feat(tui): introduce comprehensive state management for TUI interface
- Add AppStateRest as the central state struct for managing TUI state. - Implement InputState for handling user input, autocomplete, and history. - Create MiscState to manage overlays, notifications, and editor state. - Introduce ScrollState for viewport scrolling functionality. - Develop TranscriptCache for efficient message rendering in the chat pane. - Implement SimpleAgent and SimpleWorkflowEngine for agent lifecycle management. - Add helper functions for managing effort levels and token counting. - Organize state-related modules for better maintainability and clarity.
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//! System prompts and directive templates for agent and subagent turns.
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//!
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//! Centralising all prompt text here keeps the core turn logic free of
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//! hardcoded prose, making prompts easier to maintain, review, and localise.
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//!
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//! # Flow
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//! The application layer's `AgentTurnServiceImpl` calls `main_agent_prompt()`
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//! to construct the system message at the start of each turn. Subagent and
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//! review prompts are provided by their respective modules.
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/// Build the main-agent system prompt.
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///
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/// The prompt establishes the agent's identity as Zesdex, an AI coding
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/// assistant, and defines the priority hierarchy that governs tool selection:
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///
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/// 1. **Workflow first** — `workflow_run` / `hive_mind` for complex tasks
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/// 2. **Planning & TODOs** — `plan_enter` / `todowrite` for structural work
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/// 3. **Reasoning** — `seq_think` for deep analysis
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/// 4. **Tool execution** — direct tools for simple actions
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pub fn main_agent_prompt() -> String {
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"\
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You are Zesdex, an AI coding assistant. You have access to various tools \
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via native function calling to help the user.
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CRITICAL DIRECTIVES & PRIORITY HIERARCHY:
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1. WORKFLOW FIRST: For any multi-step, complex, or non-trivial task, \
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you MUST prioritise using `workflow_run` (to construct and execute a \
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multi-phase YAML workflow) or `hive_mind` (to orchestrate parallel \
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autonomous agents). Workflows are your primary strategy.
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2. PLANNING & TODOS: Use `plan_enter` to establish high-level \
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architectural plans and `todowrite` to maintain granular task checklists.
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3. REASONING: Use `seq_think` for deep step-by-step analysis.
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4. TOOL EXECUTION: Execute individual tools (file edits, terminal commands) \
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within or guided by your workflows. If an error occurs, analyse and fix it.
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Respond conversationally, concisely, and helpfully."
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.to_string()
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}
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/// Build a subagent directive prompt.
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///
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/// The directive is embedded in a system message that also communicates the
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/// current working directory and workspace root so the subagent can resolve
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/// paths correctly.
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pub fn subagent_directive(directive: &str, cwd: &str, ws_root: &str) -> String {
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format!(
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"\
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You are a focused subagent.
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Current directory (PWD): {cwd}
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Workspace root: {ws_root}
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Your directive:
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{directive}
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Complete the directive autonomously using the tools available to you. \
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Return your final answer when done."
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)
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}
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/// Build a conversation-compaction prompt.
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///
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/// The LLM is asked to produce a concise bulleted summary of the key
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/// requests, decisions, tools executed, and files modified.
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pub fn compaction_prompt() -> String {
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"\
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You are a helpful assistant summarising conversation history. \
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Provide a concise summary of the key user requests, decisions, tools \
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executed, and modified files. Format as a clear bulleted list."
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.to_string()
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn main_prompt_is_non_empty() {
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let prompt = main_agent_prompt();
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assert!(!prompt.is_empty());
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assert!(prompt.contains("Zesdex"));
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assert!(prompt.contains("WORKFLOW FIRST"));
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}
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#[test]
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fn subagent_directive_includes_directive_text() {
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let prompt = subagent_directive("test directive", "/home", "/home/project");
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assert!(prompt.contains("test directive"));
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assert!(prompt.contains("/home"));
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assert!(prompt.contains("/home/project"));
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}
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}
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