corrections

docs/Readme.md

@@ -5,20 +5,26 @@ A GUI orchestrator for local LLM-driven coding sessions, built to prevent the AI
 ## Core Management Panels
 
 ### Projects Panel
+
 The heart of context management. 
+
 - **Configuration:** You specify the Git Directory (for commit tracking) and a Main Context File (the markdown file containing your project's notes and schema).
 - **Word-Wrap Toggle:** Dynamically swaps text rendering in large read-only panels (Responses, Comms Log) between unwrapped (ideal for viewing precise code formatting) and wrapped (ideal for prose).
 - **Project Switching:** Switch between different <project>.toml profiles to instantly swap out your entire active file list, discussion history, and settings.
 
 ### Discussion History
+
 Manages your conversational branches, preventing context poisoning across different tasks.
+
 - **Discussions Sub-Menu:** Allows you to create separate timelines for different tasks (e.g., "Refactoring Auth" vs. "Adding API Endpoints"). 
 - **Git Commit Tracking:** Clicking "Update Commit" reads HEAD from your project's git directory and stamps the discussion. 
 - **Entry Management:** Each turn has a Role (User, AI, System). You can toggle entries between **Read** and **Edit** modes, collapse them, or hit [+ Max] to open them in the Global Text Viewer.
 - **Auto-Add:** If toggled, anything sent from the "Message" panel and returned to the "Response" panel is automatically appended to the current discussion history.
 
 ### Files & Screenshots
+
 Controls what is explicitly fed into the context compiler.
+
 - **Base Dir:** Defines the root for path resolution and tool constraints.
 - **Paths:** Explicit files or wildcard globs (e.g., src/**/*.rs). 
 - When generating a request, these files are summarized symbolically (summarize.py) to conserve tokens, unless the AI explicitly decides to read their full contents via its internal tools.
@@ -26,20 +32,25 @@ Controls what is explicitly fed into the context compiler.
 ## Interaction Panels
 
 ### Provider
+
 Switch between API backends (Gemini, Anthropic) on the fly. Clicking "Fetch Models" queries the active provider for the latest model list.
 
 ### Message & Response
+
 - **Message:** Your input field. 
 - **Gen + Send:** Compiles the markdown context and dispatches the background thread to the AI.
 - **MD Only:** Dry-runs the compiler so you can inspect the generated <project>_00N.md without triggering an API charge.
 - **Response:** The read-only output. Flashes green when a new response arrives. 
 
 ### Global Text Viewer & Script Outputs
+
 - **Last Script Output:** Whenever the AI executes a background script, this window pops up, flashing blue. It contains both the executed script and the stdout/stderr.
 - **Text Viewer:** A large, resizable global popup invoked anytime you click a [+] or [+ Maximize] button in the UI. Used for deep-reading long logs, discussion entries, or script bodies.
 
 ## System Prompts
+
 Provides two text inputs for overriding default instructions:
+
 1. **Global:** Applied across every project you load.
 2. **Project:** Specific to the active workspace.
 These are concatenated onto the strict tool-usage guidelines the agent is initialized with.

docs/guide_architecture.md

@@ -28,18 +28,19 @@ The application lifetime is localized within App.run in gui.py.
 ### Context Shaping & Aggregation
 
 Before making a call to an AI Provider, the current state of the workspace is resolved into a dense Markdown representation. 
-This occurs inside ggregate.run.
+This occurs inside aggregate.run.
 
-If using the default workflow, ggregate.py hashes through the following process:
+If using the default workflow, aggregate.py hashes through the following process:
-1. **Glob Resolution:** Iterates through config["files"]["paths"] and unpacks any wildcards (e.g., src/**/*.rs) against the designated ase_dir.
+
+1. **Glob Resolution:** Iterates through config["files"]["paths"] and unpacks any wildcards (e.g., src/**/*.rs) against the designated base_dir.
 2. **Summarization Pass:** Instead of concatenating raw file bodies (which would quickly overwhelm the ~200k token limit over multiple rounds), the files are passed to summarize.py.
-3. **AST Parsing:** summarize.py runs a heuristic pass. For Python files, it uses the standard st module to read structural nodes (Classes, Methods, Imports, Constants). It outputs a compact Markdown table.
+3. **AST Parsing:** summarize.py runs a heuristic pass. For Python files, it uses the standard ast module to read structural nodes (Classes, Methods, Imports, Constants). It outputs a compact Markdown table.
 4. **Markdown Generation:** The final <project>_00N.md string is constructed, comprising the truncated AST summaries, the user's current project system prompt, and the active discussion branch. 
 5. The Markdown file is persisted to disk (./md_gen/ by default) for auditing.
 
 ### AI Communication & The Tool Loop
 
-The communication model is unified under i_client.py, which normalizes the Gemini and Anthropic SDKs into a singular interface send(md_content, user_message, base_dir, file_items).
+The communication model is unified under ai_client.py, which normalizes the Gemini and Anthropic SDKs into a singular interface send(md_content, user_message, base_dir, file_items).
 
 The loop is defined as follows:
 
@@ -55,24 +56,23 @@ The loop is defined as follows:
 
 ### On Tool Execution & Concurrency
 
-When the AI calls a safe MCP tool (like 
+When the AI calls a safe MCP tool (like read_file or search_files), the daemon thread immediately executes it via mcp_client.py and returns the result.
-ead_file or search_files), the daemon thread immediately executes it via mcp_client.py and returns the result.
 
-However, when the AI requests 
+However, when the AI requests run_powershell, the operation halts:
-un_powershell, the operation halts:
 
-1. The Daemon Thread instantiates a ConfirmDialog object containing the payload and calls .wait(). This blocks the thread on a 	hreading.Event().
+1. The Daemon Thread instantiates a ConfirmDialog object containing the payload and calls .wait(). This blocks the thread on a threading.Event().
 2. The ConfirmDialog instance is safely placed in a _pending_dialog_lock.
 3. The Main Thread, during its next frame cycle, pops the dialog from the lock and renders an OS-level modal window using dpg.window(modal=True).
 4. The user can inspect the script, modify it in the text box, or reject it entirely.
-5. Upon the user clicking "Approve & Run", the main thread triggers the 	hreading.Event, unblocking the Daemon Thread.
+5. Upon the user clicking "Approve & Run", the main thread triggers the threading.Event, unblocking the Daemon Thread.
 6. The Daemon Thread passes the script to shell_runner.py, captures stdout, stderr, and exit_code, logs it to session_logger.py, and returns it to the LLM.
 
 ### On Context History Pruning (Anthropic)
 
 Because the Anthropic API requires sending the entire conversation history on every request, long sessions will inevitably hit the invalid_request_error: prompt is too long.
 
-To solve this, i_client.py implements an aggressive pruning algorithm:
+To solve this, ai_client.py implements an aggressive pruning algorithm:
+
 1. _strip_stale_file_refreshes: It recursively sweeps backward through the history dict and strips out large [FILES UPDATED] data blocks from old turns, preserving only the most recent snapshot.
 2. _trim_anthropic_history: If the estimated token count still exceeds _ANTHROPIC_MAX_PROMPT_TOKENS (~180,000), it slices off the oldest user/assistant message pairs from the beginning of the history array.
 3. The loop guarantees that at least the System prompt, Tool Definitions, and the final user prompt are preserved.
@@ -80,6 +80,7 @@ To solve this, i_client.py implements an aggressive pruning algorithm:
 ### Session Persistence
 
 All I/O bound session data is recorded sequentially. session_logger.py hooks into the execution loops and records:
+
 - logs/comms_<ts>.log: A JSON-L structured timeline of every raw payload sent/received.
 - logs/toolcalls_<ts>.log: A sequential markdown record detailing every AI tool invocation and its exact stdout result.
 - scripts/generated/: Every .ps1 script approved and executed by the shell runner is physically written to disk for version control transparency.

docs/guide_tools.md

@@ -11,38 +11,40 @@ The agent is provided two classes of tools: Read-Only MCP Tools, and a Destructi
 Implemented in mcp_client.py. These tools allow the AI to selectively expand its knowledge of the codebase without requiring the user to dump entire 10,000-line files into the static context prefix.
 
 ### Security & Scope
+
 Every filesystem MCP tool passes its arguments through _resolve_and_check. This function ensures that the requested path falls under one of the allowed directories defined in the GUI's Base Dir configurations. 
 If the AI attempts to read or search a path outside the project bounds, the tool safely catches the constraint violation and returns ACCESS DENIED.
 
 ### Supplied Tools:
-* 
+
-ead_file(path): Returns the raw UTF-8 text of a file.
+* read_file(path): Returns the raw UTF-8 text of a file.
 * list_directory(path): Returns a formatted table of a directory's contents, showing file vs dir and byte sizes.
 * search_files(path, pattern): Executes an absolute glob search (e.g., **/*.py) to find specific files.
 * get_file_summary(path): Invokes the local summarize.py heuristic parser to get the AST structure of a file without reading the whole body.
 * web_search(query): Queries DuckDuckGo's raw HTML endpoint and returns the top 5 results (Titles, URLs, Snippets) using a native HTMLParser to avoid heavy dependencies.
-* etch_url(url): Downloads a target webpage and strips out all scripts, styling, and structural HTML, returning only the raw prose content (clamped to 40,000 characters).
+* fetch_url(url): Downloads a target webpage and strips out all scripts, styling, and structural HTML, returning only the raw prose content (clamped to 40,000 characters).
 
-## 2. Destructive Execution (
+## 2. Destructive Execution (run_powershell)
-un_powershell)
 
 The core manipulation mechanism. This is a single, heavily guarded tool.
 
 ### Flow
-1. The AI generates a 
+
-un_powershell payload containing a PowerShell script.
+1. The AI generates a 'run_powershell' payload containing a PowerShell script.
 2. The AI background thread calls confirm_and_run_callback (injected by gui.py).
 3. The background thread blocks completely, creating a modal popup on the main GUI thread.
 4. The user reads the script and chooses to Approve or Reject.
-5. If Approved, shell_runner.py executes the script using -NoProfile -NonInteractive -Command within the specified ase_dir.
+5. If Approved, shell_runner.py executes the script using -NoProfile -NonInteractive -Command within the specified base_dir.
 6. The combined stdout, stderr, and EXIT CODE are captured and returned to the AI in the tool result block.
 
 ### AI Guidelines
+
 The core system prompt explicitly guides the AI on how to use this tool safely:
+
 * Prefer targeted replacements (using PowerShell's .Replace()) over full rewrites where possible.
 * If a file is large and complex (requiring specific escape characters), do not attempt an inline python -c script. Instead, use a PowerShell here-string (@'...'@) to write a temporary python helper script to disk, execute the python script, and then delete it.
 
 ### Synthetic Context Refresh
-Immediately after **any** tool call turn finishes, i_client runs _reread_file_items. It fetches the latest disk state of all files in the current project context and appends them as a synthetic [FILES UPDATED] message to the tool result. 
+
-This means if the AI writes to a file, it instantly "sees" the modification in its next turn without having to waste a cycle calling 
+Immediately after **any** tool call turn finishes, ai_client runs _reread_file_items. It fetches the latest disk state of all files in the current project context and appends them as a synthetic [FILES UPDATED] message to the tool result. 
-ead_file.
+This means if the AI writes to a file, it instantly "sees" the modification in its next turn without having to waste a cycle calling read_file.

shell_runner.py

@@ -1,4 +1,5 @@
-import subprocess, shutil
+# shell_runner.py
+import subprocess, shutil
 from pathlib import Path
 
 TIMEOUT_SECONDS = 60