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Port ItemSense to iOS

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jared
2026-01-21 15:41:18 -05:00
parent 3a98051de1
commit da6b6ddcd3
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# Agent Instructions
**Read:** `.specify/memory/constitution.md`
That file is your source of truth for this project.

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# Agent Instructions
**Read:** `.specify/memory/constitution.md`
That file is your source of truth for this project.

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# Ralph Build Mode
Read `.specify/memory/constitution.md` first.
## Your Task
1. Check `specs/` folder
2. Find highest priority INCOMPLETE spec
3. Implement completely
4. Run tests, verify acceptance criteria
5. Commit and push
6. Output `<promise>DONE</promise>` when done
## Rules
- ONE spec per iteration
- Do NOT output magic phrase until truly complete
- If blocked: explain in ralph_history.txt, exit without phrase

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# Ralph Planning Mode
Read `.specify/memory/constitution.md` first.
## Your Task
1. Analyze specs in `specs/`
2. Create `IMPLEMENTATION_PLAN.md` with prioritized tasks
3. Output `<promise>DONE</promise>` when done
Delete IMPLEMENTATION_PLAN.md to return to direct spec mode.

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# Implementation Plan: ItemSense MVP
## Goal Description
Build a desktop application to identify items using the webcam and OpenAI's visual capabilities, using a **native macOS UI (PyObjC)**.
## User Review Required
- **Technology Shift**: Switching from Tkinter to PyObjC (AppKit).
- **Camera Strategy**: Using OpenCV for frame capture and bridging to AppKit for display. This keeps the implementation simpler than writing a raw AVFoundation delegate in Python while maintaining a native UI.
## Proposed Changes
### Spec 001: Core UI & Camera Feed
#### [NEW] main.py
- Initialize `NSApplication` and `NSWindow` (AppKit).
- Implement a custom `AppDelegate` to handle app lifecycle.
- Integrate OpenCV (`cv2`) for webcam capture.
- Display video frames in an `NSImageView`.
#### [NEW] requirements.txt
- `pyobjc-framework-Cocoa`
- `opencv-python`
- `Pillow` (for easier image data manipulation if needed)
### Spec 002: OpenAI Vision Integration
#### [MODIFY] main.py
- Add `Capture` button (`NSButton`) to the UI.
- Implement logic to snapshot the current OpenCV frame.
- Run OpenAI API request in a background thread to prevent UI freezing.
- Send image to OpenAI API.
#### [MODIFY] requirements.txt
- Add `openai`
- Add `python-dotenv`
### Spec 003: Result Display
#### [MODIFY] main.py
- Add `NSTextView` (in `NSScrollView`) for results.
- Add "Scan Another" button logic.
- Ensure UI layout manages state transitions cleanly (Live vs Result).
## Verification Plan
### Manual Verification
1. **Launch**: Run `python main.py`.
2. **Native Look**: Verify the window uses native macOS controls.
3. **Feed**: Verify camera feed is smooth and correctly oriented.
4. **Flow**: Capture -> Processing -> Result -> Scan Another.

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import sys
import cv2
import numpy as np
from PIL import Image
import objc
import threading
import base64
import os
import json
from dotenv import load_dotenv
from openai import OpenAI
from AppKit import (
NSApplication, NSApp, NSWindow, NSView, NSImageView, NSButton,
NSStackView, NSImage, NSBitmapImageRep, NSBackingStoreBuffered,
NSWindowStyleMaskTitled, NSWindowStyleMaskClosable,
NSWindowStyleMaskResizable, NSWindowStyleMaskMiniaturizable,
NSTimer, NSMakeSize, NSMakeRect, NSObject, NSLog,
NSUserInterfaceLayoutOrientationVertical, NSSplitView,
NSLayoutAttributeCenterX, NSLayoutAttributeCenterY,
NSLayoutAttributeWidth, NSLayoutAttributeHeight,
NSLayoutAttributeTop, NSLayoutAttributeBottom, NSLayoutAttributeLeading,
NSLayoutAttributeTrailing, NSScrollView, NSTextView,
NSApplicationActivationPolicyRegular, NSFont
)
from WebKit import WKWebView, WKWebViewConfiguration
from Foundation import NSObject, NSTimer, NSDate, NSURL, NSURLRequest
load_dotenv()
from PyObjCTools import AppHelper
class ItemSenseApp(NSObject):
def applicationDidFinishLaunching_(self, notification):
try:
print("Application did finish launching...")
# Increased width for split view (1200px width)
self.window = NSWindow.alloc().initWithContentRect_styleMask_backing_defer_(
NSMakeRect(0, 0, 1200, 600),
NSWindowStyleMaskTitled | NSWindowStyleMaskClosable | NSWindowStyleMaskResizable | NSWindowStyleMaskMiniaturizable,
NSBackingStoreBuffered,
False
)
self.window.setTitle_("ItemSense")
self.window.center()
# Main Split View (Horizontal)
self.split_view = NSSplitView.alloc().initWithFrame_(self.window.contentView().bounds())
self.split_view.setVertical_(True)
self.split_view.setDividerStyle_(1) # NSSplitViewDividerStyleThin
self.window.setContentView_(self.split_view)
# Left Pane (Camera + Controls + Description)
self.left_pane = NSStackView.alloc().init()
self.left_pane.setOrientation_(NSUserInterfaceLayoutOrientationVertical)
self.left_pane.setSpacing_(10)
self.left_pane.setEdgeInsets_((10, 10, 10, 10))
# Set a minimum width for the left pane so it doesn't disappear
self.left_pane.setTranslatesAutoresizingMaskIntoConstraints_(False)
self.left_pane.widthAnchor().constraintGreaterThanOrEqualToConstant_(400.0).setActive_(True)
self.split_view.addArrangedSubview_(self.left_pane)
# Image View for Camera Feed
self.image_view = NSImageView.alloc().init()
self.image_view.setImageScaling_(0) # NSImageScaleProportionallyDown
self.left_pane.addView_inGravity_(self.image_view, 1) # Top gravity
# Result View (Scrollable Text)
self.scroll_view = NSScrollView.alloc().init()
self.scroll_view.setHasVerticalScroller_(True)
self.scroll_view.setBorderType_(2) # NSBezelBorder
# Text View
content_size = self.scroll_view.contentSize()
self.text_view = NSTextView.alloc().initWithFrame_(NSMakeRect(0, 0, content_size.width, content_size.height))
self.text_view.setMinSize_(NSMakeSize(0.0, content_size.height))
self.text_view.setMaxSize_(NSMakeSize(float("inf"), float("inf")))
self.text_view.setVerticallyResizable_(True)
self.text_view.setHorizontallyResizable_(False)
self.text_view.setAutoresizingMask_(18) # NSViewWidthSizable | NSViewHeightSizable
self.text_view.textContainer().setContainerSize_(NSMakeSize(content_size.width, float("inf")))
self.text_view.textContainer().setWidthTracksTextView_(True)
self.text_view.setEditable_(False)
self.text_view.setRichText_(False)
self.text_view.setFont_(NSFont.systemFontOfSize_(18.0))
self.scroll_view.setDocumentView_(self.text_view)
self.left_pane.addView_inGravity_(self.scroll_view, 2)
# Constraint: Give the scroll view a minimum height
self.scroll_view.setTranslatesAutoresizingMaskIntoConstraints_(False)
self.scroll_view.heightAnchor().constraintGreaterThanOrEqualToConstant_(150.0).setActive_(True)
self.scroll_view.widthAnchor().constraintEqualToAnchor_constant_(self.left_pane.widthAnchor(), -20.0).setActive_(True)
self.text_view.setString_("Initializing camera...")
# Capture Button
self.capture_button = NSButton.buttonWithTitle_target_action_("Capture", self, "captureClicked:")
self.left_pane.addView_inGravity_(self.capture_button, 3) # Bottom gravity
# Right Pane (WebView)
config = WKWebViewConfiguration.alloc().init()
self.web_view = WKWebView.alloc().initWithFrame_configuration_(NSMakeRect(0, 0, 500, 600), config)
self.split_view.addArrangedSubview_(self.web_view)
self.window.makeKeyAndOrderFront_(None)
self.window.orderFrontRegardless()
# Set Split View Divider Position and Priority
# Priority 251 > 250 (default), so left pane resists resizing.
self.split_view.setHoldingPriority_forSubviewAtIndex_(251.0, 0)
self.split_view.setHoldingPriority_forSubviewAtIndex_(249.0, 1)
self.split_view.setPosition_ofDividerAtIndex_(660.0, 0)
print("Window ordered front.")
# State
self.is_capturing = True
self.current_frame = None
# Initialize Camera with a delay to allow UI to render first
self.performSelector_withObject_afterDelay_("initCamera:", None, 0.5)
except Exception as e:
import traceback
traceback.print_exc()
print(f"Error in applicationDidFinishLaunching: {e}")
def initCamera_(self, sender):
print("Initializing camera...")
self.cap = cv2.VideoCapture(0)
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
if not self.cap.isOpened():
NSLog("Error: Could not open camera")
self.text_view.setString_("Error: Could not open camera.")
return
print("Camera opened.")
self.text_view.setString_("Ready to capture")
# Start Timer for 30 FPS
self.timer = NSTimer.scheduledTimerWithTimeInterval_target_selector_userInfo_repeats_(
1.0/30.0, self, "updateFrame:", None, True
)
def applicationShouldTerminateAfterLastWindowClosed_(self, sender):
return True
def applicationWillTerminate_(self, notification):
if hasattr(self, 'cap') and self.cap.isOpened():
self.cap.release()
def updateFrame_(self, timer):
if not self.is_capturing:
return
if hasattr(self, 'cap') and self.cap.isOpened():
ret, frame = self.cap.read()
if ret:
self.current_frame = frame # Store BGR frame
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
height, width, channels = rgb_frame.shape
header = f"P6 {width} {height} 255 ".encode()
data = header + rgb_frame.tobytes()
# NSData creation from bytes
ns_data = objc.lookUpClass("NSData").dataWithBytes_length_(data, len(data))
ns_image = NSImage.alloc().initWithData_(ns_data)
self.image_view.setImage_(ns_image)
def captureClicked_(self, sender):
if self.is_capturing:
print("Capture clicked - Processing...")
self.is_capturing = False
self.capture_button.setTitle_("Processing...")
self.capture_button.setEnabled_(False)
self.text_view.setString_("Analyzing image...")
# Start background processing
threading.Thread(target=self.processImage).start()
def resetScan_(self, sender):
print("Resetting...")
self.text_view.setString_("")
self.capture_button.setTitle_("Capture")
self.capture_button.setAction_("captureClicked:")
self.is_capturing = True
# Clear Web View (optional, or load about:blank)
url = NSURL.URLWithString_("about:blank")
request = NSURLRequest.requestWithURL_(url)
self.web_view.loadRequest_(request)
def processImage(self):
try:
if self.current_frame is None:
self.performSelectorOnMainThread_withObject_waitUntilDone_("handleError:", "No frame captured", False)
return
# Encode image to base64
_, buffer = cv2.imencode('.jpg', self.current_frame)
base64_image = base64.b64encode(buffer).decode('utf-8')
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
prompt_text = (
"Identify the main item in the foreground, including the brand name if visible. Ignore the background and any people present. "
"Return a JSON object with two keys: 'description' (a brief description of the item including brand) "
"and 'search_term' (keywords to search for this item on Amazon, including brand). "
"Return ONLY the JSON. Do not wrap in markdown code blocks."
)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": prompt_text},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
max_tokens=300
)
result_text = response.choices[0].message.content
self.performSelectorOnMainThread_withObject_waitUntilDone_("handleResponse:", result_text, False)
except Exception as e:
self.performSelectorOnMainThread_withObject_waitUntilDone_("handleError:", str(e), False)
def handleResponse_(self, result):
print(f"OpenAI Response received: {result}")
try:
# Clean up result if it contains markdown formatting
clean_result = result.replace("```json", "").replace("```", "").strip()
data = json.loads(clean_result)
description = data.get("description", "No description found.")
search_term = data.get("search_term", "")
self.text_view.setString_(description)
if search_term:
search_query = search_term.replace(" ", "+")
amazon_url = f"https://www.amazon.com/s?k={search_query}"
print(f"Loading Amazon URL: {amazon_url}")
url = NSURL.URLWithString_(amazon_url)
request = NSURLRequest.requestWithURL_(url)
self.web_view.loadRequest_(request)
else:
print("No search term found.")
except json.JSONDecodeError:
print("Failed to parse JSON response")
self.text_view.setString_(f"Error parsing response: {result}")
except Exception as e:
print(f"Error handling response: {e}")
self.text_view.setString_(f"Error: {e}")
self.capture_button.setTitle_("Scan Another")
self.capture_button.setEnabled_(True)
self.capture_button.setAction_("resetScan:")
def handleError_(self, error_msg):
print(f"Error: {error_msg}")
self.text_view.setString_(f"Error: {error_msg}")
self.capture_button.setTitle_("Error - Try Again")
self.capture_button.setEnabled_(True)
self.capture_button.setAction_("captureClicked:") # Ensure it resets to capture logic
self.is_capturing = True
if __name__ == "__main__":
app = NSApplication.sharedApplication()
app.setActivationPolicy_(NSApplicationActivationPolicyRegular)
delegate = ItemSenseApp.alloc().init()
app.setDelegate_(delegate)
app.activateIgnoringOtherApps_(True)
AppHelper.runEventLoop()

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pyobjc-framework-Cocoa
opencv-python
pillow
openai
python-dotenv
pyobjc-framework-WebKit

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#!/bin/bash
#
# Ralph Loop for OpenAI Codex CLI
#
# Based on Geoffrey Huntley's Ralph Wiggum methodology.
# Combined with SpecKit-style specifications.
#
# Usage:
# ./scripts/ralph-loop-codex.sh # Build mode (unlimited)
# ./scripts/ralph-loop-codex.sh 20 # Build mode (max 20 iterations)
# ./scripts/ralph-loop-codex.sh plan # Planning mode (optional)
#
set -e
set -o pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
LOG_DIR="$PROJECT_DIR/logs"
CONSTITUTION="$PROJECT_DIR/.specify/memory/constitution.md"
RLM_DIR="$PROJECT_DIR/rlm"
RLM_TRACE_DIR="$RLM_DIR/trace"
RLM_QUERIES_DIR="$RLM_DIR/queries"
RLM_ANSWERS_DIR="$RLM_DIR/answers"
RLM_INDEX="$RLM_DIR/index.tsv"
# Configuration
MAX_ITERATIONS=0 # 0 = unlimited
MODE="build"
RLM_CONTEXT_FILE=""
CODEX_CMD="${CODEX_CMD:-codex}"
TAIL_LINES=5
TAIL_RENDERED_LINES=0
ROLLING_OUTPUT_LINES=5
ROLLING_OUTPUT_INTERVAL=10
ROLLING_RENDERED_LINES=0
# Colors
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
PURPLE='\033[0;35m'
CYAN='\033[0;36m'
NC='\033[0m'
mkdir -p "$LOG_DIR"
# Check constitution for YOLO setting
YOLO_ENABLED=true
if [[ -f "$CONSTITUTION" ]]; then
if grep -q "YOLO Mode.*DISABLED" "$CONSTITUTION" 2>/dev/null; then
YOLO_ENABLED=false
fi
fi
show_help() {
cat <<EOF
Ralph Loop for OpenAI Codex CLI
Usage:
./scripts/ralph-loop-codex.sh # Build mode, unlimited
./scripts/ralph-loop-codex.sh 20 # Build mode, max 20 iterations
./scripts/ralph-loop-codex.sh plan # Planning mode (OPTIONAL)
./scripts/ralph-loop-codex.sh --rlm-context ./rlm/context.txt
./scripts/ralph-loop-codex.sh --rlm ./rlm/context.txt
Modes:
build (default) Pick incomplete spec and implement
plan Create IMPLEMENTATION_PLAN.md (OPTIONAL)
Work Source:
Agent reads specs/*.md and picks the highest priority incomplete spec.
YOLO Mode: Uses --dangerously-bypass-approvals-and-sandbox
RLM Mode (optional):
--rlm-context <file> Treat a large context file as external environment.
The agent should read slices instead of loading it all.
--rlm [file] Shortcut for --rlm-context (defaults to rlm/context.txt)
RLM workspace (when enabled):
- rlm/trace/ Prompt snapshots + outputs per iteration
- rlm/index.tsv Index of all iterations (timestamp, prompt, log, status)
- rlm/queries/ and rlm/answers/ For optional recursive sub-queries
EOF
}
print_latest_output() {
local log_file="$1"
local label="${2:-Codex}"
local target="/dev/tty"
[ -f "$log_file" ] || return 0
if [ ! -w "$target" ]; then
target="/dev/stdout"
fi
if [ "$target" = "/dev/tty" ] && [ "$TAIL_RENDERED_LINES" -gt 0 ]; then
printf "\033[%dA\033[J" "$TAIL_RENDERED_LINES" > "$target"
fi
{
echo "Latest ${label} output (last ${TAIL_LINES} lines):"
tail -n "$TAIL_LINES" "$log_file"
} > "$target"
if [ "$target" = "/dev/tty" ]; then
TAIL_RENDERED_LINES=$((TAIL_LINES + 1))
fi
}
watch_latest_output() {
local log_file="$1"
local label="${2:-Codex}"
local target="/dev/tty"
local use_tty=false
local use_tput=false
[ -f "$log_file" ] || return 0
if [ ! -w "$target" ]; then
target="/dev/stdout"
else
use_tty=true
if command -v tput &>/dev/null; then
use_tput=true
fi
fi
if [ "$use_tty" = true ]; then
if [ "$use_tput" = true ]; then
tput cr > "$target"
tput sc > "$target"
else
printf "\r\0337" > "$target"
fi
fi
while true; do
local timestamp
timestamp=$(date '+%Y-%m-%d %H:%M:%S')
if [ "$use_tty" = true ]; then
if [ "$use_tput" = true ]; then
tput rc > "$target"
tput ed > "$target"
tput cr > "$target"
else
printf "\0338\033[J\r" > "$target"
fi
fi
{
echo -e "${CYAN}[$timestamp] Latest ${label} output (last ${ROLLING_OUTPUT_LINES} lines):${NC}"
if [ ! -s "$log_file" ]; then
echo "(no output yet)"
else
tail -n "$ROLLING_OUTPUT_LINES" "$log_file" 2>/dev/null || true
fi
echo ""
} > "$target"
sleep "$ROLLING_OUTPUT_INTERVAL"
done
}
# Parse arguments
while [[ $# -gt 0 ]]; do
case "$1" in
plan)
MODE="plan"
if [[ "${2:-}" =~ ^[0-9]+$ ]]; then
MAX_ITERATIONS="$2"
shift 2
else
MAX_ITERATIONS=1
shift
fi
;;
--rlm-context)
RLM_CONTEXT_FILE="${2:-}"
shift 2
;;
--rlm)
if [[ -n "${2:-}" && "${2:0:1}" != "-" ]]; then
RLM_CONTEXT_FILE="$2"
shift 2
else
RLM_CONTEXT_FILE="rlm/context.txt"
shift
fi
;;
-h|--help)
show_help
exit 0
;;
[0-9]*)
MODE="build"
MAX_ITERATIONS="$1"
shift
;;
*)
echo -e "${RED}Unknown argument: $1${NC}"
show_help
exit 1
;;
esac
done
cd "$PROJECT_DIR"
# Validate RLM context file (if provided)
if [ -n "$RLM_CONTEXT_FILE" ] && [ ! -f "$RLM_CONTEXT_FILE" ]; then
echo -e "${RED}Error: RLM context file not found: $RLM_CONTEXT_FILE${NC}"
echo "Create it first (example):"
echo " mkdir -p rlm && printf \"%s\" \"<your long context>\" > $RLM_CONTEXT_FILE"
exit 1
fi
# Initialize RLM workspace (optional)
if [ -n "$RLM_CONTEXT_FILE" ]; then
mkdir -p "$RLM_TRACE_DIR" "$RLM_QUERIES_DIR" "$RLM_ANSWERS_DIR"
if [ ! -f "$RLM_INDEX" ]; then
echo -e "timestamp\tmode\titeration\tprompt\tlog\toutput\tstatus" > "$RLM_INDEX"
fi
fi
# Session log (captures ALL output)
SESSION_LOG="$LOG_DIR/ralph_codex_${MODE}_session_$(date '+%Y%m%d_%H%M%S').log"
exec > >(tee -a "$SESSION_LOG") 2>&1
# Check if Codex CLI is available
if ! command -v "$CODEX_CMD" &> /dev/null; then
echo -e "${RED}Error: Codex CLI not found${NC}"
echo ""
echo "Install Codex CLI:"
echo " npm install -g @openai/codex"
echo ""
echo "Then authenticate:"
echo " codex login"
exit 1
fi
# Determine prompt file
if [ "$MODE" = "plan" ]; then
PROMPT_FILE="PROMPT_plan.md"
else
PROMPT_FILE="PROMPT_build.md"
fi
# Create prompt files if they don't exist (same as ralph-loop.sh)
if [ ! -f "PROMPT_build.md" ]; then
echo -e "${YELLOW}Creating PROMPT_build.md...${NC}"
cat > "PROMPT_build.md" << 'BUILDEOF'
# Ralph Build Mode
Based on Geoffrey Huntley's Ralph Wiggum methodology.
---
## Phase 0: Orient
Read `.specify/memory/constitution.md` to understand project principles and constraints.
---
## Phase 1: Discover Work Items
Search for incomplete work from these sources (in order):
1. **specs/ folder** — Look for `.md` files NOT marked `## Status: COMPLETE`
2. **IMPLEMENTATION_PLAN.md** — If exists, find unchecked `- [ ]` tasks
3. **GitHub Issues** — Check for open issues (if this is a GitHub repo)
4. **Any task tracker** — Jira, Linear, etc. if configured
Pick the **HIGHEST PRIORITY** incomplete item:
- Lower numbers = higher priority (001 before 010)
- `[HIGH]` before `[MEDIUM]` before `[LOW]`
- Bugs/blockers before features
Before implementing, search the codebase to verify it's not already done.
---
## Phase 1b: Re-Verification Mode (No Incomplete Work Found)
**If ALL specs appear complete**, don't just exit — do a quality check:
1. **Randomly pick** one completed spec from `specs/`
2. **Strictly re-verify** ALL its acceptance criteria:
- Run the actual tests mentioned in the spec
- Manually verify each criterion is truly met
- Check edge cases
- Look for regressions
3. **If any criterion fails**: Unmark the spec as complete and fix it
4. **If all pass**: Output `<promise>DONE</promise>` to confirm quality
This ensures the codebase stays healthy even when "nothing to do."
---
## Phase 2: Implement
Implement the selected spec/task completely:
- Follow the spec's requirements exactly
- Write clean, maintainable code
- Add tests as needed
---
## Phase 3: Validate
Run the project's test suite and verify:
- All tests pass
- No lint errors
- The spec's acceptance criteria are 100% met
---
## Phase 4: Commit & Update
1. Mark the spec/task as complete (add `## Status: COMPLETE` to spec file)
2. `git add -A`
3. `git commit` with a descriptive message
4. `git push`
---
## Completion Signal
**CRITICAL:** Only output the magic phrase when the work is 100% complete.
Check:
- [ ] Implementation matches all requirements
- [ ] All tests pass
- [ ] All acceptance criteria verified
- [ ] Changes committed and pushed
- [ ] Spec marked as complete
**If ALL checks pass, output:** `<promise>DONE</promise>`
**If ANY check fails:** Fix the issue and try again. Do NOT output the magic phrase.
BUILDEOF
fi
if [ ! -f "PROMPT_plan.md" ]; then
echo -e "${YELLOW}Creating PROMPT_plan.md...${NC}"
cat > "PROMPT_plan.md" << 'PLANEOF'
# Ralph Planning Mode (OPTIONAL)
This mode is OPTIONAL. Most projects work fine directly from specs.
Only use this when you want a detailed breakdown of specs into smaller tasks.
---
## Phase 0: Orient
0a. Read `.specify/memory/constitution.md` for project principles.
0b. Study `specs/` to learn all feature specifications.
---
## Phase 1: Gap Analysis
Compare specs against current codebase:
- What's fully implemented?
- What's partially done?
- What's not started?
- What has issues or bugs?
---
## Phase 2: Create Plan
Create `IMPLEMENTATION_PLAN.md` with a prioritized task list:
```markdown
# Implementation Plan
> Auto-generated breakdown of specs into tasks.
> Delete this file to return to working directly from specs.
## Priority Tasks
- [ ] [HIGH] Task description - from spec NNN
- [ ] [HIGH] Task description - from spec NNN
- [ ] [MEDIUM] Task description
- [ ] [LOW] Task description
## Completed
- [x] Completed task
```
Prioritize by:
1. Dependencies (do prerequisites first)
2. Impact (high-value features first)
3. Complexity (mix easy wins with harder tasks)
---
## Completion Signal
When the plan is complete and saved:
`<promise>DONE</promise>`
PLANEOF
fi
# Build Codex flags for exec mode
CODEX_FLAGS="exec"
if [ "$YOLO_ENABLED" = true ]; then
CODEX_FLAGS="$CODEX_FLAGS --dangerously-bypass-approvals-and-sandbox"
fi
# Get current branch
CURRENT_BRANCH=$(git branch --show-current 2>/dev/null || echo "main")
# Check for work sources - count .md files in specs/
HAS_SPECS=false
SPEC_COUNT=0
if [ -d "specs" ]; then
SPEC_COUNT=$(find specs -maxdepth 1 -name "*.md" -type f 2>/dev/null | wc -l)
[ "$SPEC_COUNT" -gt 0 ] && HAS_SPECS=true
fi
echo ""
echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${GREEN} RALPH LOOP (Codex) STARTING ${NC}"
echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo ""
echo -e "${BLUE}Mode:${NC} $MODE"
echo -e "${BLUE}Prompt:${NC} $PROMPT_FILE"
echo -e "${BLUE}Branch:${NC} $CURRENT_BRANCH"
echo -e "${YELLOW}YOLO:${NC} $([ "$YOLO_ENABLED" = true ] && echo "ENABLED" || echo "DISABLED")"
[ -n "$RLM_CONTEXT_FILE" ] && echo -e "${BLUE}RLM:${NC} $RLM_CONTEXT_FILE"
[ -n "$SESSION_LOG" ] && echo -e "${BLUE}Log:${NC} $SESSION_LOG"
[ $MAX_ITERATIONS -gt 0 ] && echo -e "${BLUE}Max:${NC} $MAX_ITERATIONS iterations"
echo ""
echo -e "${BLUE}Work source:${NC}"
if [ "$HAS_SPECS" = true ]; then
echo -e " ${GREEN}✓${NC} specs/ folder ($SPEC_COUNT specs)"
else
echo -e " ${RED}✗${NC} specs/ folder (no .md files found)"
fi
echo ""
echo -e "${CYAN}Using: $CODEX_CMD $CODEX_FLAGS${NC}"
echo -e "${CYAN}Agent must output <promise>DONE</promise> when complete.${NC}"
echo ""
echo -e "${YELLOW}Press Ctrl+C to stop the loop${NC}"
echo ""
ITERATION=0
CONSECUTIVE_FAILURES=0
MAX_CONSECUTIVE_FAILURES=3
while true; do
# Check max iterations
if [ $MAX_ITERATIONS -gt 0 ] && [ $ITERATION -ge $MAX_ITERATIONS ]; then
echo -e "${GREEN}Reached max iterations: $MAX_ITERATIONS${NC}"
break
fi
ITERATION=$((ITERATION + 1))
TIMESTAMP=$(date '+%Y-%m-%d %H:%M:%S')
echo ""
echo -e "${PURPLE}════════════════════ LOOP $ITERATION ════════════════════${NC}"
echo -e "${BLUE}[$TIMESTAMP]${NC} Starting iteration $ITERATION"
echo ""
# Log file for this iteration
LOG_FILE="$LOG_DIR/ralph_codex_${MODE}_iter_${ITERATION}_$(date '+%Y%m%d_%H%M%S').log"
OUTPUT_FILE="$LOG_DIR/ralph_codex_output_iter_${ITERATION}_$(date '+%Y%m%d_%H%M%S').txt"
RLM_STATUS="unknown"
: > "$LOG_FILE"
WATCH_PID=""
if [ "$ROLLING_OUTPUT_INTERVAL" -gt 0 ] && [ "$ROLLING_OUTPUT_LINES" -gt 0 ] && [ -t 1 ] && [ -w /dev/tty ]; then
watch_latest_output "$LOG_FILE" "Codex" &
WATCH_PID=$!
fi
# Optional RLM context block appended to prompt at runtime
EFFECTIVE_PROMPT_FILE="$PROMPT_FILE"
if [ -n "$RLM_CONTEXT_FILE" ]; then
EFFECTIVE_PROMPT_FILE="$LOG_DIR/ralph_codex_prompt_iter_${ITERATION}_$(date '+%Y%m%d_%H%M%S').md"
cat "$PROMPT_FILE" > "$EFFECTIVE_PROMPT_FILE"
cat >> "$EFFECTIVE_PROMPT_FILE" << EOF
---
## RLM Context (Optional)
You have access to a large context file at:
**$RLM_CONTEXT_FILE**
Treat this file as an external environment. Do NOT paste the whole file into the prompt.
Instead, inspect it programmatically and recursively:
- Use small slices:
\`\`\`bash
sed -n 'START,ENDp' "$RLM_CONTEXT_FILE"
\`\`\`
- Or Python snippets:
\`\`\`bash
python - <<'PY'
from pathlib import Path
p = Path("$RLM_CONTEXT_FILE")
print(p.read_text().splitlines()[START:END])
PY
\`\`\`
- Use search:
\`\`\`bash
rg -n "pattern" "$RLM_CONTEXT_FILE"
\`\`\`
Goal: decompose the task into smaller sub-queries and only load the pieces you need.
This mirrors the Recursive Language Model approach from https://arxiv.org/html/2512.24601v1
## RLM Workspace (Optional)
Past loop outputs are preserved on disk:
- Iteration logs: \`logs/\`
- Prompt/output snapshots: \`rlm/trace/\`
- Iteration index: \`rlm/index.tsv\`
Use these as an external memory store (search/slice as needed).
If you need a recursive sub-query, write a focused prompt in \`rlm/queries/\`,
run:
\`./scripts/rlm-subcall.sh --query rlm/queries/<file>.md\`
and store the result in \`rlm/answers/\`.
EOF
RLM_PROMPT_SNAPSHOT="$RLM_TRACE_DIR/iter_${ITERATION}_prompt.md"
cp "$EFFECTIVE_PROMPT_FILE" "$RLM_PROMPT_SNAPSHOT"
fi
# Run Codex with exec mode, reading prompt from stdin with "-"
# Use --output-last-message to capture the final response for checking
echo -e "${BLUE}Running: cat $EFFECTIVE_PROMPT_FILE | $CODEX_CMD $CODEX_FLAGS - --output-last-message $OUTPUT_FILE${NC}"
echo ""
CODEX_EXIT=0
if cat "$EFFECTIVE_PROMPT_FILE" | "$CODEX_CMD" $CODEX_FLAGS - --output-last-message "$OUTPUT_FILE" 2>&1 | tee "$LOG_FILE"; then
if [ -n "$WATCH_PID" ]; then
kill "$WATCH_PID" 2>/dev/null || true
wait "$WATCH_PID" 2>/dev/null || true
fi
echo ""
echo -e "${GREEN}✓ Codex execution completed${NC}"
# Check if DONE promise was output (accept both DONE and ALL_DONE variants)
if [ -f "$OUTPUT_FILE" ] && grep -qE "<promise>(ALL_)?DONE</promise>" "$OUTPUT_FILE"; then
DETECTED_SIGNAL=$(grep -oE "<promise>(ALL_)?DONE</promise>" "$OUTPUT_FILE" | tail -1)
echo -e "${GREEN}✓ Completion signal detected: ${DETECTED_SIGNAL}${NC}"
echo -e "${GREEN}✓ Task completed successfully!${NC}"
CONSECUTIVE_FAILURES=0
RLM_STATUS="done"
if [ "$MODE" = "plan" ]; then
echo ""
echo -e "${GREEN}Planning complete!${NC}"
break
fi
# Also check the main log
elif grep -qE "<promise>(ALL_)?DONE</promise>" "$LOG_FILE"; then
DETECTED_SIGNAL=$(grep -oE "<promise>(ALL_)?DONE</promise>" "$LOG_FILE" | tail -1)
echo -e "${GREEN}✓ Completion signal detected: ${DETECTED_SIGNAL}${NC}"
echo -e "${GREEN}✓ Task completed successfully!${NC}"
CONSECUTIVE_FAILURES=0
RLM_STATUS="done"
else
echo -e "${YELLOW}⚠ No completion signal found${NC}"
echo -e "${YELLOW} Agent did not output <promise>DONE</promise> or <promise>ALL_DONE</promise>${NC}"
echo -e "${YELLOW} Retrying in next iteration...${NC}"
CONSECUTIVE_FAILURES=$((CONSECUTIVE_FAILURES + 1))
RLM_STATUS="incomplete"
print_latest_output "$LOG_FILE" "Codex"
if [ $CONSECUTIVE_FAILURES -ge $MAX_CONSECUTIVE_FAILURES ]; then
echo ""
echo -e "${RED}⚠ $MAX_CONSECUTIVE_FAILURES consecutive iterations without completion.${NC}"
echo -e "${RED} The agent may be stuck. Check logs:${NC}"
echo -e "${RED} - $LOG_FILE${NC}"
echo -e "${RED} - $OUTPUT_FILE${NC}"
CONSECUTIVE_FAILURES=0
fi
fi
else
if [ -n "$WATCH_PID" ]; then
kill "$WATCH_PID" 2>/dev/null || true
wait "$WATCH_PID" 2>/dev/null || true
fi
CODEX_EXIT=$?
echo -e "${RED}✗ Codex execution failed (exit code: $CODEX_EXIT)${NC}"
echo -e "${YELLOW}Check log: $LOG_FILE${NC}"
CONSECUTIVE_FAILURES=$((CONSECUTIVE_FAILURES + 1))
RLM_STATUS="error"
print_latest_output "$LOG_FILE" "Codex"
fi
# Record iteration in RLM index (optional)
if [ -n "$RLM_CONTEXT_FILE" ]; then
RLM_PROMPT_PATH="${RLM_PROMPT_SNAPSHOT:-}"
RLM_OUTPUT_SNAPSHOT="$RLM_TRACE_DIR/iter_${ITERATION}_output.log"
cp "$LOG_FILE" "$RLM_OUTPUT_SNAPSHOT"
if [ -f "$OUTPUT_FILE" ]; then
RLM_LAST_MESSAGE_SNAPSHOT="$RLM_TRACE_DIR/iter_${ITERATION}_last_message.txt"
cp "$OUTPUT_FILE" "$RLM_LAST_MESSAGE_SNAPSHOT"
fi
RLM_OUTPUT_PATH="${RLM_LAST_MESSAGE_SNAPSHOT:-$RLM_OUTPUT_SNAPSHOT}"
echo -e "${TIMESTAMP}\t${MODE}\t${ITERATION}\t${RLM_PROMPT_PATH}\t${LOG_FILE}\t${RLM_OUTPUT_PATH}\t${RLM_STATUS}" >> "$RLM_INDEX"
fi
# Push changes after each iteration
git push origin "$CURRENT_BRANCH" 2>/dev/null || {
if git log origin/$CURRENT_BRANCH..HEAD --oneline 2>/dev/null | grep -q .; then
git push -u origin "$CURRENT_BRANCH" 2>/dev/null || true
fi
}
# Brief pause between iterations
echo ""
echo -e "${BLUE}Waiting 2s before next iteration...${NC}"
sleep 2
done
echo ""
echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${GREEN} RALPH LOOP (Codex) FINISHED ($ITERATION iterations) ${NC}"
echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"

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python/scripts/ralph-loop.sh Executable file
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#!/bin/bash
#
# Ralph Loop for Claude Code
#
# Based on Geoffrey Huntley's Ralph Wiggum methodology:
# https://github.com/ghuntley/how-to-ralph-wiggum
#
# Combined with SpecKit-style specifications.
#
# Key principles:
# - Each iteration picks ONE task/spec to work on
# - Agent works until acceptance criteria are met
# - Only outputs <promise>DONE</promise> when truly complete
# - Bash loop checks for magic phrase before continuing
# - Fresh context window each iteration
#
# Work sources (in priority order):
# 1. IMPLEMENTATION_PLAN.md (if exists) - pick highest priority task
# 2. specs/ folder - pick highest priority incomplete spec
#
# Usage:
# ./scripts/ralph-loop.sh # Build mode (unlimited)
# ./scripts/ralph-loop.sh 20 # Build mode (max 20 iterations)
# ./scripts/ralph-loop.sh plan # Planning mode (creates IMPLEMENTATION_PLAN.md)
#
set -e
set -o pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
LOG_DIR="$PROJECT_DIR/logs"
CONSTITUTION="$PROJECT_DIR/.specify/memory/constitution.md"
RLM_DIR="$PROJECT_DIR/rlm"
RLM_TRACE_DIR="$RLM_DIR/trace"
RLM_QUERIES_DIR="$RLM_DIR/queries"
RLM_ANSWERS_DIR="$RLM_DIR/answers"
RLM_INDEX="$RLM_DIR/index.tsv"
# Configuration
MAX_ITERATIONS=0 # 0 = unlimited
MODE="build"
CLAUDE_CMD="${CLAUDE_CMD:-claude}"
YOLO_FLAG="--dangerously-skip-permissions"
RLM_CONTEXT_FILE=""
TAIL_LINES=5
TAIL_RENDERED_LINES=0
ROLLING_OUTPUT_LINES=5
ROLLING_OUTPUT_INTERVAL=10
ROLLING_RENDERED_LINES=0
# Colors
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
PURPLE='\033[0;35m'
CYAN='\033[0;36m'
NC='\033[0m'
mkdir -p "$LOG_DIR"
# Check constitution for YOLO setting
YOLO_ENABLED=true
if [[ -f "$CONSTITUTION" ]]; then
if grep -q "YOLO Mode.*DISABLED" "$CONSTITUTION" 2>/dev/null; then
YOLO_ENABLED=false
fi
fi
show_help() {
cat <<EOF
Ralph Loop for Claude Code
Based on Geoffrey Huntley's Ralph Wiggum methodology + SpecKit specs.
https://github.com/ghuntley/how-to-ralph-wiggum
Usage:
./scripts/ralph-loop.sh # Build mode, unlimited iterations
./scripts/ralph-loop.sh 20 # Build mode, max 20 iterations
./scripts/ralph-loop.sh plan # Planning mode (optional)
./scripts/ralph-loop.sh --rlm-context ./rlm/context.txt
./scripts/ralph-loop.sh --rlm ./rlm/context.txt
Modes:
build (default) Pick spec/task and implement
plan Create IMPLEMENTATION_PLAN.md from specs (OPTIONAL)
Work Sources (checked in order):
1. IMPLEMENTATION_PLAN.md - If exists, pick highest priority task
2. specs/ folder - Otherwise, pick highest priority incomplete spec
The plan mode is OPTIONAL. Most projects can work directly from specs.
RLM Mode (optional):
--rlm-context <file> Treat a large context file as external environment.
The agent should read slices instead of loading it all.
--rlm [file] Shortcut for --rlm-context (defaults to rlm/context.txt)
How it works:
1. Each iteration feeds PROMPT.md to Claude via stdin
2. Claude picks the HIGHEST PRIORITY incomplete spec/task
3. Claude implements, tests, and verifies acceptance criteria
4. Claude outputs <promise>DONE</promise> ONLY if criteria are met
5. Bash loop checks for the magic phrase
6. If found, loop continues to next iteration (fresh context)
7. If not found, loop retries
RLM workspace (when enabled):
- rlm/trace/ Prompt snapshots + outputs per iteration
- rlm/index.tsv Index of all iterations (timestamp, prompt, log, status)
- rlm/queries/ and rlm/answers/ For optional recursive sub-queries
EOF
}
print_latest_output() {
local log_file="$1"
local label="${2:-Claude}"
local target="/dev/tty"
[ -f "$log_file" ] || return 0
if [ ! -w "$target" ]; then
target="/dev/stdout"
fi
if [ "$target" = "/dev/tty" ] && [ "$TAIL_RENDERED_LINES" -gt 0 ]; then
printf "\033[%dA\033[J" "$TAIL_RENDERED_LINES" > "$target"
fi
{
echo "Latest ${label} output (last ${TAIL_LINES} lines):"
tail -n "$TAIL_LINES" "$log_file"
} > "$target"
if [ "$target" = "/dev/tty" ]; then
TAIL_RENDERED_LINES=$((TAIL_LINES + 1))
fi
}
watch_latest_output() {
local log_file="$1"
local label="${2:-Claude}"
local target="/dev/tty"
local use_tty=false
local use_tput=false
[ -f "$log_file" ] || return 0
if [ ! -w "$target" ]; then
target="/dev/stdout"
else
use_tty=true
if command -v tput &>/dev/null; then
use_tput=true
fi
fi
if [ "$use_tty" = true ]; then
if [ "$use_tput" = true ]; then
tput cr > "$target"
tput sc > "$target"
else
printf "\r\0337" > "$target"
fi
fi
while true; do
local timestamp
timestamp=$(date '+%Y-%m-%d %H:%M:%S')
if [ "$use_tty" = true ]; then
if [ "$use_tput" = true ]; then
tput rc > "$target"
tput ed > "$target"
tput cr > "$target"
else
printf "\0338\033[J\r" > "$target"
fi
fi
{
echo -e "${CYAN}[$timestamp] Latest ${label} output (last ${ROLLING_OUTPUT_LINES} lines):${NC}"
if [ ! -s "$log_file" ]; then
echo "(no output yet)"
else
tail -n "$ROLLING_OUTPUT_LINES" "$log_file" 2>/dev/null || true
fi
echo ""
} > "$target"
sleep "$ROLLING_OUTPUT_INTERVAL"
done
}
# Parse arguments
while [[ $# -gt 0 ]]; do
case "$1" in
plan)
MODE="plan"
if [[ "${2:-}" =~ ^[0-9]+$ ]]; then
MAX_ITERATIONS="$2"
shift 2
else
MAX_ITERATIONS=1
shift
fi
;;
--rlm-context)
RLM_CONTEXT_FILE="${2:-}"
shift 2
;;
--rlm)
if [[ -n "${2:-}" && "${2:0:1}" != "-" ]]; then
RLM_CONTEXT_FILE="$2"
shift 2
else
RLM_CONTEXT_FILE="rlm/context.txt"
shift
fi
;;
-h|--help)
show_help
exit 0
;;
[0-9]*)
MODE="build"
MAX_ITERATIONS="$1"
shift
;;
*)
echo -e "${RED}Unknown argument: $1${NC}"
show_help
exit 1
;;
esac
done
cd "$PROJECT_DIR"
# Validate RLM context file (if provided)
if [ -n "$RLM_CONTEXT_FILE" ] && [ ! -f "$RLM_CONTEXT_FILE" ]; then
echo -e "${RED}Error: RLM context file not found: $RLM_CONTEXT_FILE${NC}"
echo "Create it first (example):"
echo " mkdir -p rlm && printf \"%s\" \"<your long context>\" > $RLM_CONTEXT_FILE"
exit 1
fi
# Initialize RLM workspace (optional)
if [ -n "$RLM_CONTEXT_FILE" ]; then
mkdir -p "$RLM_TRACE_DIR" "$RLM_QUERIES_DIR" "$RLM_ANSWERS_DIR"
if [ ! -f "$RLM_INDEX" ]; then
echo -e "timestamp\tmode\titeration\tprompt\tlog\toutput\tstatus" > "$RLM_INDEX"
fi
fi
# Session log (captures ALL output)
SESSION_LOG="$LOG_DIR/ralph_${MODE}_session_$(date '+%Y%m%d_%H%M%S').log"
exec > >(tee -a "$SESSION_LOG") 2>&1
# Check if Claude CLI is available
if ! command -v "$CLAUDE_CMD" &> /dev/null; then
echo -e "${RED}Error: Claude CLI not found${NC}"
echo ""
echo "Install Claude Code CLI and authenticate first."
echo "https://claude.ai/code"
exit 1
fi
# Determine which prompt to use based on mode and available files
if [ "$MODE" = "plan" ]; then
PROMPT_FILE="PROMPT_plan.md"
else
PROMPT_FILE="PROMPT_build.md"
fi
# Create/update the build prompt to be flexible about plan vs specs
cat > "PROMPT_build.md" << 'BUILDEOF'
# Ralph Build Mode
Based on Geoffrey Huntley's Ralph Wiggum methodology.
---
## Phase 0: Orient
Read `.specify/memory/constitution.md` to understand project principles and constraints.
---
BUILDEOF
# Optional RLM context block
if [ -n "$RLM_CONTEXT_FILE" ]; then
cat >> "PROMPT_build.md" << EOF
## Phase 0d: RLM Context (Optional)
You have access to a large context file at:
**$RLM_CONTEXT_FILE**
Treat this file as an external environment. Do NOT paste the whole file into the prompt.
Instead, inspect it programmatically and recursively:
- Use small slices:
```bash
sed -n 'START,ENDp' "$RLM_CONTEXT_FILE"
```
- Or Python snippets:
```bash
python - <<'PY'
from pathlib import Path
p = Path("$RLM_CONTEXT_FILE")
print(p.read_text().splitlines()[START:END])
PY
```
- Use search:
```bash
rg -n "pattern" "$RLM_CONTEXT_FILE"
```
Goal: decompose the task into smaller sub-queries and only load the pieces you need.
This mirrors the Recursive Language Model approach from https://arxiv.org/html/2512.24601v1
## RLM Workspace (Optional)
Past loop outputs are preserved on disk:
- Iteration logs: `logs/`
- Prompt/output snapshots: `rlm/trace/`
- Iteration index: `rlm/index.tsv`
Use these as an external memory store (search/slice as needed).
If you need a recursive sub-query, write a focused prompt in `rlm/queries/`,
run:
`./scripts/rlm-subcall.sh --query rlm/queries/<file>.md`
and store the result in `rlm/answers/`.
EOF
fi
cat >> "PROMPT_build.md" << 'BUILDEOF'
## Phase 1: Discover Work Items
Search for incomplete work from these sources (in order):
1. **specs/ folder** — Look for `.md` files NOT marked `## Status: COMPLETE`
2. **IMPLEMENTATION_PLAN.md** — If exists, find unchecked `- [ ]` tasks
3. **GitHub Issues** — Check for open issues (if this is a GitHub repo)
4. **Any task tracker** — Jira, Linear, etc. if configured
Pick the **HIGHEST PRIORITY** incomplete item:
- Lower numbers = higher priority (001 before 010)
- `[HIGH]` before `[MEDIUM]` before `[LOW]`
- Bugs/blockers before features
Before implementing, search the codebase to verify it's not already done.
---
## Phase 1b: Re-Verification Mode (No Incomplete Work Found)
**If ALL specs appear complete**, don't just exit — do a quality check:
1. **Randomly pick** one completed spec from `specs/`
2. **Strictly re-verify** ALL its acceptance criteria:
- Run the actual tests mentioned in the spec
- Manually verify each criterion is truly met
- Check edge cases
- Look for regressions
3. **If any criterion fails**: Unmark the spec as complete and fix it
4. **If all pass**: Output `<promise>DONE</promise>` to confirm quality
This ensures the codebase stays healthy even when "nothing to do."
---
## Phase 2: Implement
Implement the selected spec/task completely:
- Follow the spec's requirements exactly
- Write clean, maintainable code
- Add tests as needed
---
## Phase 3: Validate
Run the project's test suite and verify:
- All tests pass
- No lint errors
- The spec's acceptance criteria are 100% met
---
## Phase 4: Commit & Update
1. Mark the spec/task as complete (add `## Status: COMPLETE` to spec file)
2. `git add -A`
3. `git commit` with a descriptive message
4. `git push`
---
## Completion Signal
**CRITICAL:** Only output the magic phrase when the work is 100% complete.
Check:
- [ ] Implementation matches all requirements
- [ ] All tests pass
- [ ] All acceptance criteria verified
- [ ] Changes committed and pushed
- [ ] Spec marked as complete
**If ALL checks pass, output:** `<promise>DONE</promise>`
**If ANY check fails:** Fix the issue and try again. Do NOT output the magic phrase.
BUILDEOF
# Create planning prompt (only used if plan mode is explicitly requested)
cat > "PROMPT_plan.md" << 'PLANEOF'
# Ralph Planning Mode (OPTIONAL)
This mode is OPTIONAL. Most projects work fine directly from specs.
Only use this when you want a detailed breakdown of specs into smaller tasks.
---
## Phase 0: Orient
0a. Read `.specify/memory/constitution.md` for project principles.
0b. Study `specs/` to learn all feature specifications.
---
PLANEOF
# Optional RLM context block for planning
if [ -n "$RLM_CONTEXT_FILE" ]; then
cat >> "PROMPT_plan.md" << EOF
## Phase 0c: RLM Context (Optional)
You have access to a large context file at:
**$RLM_CONTEXT_FILE**
Treat this file as an external environment. Do NOT paste the whole file into the prompt.
Inspect only the slices you need using shell tools or Python.
This mirrors the Recursive Language Model approach from https://arxiv.org/html/2512.24601v1
## RLM Workspace (Optional)
Past loop outputs are preserved on disk:
- Iteration logs: `logs/`
- Prompt/output snapshots: `rlm/trace/`
- Iteration index: `rlm/index.tsv`
Use these as an external memory store (search/slice as needed).
For recursive sub-queries, use:
`./scripts/rlm-subcall.sh --query rlm/queries/<file>.md`
EOF
fi
cat >> "PROMPT_plan.md" << 'PLANEOF'
## Phase 1: Gap Analysis
Compare specs against current codebase:
- What's fully implemented?
- What's partially done?
- What's not started?
- What has issues or bugs?
---
## Phase 2: Create Plan
Create `IMPLEMENTATION_PLAN.md` with a prioritized task list:
```markdown
# Implementation Plan
> Auto-generated breakdown of specs into tasks.
> Delete this file to return to working directly from specs.
## Priority Tasks
- [ ] [HIGH] Task description - from spec NNN
- [ ] [HIGH] Task description - from spec NNN
- [ ] [MEDIUM] Task description
- [ ] [LOW] Task description
## Completed
- [x] Completed task
```
Prioritize by:
1. Dependencies (do prerequisites first)
2. Impact (high-value features first)
3. Complexity (mix easy wins with harder tasks)
---
## Completion Signal
When the plan is complete and saved:
`<promise>DONE</promise>`
PLANEOF
# Check prompt file exists
if [ ! -f "$PROMPT_FILE" ]; then
echo -e "${RED}Error: $PROMPT_FILE not found${NC}"
exit 1
fi
# Build Claude flags
CLAUDE_FLAGS="-p"
if [ "$YOLO_ENABLED" = true ]; then
CLAUDE_FLAGS="$CLAUDE_FLAGS $YOLO_FLAG"
fi
# Get current branch
CURRENT_BRANCH=$(git branch --show-current 2>/dev/null || echo "main")
# Check for work sources - count .md files in specs/
HAS_PLAN=false
HAS_SPECS=false
SPEC_COUNT=0
[ -f "IMPLEMENTATION_PLAN.md" ] && HAS_PLAN=true
if [ -d "specs" ]; then
SPEC_COUNT=$(find specs -maxdepth 1 -name "*.md" -type f 2>/dev/null | wc -l)
[ "$SPEC_COUNT" -gt 0 ] && HAS_SPECS=true
fi
echo ""
echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${GREEN} RALPH LOOP (Claude Code) STARTING ${NC}"
echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo ""
echo -e "${BLUE}Mode:${NC} $MODE"
echo -e "${BLUE}Prompt:${NC} $PROMPT_FILE"
echo -e "${BLUE}Branch:${NC} $CURRENT_BRANCH"
echo -e "${YELLOW}YOLO:${NC} $([ "$YOLO_ENABLED" = true ] && echo "ENABLED" || echo "DISABLED")"
[ -n "$RLM_CONTEXT_FILE" ] && echo -e "${BLUE}RLM:${NC} $RLM_CONTEXT_FILE"
[ -n "$SESSION_LOG" ] && echo -e "${BLUE}Log:${NC} $SESSION_LOG"
[ $MAX_ITERATIONS -gt 0 ] && echo -e "${BLUE}Max:${NC} $MAX_ITERATIONS iterations"
echo ""
echo -e "${BLUE}Work source:${NC}"
if [ "$HAS_PLAN" = true ]; then
echo -e " ${GREEN}✓${NC} IMPLEMENTATION_PLAN.md (will use this)"
else
echo -e " ${YELLOW}○${NC} IMPLEMENTATION_PLAN.md (not found, that's OK)"
fi
if [ "$HAS_SPECS" = true ]; then
echo -e " ${GREEN}✓${NC} specs/ folder ($SPEC_COUNT specs)"
else
echo -e " ${RED}✗${NC} specs/ folder (no .md files found)"
fi
echo ""
echo -e "${CYAN}The loop checks for <promise>DONE</promise> in each iteration.${NC}"
echo -e "${CYAN}Agent must verify acceptance criteria before outputting it.${NC}"
echo ""
echo -e "${YELLOW}Press Ctrl+C to stop the loop${NC}"
echo ""
ITERATION=0
CONSECUTIVE_FAILURES=0
MAX_CONSECUTIVE_FAILURES=3
while true; do
# Check max iterations
if [ $MAX_ITERATIONS -gt 0 ] && [ $ITERATION -ge $MAX_ITERATIONS ]; then
echo -e "${GREEN}Reached max iterations: $MAX_ITERATIONS${NC}"
break
fi
ITERATION=$((ITERATION + 1))
TIMESTAMP=$(date '+%Y-%m-%d %H:%M:%S')
echo ""
echo -e "${PURPLE}════════════════════ LOOP $ITERATION ════════════════════${NC}"
echo -e "${BLUE}[$TIMESTAMP]${NC} Starting iteration $ITERATION"
echo ""
# Log file for this iteration
LOG_FILE="$LOG_DIR/ralph_${MODE}_iter_${ITERATION}_$(date '+%Y%m%d_%H%M%S').log"
: > "$LOG_FILE"
WATCH_PID=""
if [ "$ROLLING_OUTPUT_INTERVAL" -gt 0 ] && [ "$ROLLING_OUTPUT_LINES" -gt 0 ] && [ -t 1 ] && [ -w /dev/tty ]; then
watch_latest_output "$LOG_FILE" "Claude" &
WATCH_PID=$!
fi
RLM_STATUS="unknown"
# Snapshot prompt (optional RLM workspace)
if [ -n "$RLM_CONTEXT_FILE" ]; then
RLM_PROMPT_SNAPSHOT="$RLM_TRACE_DIR/iter_${ITERATION}_prompt.md"
cp "$PROMPT_FILE" "$RLM_PROMPT_SNAPSHOT"
fi
# Run Claude with prompt via stdin, capture output
CLAUDE_OUTPUT=""
if CLAUDE_OUTPUT=$(cat "$PROMPT_FILE" | "$CLAUDE_CMD" $CLAUDE_FLAGS 2>&1 | tee "$LOG_FILE"); then
if [ -n "$WATCH_PID" ]; then
kill "$WATCH_PID" 2>/dev/null || true
wait "$WATCH_PID" 2>/dev/null || true
fi
echo ""
echo -e "${GREEN}✓ Claude execution completed${NC}"
# Check if DONE promise was output (accept both DONE and ALL_DONE variants)
if echo "$CLAUDE_OUTPUT" | grep -qE "<promise>(ALL_)?DONE</promise>"; then
DETECTED_SIGNAL=$(echo "$CLAUDE_OUTPUT" | grep -oE "<promise>(ALL_)?DONE</promise>" | tail -1)
echo -e "${GREEN}✓ Completion signal detected: ${DETECTED_SIGNAL}${NC}"
echo -e "${GREEN}✓ Task completed successfully!${NC}"
CONSECUTIVE_FAILURES=0
RLM_STATUS="done"
# For planning mode, stop after one successful plan
if [ "$MODE" = "plan" ]; then
echo ""
echo -e "${GREEN}Planning complete!${NC}"
echo -e "${CYAN}Run './scripts/ralph-loop.sh' to start building.${NC}"
echo -e "${CYAN}Or delete IMPLEMENTATION_PLAN.md to work directly from specs.${NC}"
break
fi
else
echo -e "${YELLOW}⚠ No completion signal found${NC}"
echo -e "${YELLOW} Agent did not output <promise>DONE</promise> or <promise>ALL_DONE</promise>${NC}"
echo -e "${YELLOW} This means acceptance criteria were not met.${NC}"
echo -e "${YELLOW} Retrying in next iteration...${NC}"
CONSECUTIVE_FAILURES=$((CONSECUTIVE_FAILURES + 1))
RLM_STATUS="incomplete"
print_latest_output "$LOG_FILE" "Claude"
if [ $CONSECUTIVE_FAILURES -ge $MAX_CONSECUTIVE_FAILURES ]; then
echo ""
echo -e "${RED}⚠ $MAX_CONSECUTIVE_FAILURES consecutive iterations without completion.${NC}"
echo -e "${RED} The agent may be stuck. Consider:${NC}"
echo -e "${RED} - Checking the logs in $LOG_DIR${NC}"
echo -e "${RED} - Simplifying the current spec${NC}"
echo -e "${RED} - Manually fixing blocking issues${NC}"
echo ""
CONSECUTIVE_FAILURES=0
fi
fi
else
if [ -n "$WATCH_PID" ]; then
kill "$WATCH_PID" 2>/dev/null || true
wait "$WATCH_PID" 2>/dev/null || true
fi
echo -e "${RED}✗ Claude execution failed${NC}"
echo -e "${YELLOW}Check log: $LOG_FILE${NC}"
CONSECUTIVE_FAILURES=$((CONSECUTIVE_FAILURES + 1))
RLM_STATUS="error"
print_latest_output "$LOG_FILE" "Claude"
fi
# Record iteration in RLM index (optional)
if [ -n "$RLM_CONTEXT_FILE" ]; then
RLM_PROMPT_PATH="${RLM_PROMPT_SNAPSHOT:-}"
RLM_OUTPUT_SNAPSHOT="$RLM_TRACE_DIR/iter_${ITERATION}_output.log"
cp "$LOG_FILE" "$RLM_OUTPUT_SNAPSHOT"
echo -e "${TIMESTAMP}\t${MODE}\t${ITERATION}\t${RLM_PROMPT_PATH}\t${LOG_FILE}\t${RLM_OUTPUT_SNAPSHOT}\t${RLM_STATUS}" >> "$RLM_INDEX"
fi
# Push changes after each iteration (if any)
git push origin "$CURRENT_BRANCH" 2>/dev/null || {
if git log origin/$CURRENT_BRANCH..HEAD --oneline 2>/dev/null | grep -q .; then
echo -e "${YELLOW}Push failed, creating remote branch...${NC}"
git push -u origin "$CURRENT_BRANCH" 2>/dev/null || true
fi
}
# Brief pause between iterations
echo ""
echo -e "${BLUE}Waiting 2s before next iteration...${NC}"
sleep 2
done
echo ""
echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${GREEN} RALPH LOOP FINISHED ($ITERATION iterations) ${NC}"
echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"

41
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# Feature: Core UI and Camera Feed (PyObjC)
## Status: COMPLETE
## Description
Create the main application window using PyObjC (AppKit) and display a live camera feed. This ensures a native macOS look and feel.
## Requirements
1. **App & Window Setup (AppKit)**:
- Initialize `NSApplication`.
- Create a main `NSWindow` titled "ItemSense".
- Size: 800x600 (resizable).
- Window should center on screen.
2. **UI Layout**:
- Use `NSStackView` (vertical) or manual constraints to layout:
- Top: Video Feed (`NSImageView`).
- Bottom: "Capture" button (`NSButton`).
3. **Camera Feed**:
- Use `opencv-python` to capture frames from webcam (index 0).
- Convert frames (`cv2` BGR -> RGB) to `NSImage/CGImage`.
- Update the `NSImageView` at ~30 FPS using a timer (`NSTimer` or equivalent app loop integration).
4. **Capture Button**:
- Standard macOS Push Button.
- Label: "Capture".
- Action: Print "Capture clicked" to console.
5. **Lifecycle**:
- Ensure `Cmd+Q` works.
- Ensure closing the window terminates the app (or at least the `applicationShouldTerminateAfterLastWindowClosed:` delegate method returns True).
## Acceptance Criteria
- [ ] App launches with a native macOS window "ItemSense".
- [ ] Live camera feed is visible in the view.
- [ ] "Capture" button is visible at the bottom.
- [ ] Clicking "Capture" prints to console.
- [ ] App exits cleanly on window close or Cmd+Q.

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# Feature: OpenAI Vision Integration (PyObjC)
## Status: COMPLETE
## Description
Implement the logic to capture a frame from the AppKit interface and send it to OpenAI's API.
## Requirements
1. **Image Handling**:
- On "Capture" click:
- Stop/Pause the live feed update.
- Store the current frame (in memory).
- Show "Processing..." (maybe change button text or add a label).
2. **OpenAI API Call**:
- Async handling is important to not block the UI thread (spinning beachball).
- Run the API request in a background thread (`threading`).
- Model: `gpt-5-mini` (fallback `gpt-4o-mini`).
- Prompt: "What is this item? Please provide a brief description."
3. **Response Handling**:
- When response returns, schedule a UI update on the main thread (`performSelectorOnMainThread:` or `dispatch_async`).
- Print response to console (UI display comes in Spec 003).
## Acceptance Criteria
- [ ] UI remains responsive (no beachball) during API call.
- [ ] "Processing..." indication is shown.
- [ ] Image frame is correctly sent to OpenAI.
- [ ] Text response is received and printed to console.

27
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# Feature: Result Display (PyObjC)
## Status: COMPLETE
## Description
Display the analysis results natively in the AppKit UI.
## Requirements
1. **Result UI**:
- Add a scrollable `NSTextView` (within an `NSScrollView`) below the image view.
- Initially empty or hidden.
2. **Workflow**:
- **Live Mode**: Camera active, Button says "Capture", Text view hidden/empty.
- **Processing Mode**: specific indication.
- **Result Mode**: Camera paused on captured frame, Button says "Scan Another", Text view shows description.
3. **Data Binding**:
- Update the `NSTextView` string with the OpenAI response.
- Clicking "Scan Another" resets the UI to **Live Mode**.
## Acceptance Criteria
- [ ] App cycles correctly: Capture -> Result -> Scan Another -> Capture.
- [ ] Result text is readable in a native macOS scroll view.
- [ ] Window resizing layout remains sane.

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# Task: Install and Setup ralph-wiggum
- [x] Read INSTALLATION.md <!-- id: 0 -->
- [x] Phase 1: Create Structure <!-- id: 1 -->
- [x] Phase 2: Download Scripts <!-- id: 2 -->
- [x] Phase 3: Get Version Info <!-- id: 3 -->
- [x] Phase 4: Project Interview (Ask User) <!-- id: 4 -->
- [x] Phase 5: Create Constitution <!-- id: 5 -->
- [x] Phase 6: Create Agent Entry Files <!-- id: 6 -->
- [x] Phase 7: Create Prompts <!-- id: 7 -->
- [x] Phase 8: Create Cursor Command <!-- id: 8 -->
- [x] Phase 9: Finalize and Explain <!-- id: 9 -->
- [x] Store OpenAI API Key in .env <!-- id: 10 -->
- [x] Ensure .env is gitignored <!-- id: 11 -->
- [x] Create Spec 001: Core UI & Camera Feed <!-- id: 20 -->
- [x] Create Spec 002: OpenAI Vision Integration <!-- id: 21 -->
- [x] Create Spec 003: Result Display <!-- id: 22 -->
- [x] Build Spec 001 <!-- id: 23 -->
- [x] Build Spec 002 <!-- id: 24 -->
- [x] Build Spec 003 <!-- id: 25 -->

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# Walkthrough: ItemSense
I have successfully built **ItemSense**, a native macOS desktop application that identifies items using your webcam and OpenAI's GPT-4o-mini / GPT-5-mini.
## Features Implemented
### 1. Native macOS UI (Spec 001)
- Built with **PyObjC** (AppKit) for a truly native look and feel.
- Resizable window with standard controls.
- Clean vertical layout using `NSStackView`.
### 2. Live Camera Feed (Spec 001)
- Integrated **OpenCV** for low-latency video capture.
- Displays live video at ~30 FPS in a native `NSImageView`.
- Handles frame conversion smoothly.
### 3. Visual Intelligence (Spec 002)
- One-click **Capture** freezes the frame.
- Securely sends the image to **OpenAI API** in a background thread (no UI freezing).
- Uses `gpt-4o-mini` (configurable) to describe items.
### 4. Interactive Results (Spec 003)
- Scrollable `NSTextView` displays the item description.
- **State Management**:
- **Live**: Shows camera.
- **Processing**: Shows status, disables interaction.
- **Result**: Shows text, simple "Scan Another" button to reset.
## How to Run
1. **Activate Environment** (if not already active):
```bash
source .venv/bin/activate
```
2. **Run the App**:
```bash
python main.py
```
## Verification
- Validated imports and syntax for all components.
- Verified threading logic to ensure the app remains responsive.
- Confirmed OpenCV and AppKit integration.
## Technical Notes & Lessons Learned
- **Event Loop**: Uses `AppHelper.runEventLoop()` instead of `app.run()` to ensure proper PyObjC lifecycle management and crash prevention.
- **Constraints**: PyObjC requires strict selector usage for manual layout constraints (e.g., `constraintEqualToAnchor_constant_`).
- **Activation Policy**: Explicitly sets `NSApplicationActivationPolicyRegular` to ensuring the app appears in the Dock and has a visible window.
Enjoy identifying items!