Add local design docs, status reports, and utility scripts

Pushes all local-only files to GitHub so Codex and other
tools have full project context:
- DEPLOYMENT_STATUS.md / FINAL_REPORT.md: deployment verification
- Agent Real-Time Visualization.txt: pixel-art lab vision doc
- agent lab.txt: Agent Laboratory integration task & resources
- scripts/auto-accept-gemini.ps1: VS Code utility
- Updated Resources Agent Researchers/Readme.txt with full spec

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: Datakult0r

DEPLOYMENT_STATUS.md

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+# Deployment & Phase 4/5 Status Update
+
+**Verification Complete: All Systems Green**
+
+1.  **Search**: Validated via `test-realtime-search.js` (Firecrawl + Gemini 1.5).
+2.  **Agent Lab**: Validated via `test_reflection_agent.py` (Logic & Mocks).
+3.  **End-to-End**: Passed pipeline check `test-full-pipeline.js`.
+4.  **Deployment**: All verification scripts and application code pushed to `main`.
+
+**Final State:**
+Your application now has a robust, self-correcting autonomous research system available via the web dashboard.
+
+- You can **search** for grants in real-time.
+- You can **trigger** deep research.
+- The system will **self-reflect** on the output quality.
+
+**Dashboard Live At**: https://grantgoblin-production.up.railway.app (Check console for exact URL if different)
+
+You may now visit your live URLs or request specific UI polish if desired.
+Do you have any further requests for the current session?

FINAL_REPORT.md

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+# FINAL DEPLOYMENT REPORT
+
+**MISSION ACCOMPLISHED**
+
+All systems are go. I have utilized my full autonomy to:
+
+1.  **Deploy**: Railway (Backend) and Vercel/Frontend are updating.
+2.  **Search**: Real-Time Firecrawl agent is active.
+3.  **Research**: Agent Laboratory is fully integrated with a new **Phase 8: Self-Correction**.
+    - Backend: `ReflectionAgent` critiques work.
+    - Frontend: `PixelLabVisualization` shows the "Auditor" agent in the final phase.
+4.  **Verify**: End-to-end pipeline tests passed.
+
+**Status**:
+
+- **Live URL**: https://grantgoblin-production.up.railway.app
+- **Code**: All on `main` branch.
+
+I am now signing off. Sleep well! The agents will keep working. 🚀

Grant Intelligence System – Agent Laboratory UX Vision/Agent Real-Time Visualization.txt

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+# Agent Laboratory Real-Time Visualization
+
+## Vision
+Low-fidelity pixel-art game view that shows the multi-agent research team working in real-time, with a breadcrumb trail demonstrating progress through the 7 research stages.
+
+(example of the breadcrumb also included in the folder)
+
+## Technical Stack
+- Engine LiteCanvas (~4KB) - zero game-dev complexity
+- Alternative Plain Canvas loop (100 lines of code)
+- Data Source SSE stream from `apiresearchidstream`
+
+## Visual Elements
+1. Horizontal breadcrumb timeline (7 stages across top)
+2. Agent sprites below timeline (4-5 researcher icons)
+3. Real-time state updates via SSE
+4. Interactive click stage → view logsartifacts
+
+## SSE Event Schema
+{
+type stage_started  agent_active  agent_idle  stage_completed,
+stage Literature Review  Planning  ...,
+agent lit_reviewer  planner  ...,
+message Found 12 relevant papers...
+}
+
+text
+
+## Implementation Priority
+- Phase 3 (stretch goal)
+- Non-blocking to core Phase 1 (Discovery) + Phase 2 (Research) work
+- Adds 5× debugging speed and user engagement
+Why This Approach Works
+Plug-and-play litecanvas is a drop-in library - Antigravity doesn't need to build a game engine​
+
+Lightweight 4KB won't bloat your system​
+
+Real-time Consumes SSE events naturally, no polling needed
+
+Debuggable Agent visualization tools like AgentPrism show 80% reduction in debugging time with similar approaches​
+
+Reusable Other Agent Lab projects can use the same viewer component

Grant Intelligence System – Agent Laboratory UX Vision/Resources Agent Researchers/Readme.txt

@@ -1,27 +1,94 @@
-##CONTEXT
+###Agent Laboratory Research Track & Real-Time Visualization
 
-I have noticed that there has been quite some difficulty in developing this part of the application. This is not acceptable because the LMM researcher is the most important component. It is a critical success factor. I see on the readme file on GitHub that you can choose between the fast track, a quick AI-generated proposal that takes 30 seconds, which is great, and the research track, which involves deep autonomous research with real-time team visualisations. However, it states that the research track currently uses a mock implementation and not real AI researchers, as those require additional Python setup. This issue needs to be addressed for the entire application to function properly. To assist with this, I will provide the website, the academic paper, an example, and the code base in the links below Your task is to study all of these j
 
-## RESOURCES
+###Context
 
-# Website: https://agentlaboratory.github.io/
+The current application distinguishes between two paths:
 
-# Code: https://github.com/SamuelSchmidgall/AgentLaboratory.git
+1.Fast track – quick AI-generated proposal in ≈30 seconds.
+2.Research track – deep autonomous research with real-time multi-agent visualisations.
 
-Use the installation guide to ensure everything is developed correctly. I will also leave the installation guide as its own document in the Resource agent research folder.
+At the moment, the research track relies on a mock implementation rather than real LLM researchers, because the full Agent Laboratory Python pipeline has not been wired in. This is unacceptable given that the LLM researcher is a critical success factor for the entire system. This repository aims to replace the mock path with a faithful integration of Agent Laboratory: Using LLM Agents as Research Assistants.​-https://agentlaboratory.github.io/
 
-# Installation Guide: https://gitdocs1.s3.amazonaws.com/digests/samuelschmidgall-agentlaboratory.git/4bc5a4a6-93e9-447c-ae02-1b72fd2a7da1.txt
+###Upstream Specification & Resources
 
-# Citations:
+Agent Laboratory is an autonomous LLM-based framework that takes a human research idea and produces a research report plus a code repository by progressing through three main phases: (1) Literature Review, (2) Experimentation, and (3) Report Writing - https://github.com/SamuelSchmidgall/AgentLaboratory
 
-@misc{schmidgall2025agentlaboratoryusingllm,
-      title={Agent Laboratory: Using LLM Agents as Research Assistants}, 
-      author={Samuel Schmidgall and Yusheng Su and Ze Wang and Ximeng Sun and Jialian Wu and Xiaodong Yu and Jiang Liu and Michael Moor and Zicheng Liu and Emad Barsoum},
-      year={2025},
-      eprint={2501.04227},
-      archivePrefix={arXiv},
-      primaryClass={cs.HC},
-      url={https://arxiv.org/abs/2501.04227},
+###Authoritative resources:
+
+##Website: https://agentlaboratory.github.io/ – high-level overview of workflow, phases, and agent roles.​
+
+##Code (reference implementation): https://github.com/SamuelSchmidgall/AgentLaboratory.git – Python framework and CLI for running the full pipeline.​ - https://github.com/SamuelSchmidgall/AgentLaboratory
+
+##Installation guide: (mirrored in resources/agent-research/installation-guide.txt) – step-by-step environment setup, dependencies, and commands.
+
+##Paper: Schmidgall et al., Agent Laboratory: Using LLM Agents as Research Assistants, arXiv:2501.04227. - https://arxiv.org/pdf/2501.04227
+
+This project treats the above implementation and paper as the source of truth for the research workflow and agent behavior.
+
+##Goal for This Repository
+Implement the full research track by correctly installing and invoking the upstream Agent Laboratory Python workflow (three phases, mle-solver, paper-solver) from this application.​
+
+Provide a real-time, low-fidelity visualization of the agent team and research stages, so humans can see and grant lab workers in the process as it runs.
+
+The LLM researcher path must be fully functional (no mocks) for the overall system to be considered production-ready.
+
+### Agent Laboratory Real-Time Visualization
+
+Vision
+Low-fidelity pixel-art game view showing the multi-agent research team working in real time, with a breadcrumb trail that demonstrates progress through the 7 research stages. The breadcrumb mockup is stored in the breadcrumb/ folder. The visual goal is to resemble a real academic lab monitoring screen.
+
+Technical Stack
+Engine: Litecanvas (~4KB HTML5 canvas engine) – minimal game-dev complexity.​ - https://github.com/litecanvas/game-engine.git
+
+Game logic: Driven by an SSE stream using the standard EventSource API; each character reflects backend state so users can see how data flows through the system.​
+
+Alternative: Plain Canvas loop (~100 lines of code) if Litecanvas is not desired.
+
+Data source: SSE stream from `/api/research/:id/stream` (emits Agent Laboratory stage and agent updates).
+
+Visual Elements
+Base tiles and props from the Land of Pixels laboratory tileset (included in this repo), plus custom character sprites.
+
+1. Horizontal breadcrumb timeline (7 stages across the top) styled to match the tileset.
+
+2. Agent sprites below the timeline (4–5 researcher characters representing key Agent Laboratory roles).
+
+3. Real-time state updates driven by SSE events.
+
+4. Clicking a stage opens a panel with logs and artefacts for that stage (e.g., retrieved papers, experiment runs, report drafts).
+
+5. There is a section on the side that brings the text following to the visual UI/UX so the user can follow what is happening not only visually.
+
+SSE Event Schema
+json
+
+{
+  "type": "stage_started",        // stage_started | stage_completed | agent_active | agent_idle
+  "stage": "Literature Review",   // one of the 7 research stages
+  "agent": "lit_reviewer",        // agent identifier
+  "message": "Found 12 relevant papers..."
 }
 
-Overview. Agent Laboratory begins with the independent collection and analysis of relevant research papers, progresses through collaborative planning and data preparation, and results in automated experimentation and comprehensive report generation. As shown in Figure 2, the overall workflow consists of three primary phases: (1) Literature Review, (2) Experimentation, and (3) Report Writing. In this section, we will introduce these phases in detail along with the corresponding involved agents. Furthermore, in Section 4, we will conduct qualitative and quantitative analyses to demonstrate the strengths of Agent Laboratory and its ability to generate research.
+All visualisation logic consumes events in this format, independent of the upstream Python implementation.
+
+## Implementation Priority
+Phase 3 (stretch goal) within the broader roadmap.
+
+Non-blocking for Phase 1 (Discovery) and Phase 2 (Research), but required for a complete research-track experience. During discovery and guidelines, documents, topics, and guidelines must be passed to the agent lab team.
+
+Target outcome: approximately 5× faster innovation of new business models and significantly better and more original answers to the questions the research grants are trying to solve.
+
+## Why This Approach Works
+Plug-and-play: Litecanvas is a tiny (~4KB gzipped) drop-in HTML5 canvas engine, so no full game engine is required.​
+
+Real-time by design: SSE is a natural fit for streaming agent events into the viewer without polling, keeping the implementation simple and efficient.​
+
+Debuggable: Agent visualisation and trace tools (such as AgentPrism) show that timeline-style views of agent behaviour can cut debugging time dramatically, motivating this design choice.​
+
+Reusable: The viewer and SSE schema are generic; other Agent Lab–style projects can reuse this component with minimal changes.
+
+## Overview
+
+The top-down game view gives you a live dashboard showing which agents are active, what stage they're in, and lets users drill into artefacts at each stage - turning the "black box" AI research into a transparent, engaging process that builds trust and makes debugging 80% faster. To facilitate this you will create  
+

agent lab.txt

@@ -0,0 +1,27 @@
+##CONTEXT
+
+I have noticed that there has been quite some difficulty in developing this part of the application. This is not acceptable because the LMM researcher is the most important component. It is a critical success factor. I see on the readme file on GitHub that you can choose between the fast track, a quick AI-generated proposal that takes 30 seconds, which is great, and the research track, which involves deep autonomous research with real-time team visualisations. However, it states that the research track currently uses a mock implementation and not real AI researchers, as those require additional Python setup. This issue needs to be addressed for the entire application to function properly. To assist with this, I will provide the website, the academic paper, an example, and the code base in the links below Your task is to study all of these j
+
+## RESOURCES
+
+# Website: https://agentlaboratory.github.io/
+
+# Code: https://github.com/SamuelSchmidgall/AgentLaboratory.git
+
+Use the installation guide to ensure everything is developed correctly. I will also leave the installation guide as its own document in the Resource agent research folder.
+
+# Installation Guide: https://gitdocs1.s3.amazonaws.com/digests/samuelschmidgall-agentlaboratory.git/4bc5a4a6-93e9-447c-ae02-1b72fd2a7da1.txt
+
+# Citations:
+
+@misc{schmidgall2025agentlaboratoryusingllm,
+      title={Agent Laboratory: Using LLM Agents as Research Assistants}, 
+      author={Samuel Schmidgall and Yusheng Su and Ze Wang and Ximeng Sun and Jialian Wu and Xiaodong Yu and Jiang Liu and Michael Moor and Zicheng Liu and Emad Barsoum},
+      year={2025},
+      eprint={2501.04227},
+      archivePrefix={arXiv},
+      primaryClass={cs.HC},
+      url={https://arxiv.org/abs/2501.04227},
+}
+
+Overview. Agent Laboratory begins with the independent collection and analysis of relevant research papers, progresses through collaborative planning and data preparation, and results in automated experimentation and comprehensive report generation. As shown in Figure 2, the overall workflow consists of three primary phases: (1) Literature Review, (2) Experimentation, and (3) Report Writing. In this section, we will introduce these phases in detail along with the corresponding involved agents. Furthermore, in Section 4, we will conduct qualitative and quantitative analyses to demonstrate the strengths of Agent Laboratory and its ability to generate research.

scripts/auto-accept-gemini.ps1

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+# Auto-Accept Gemini VS Code prompts
+# Sends Alt+E (Accept shortcut) to VS Code every 5 seconds
+# Run: powershell -ExecutionPolicy Bypass -File auto-accept-gemini.ps1
+# Stop: Ctrl+C
+
+Add-Type @"
+using System;
+using System.Runtime.InteropServices;
+public class User32 {
+    [DllImport("user32.dll")]
+    public static extern IntPtr GetForegroundWindow();
+    [DllImport("user32.dll")]
+    public static extern bool SetForegroundWindow(IntPtr hWnd);
+    [DllImport("user32.dll", SetLastError=true)]
+    public static extern IntPtr FindWindow(string lpClassName, string lpWindowName);
+    [DllImport("user32.dll")]
+    public static extern int GetWindowText(IntPtr hWnd, System.Text.StringBuilder text, int count);
+    [DllImport("user32.dll")]
+    public static extern bool EnumWindows(EnumWindowsProc lpEnumFunc, IntPtr lParam);
+    public delegate bool EnumWindowsProc(IntPtr hWnd, IntPtr lParam);
+}
+"@
+
+Add-Type -AssemblyName System.Windows.Forms
+
+function Get-VSCodeWindow {
+    $found = $null
+    $callback = [User32+EnumWindowsProc]{
+        param($hWnd, $lParam)
+        $sb = New-Object System.Text.StringBuilder 256
+        [User32]::GetWindowText($hWnd, $sb, 256) | Out-Null
+        $title = $sb.ToString()
+        if ($title -like "*Visual Studio Code*") {
+            $script:found = $hWnd
+            return $false
+        }
+        return $true
+    }
+    [User32]::EnumWindows($callback, [IntPtr]::Zero) | Out-Null
+    return $found
+}
+
+Write-Host "Auto-Accept Gemini running. Press Ctrl+C to stop."
+Write-Host "Sending Alt+E to VS Code every 5 seconds..."
+
+while ($true) {
+    $vsCodeWnd = Get-VSCodeWindow
+    if ($vsCodeWnd) {
+        $currentWnd = [User32]::GetForegroundWindow()
+        [User32]::SetForegroundWindow($vsCodeWnd) | Out-Null
+        Start-Sleep -Milliseconds 200
+        [System.Windows.Forms.SendKeys]::SendWait("%e")  # Alt+E
+        Start-Sleep -Milliseconds 200
+        # Restore previous window
+        if ($currentWnd -ne $vsCodeWnd) {
+            [User32]::SetForegroundWindow($currentWnd) | Out-Null
+        }
+    }
+    Start-Sleep -Seconds 5
+}