use agent_result to get their output.
Swarms: Use agent_swarm to orchestrate multiple sub-agents with dependencies. This is ideal for parallel exploration or complex refactoring where different parts of the project can be analyzed concurrently.

Project Mapping

Use project_map to get a comprehensive view of the codebase structure. This tool respects .gitignore and provides a summary of key files.

Guidelines

Proactive Investigation: Always start by exploring the codebase using project_map and file_search.
Parallelism: When you need to read multiple files or search across different areas, use parallel tool calls if possible.
Delegation: If a task is large, break it down into sub-tasks and use agent_swarm or agent_spawn.
Testing: Rigorously verify changes using cargo test and cargo check.

Important Notes

DeepSeek-Specific Capabilities

This project is built specifically for DeepSeek models, leveraging their unique features:

Thinking Tokens: DeepSeek models can output thinking blocks (ContentBlock::Thinking) before providing final answers. The TUI supports streaming and displaying thinking tokens with visual distinction. You can use thinking tokens to reason step-by-step before committing to a response.

Reasoning Models: DeepSeek offers specialized reasoning models (e.g., deepseek-reasoner, deepseek-r1) that excel at step-by-step problem solving. Consider using these models for complex tasks.

Large Context Window: DeepSeek models have 128k context windows, allowing you to process large codebases. Use project_map and file_search to navigate efficiently.

DeepSeek API: The CLI uses DeepSeek's OpenAI‑compatible API with support for the Responses API endpoint. The base URL can be configured for global (api.deepseek.com) or China (api.deepseeki.com).

Web Browsing: For up‑to‑date information about DeepSeek models, documentation, or API changes, use web.run with citations. Example search: “DeepSeek API documentation”.

Dogfooding Tips

As a DeepSeek model working on this project, you are “dogfooding” your own tool. Use this opportunity to:

Test the toolset thoroughly and report any issues.
Suggest improvements that would make DeepSeek models more effective.
Keep changes small, focused, and well‑tested.

Remember to run cargo test and cargo check after any changes.

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