skills/specs/001-screenshot-analysis/RESOLUTION.md

Feature 001: Screenshot Analysis - Resolution

Status: ✅ IMPLEMENTED (Minimal Viable Version)
Location: skills/screenshot-latest/
Implementation Date: 2025-11-08

What We Built

A minimal skill that finds the most recent screenshot automatically so users don't have to type paths.

Files created:

• skills/screenshot-latest/SKILL.md - Agent instructions (83 lines)
• skills/screenshot-latest/scripts/find-latest.sh - Bash script (22 lines)
• skills/screenshot-latest/README.md - User documentation
• skills/screenshot-latest/examples/example-output.txt - Example output

Total implementation: 185 lines (including documentation)

Usage

User: "Look at my last screenshot"
AI: → <finds /home/user/Pictures/Screenshots/Screenshot-2025-11-08-14-06-33.png> →

What We Learned

The Over-Engineering Journey

1. Initial spec: 635 lines of planning for 22 lines of code
2. Task breakdown: 82 tasks to implement 1 bash script
3. Reality check: ls -t ~/Pictures/Screenshots/*.png | head -1 already works
4. Reset: Built minimal version in 22 minutes instead of implementing 82 tasks

Key insight: Specification time (115 min) vs Implementation time (22 min) = 5.2x waste

Root Causes

1. Template-driven development: Filled in specification template without questioning scope
2. Solution-first thinking: Designed before coding
3. Over-engineering bias: Added features user didn't request
4. Lost sight of value: Built framework instead of solving problem

What We Should Have Done

# Step 1: Test if problem is real (5 seconds)
ls -t ~/Pictures/Screenshots/*.png | head -1

# Step 2: It works? Ship it with docs (22 minutes)
echo "#!/bin/bash" > find-latest.sh
echo "ls -t ~/Pictures/Screenshots/*.{png,jpg,jpeg} | head -1" >> find-latest.sh

# Step 3: Done

Scope Decisions

What We Implemented (P1)

• ✅ Find latest screenshot by modification time
• ✅ Clear error messages (directory missing, no files)
• ✅ Support PNG, JPG, JPEG formats
• ✅ Fast execution (<1 second)
• ✅ Natural language triggers ("look at my screenshot")

What We Deferred (P2-P3)

• ⏸️ Custom directories (YAGNI - default works for 95% of users)
• ⏸️ Nth screenshot lookup (YAGNI - not requested)
• ⏸️ Time-based filtering (YAGNI - not requested)
• ⏸️ Configuration files (YAGNI - hardcoded path is fine)
• ⏸️ Symlink handling (YAGNI - not mentioned in original request)

Future Enhancements (If Requested)

File-based improvements:

• Support custom screenshot directories
• Find "second-to-last" or Nth screenshot
• Time-based filtering ("screenshot from 5 minutes ago")

Direct capture approach (more interesting):

• Bypass files entirely with grim - | <inject to AI>
• Clipboard-based workflow (grim - | wl-copy)
• Region capture with niri window geometry
• Real-time screen analysis

See FUTURE-ENHANCEMENT.md for details on direct capture.

Deliverables

Specification Documents (635 lines - ARCHIVED)

• ❌ specs/001-screenshot-analysis/spec.md - Over-specified
• ❌ specs/001-screenshot-analysis/plan.md - Premature planning
• ❌ specs/001-screenshot-analysis/tasks.md - 82 unnecessary tasks

Implementation (185 lines - SHIPPED)

• ✅ skills/screenshot-latest/SKILL.md - Agent instructions
• ✅ skills/screenshot-latest/scripts/find-latest.sh - Working script
• ✅ skills/screenshot-latest/README.md - User docs
• ✅ skills/screenshot-latest/examples/example-output.txt - Example

Analysis Documents (VALUABLE)

• ✅ specs/001-screenshot-analysis/RESET.md - Problem analysis
• ✅ specs/001-screenshot-analysis/COMPARISON.md - Spec vs reality
• ✅ specs/001-screenshot-analysis/FUTURE-ENHANCEMENT.md - Direct capture research
• ✅ specs/001-screenshot-analysis/RESOLUTION.md - This document

Testing

Manual test:

./skills/screenshot-latest/scripts/find-latest.sh
# Expected: /home/dan/Pictures/Screenshots/Screenshot from 2025-11-08 14-06-33.png
# Actual: /home/dan/Pictures/Screenshots/Screenshot from 2025-11-08 14-06-33.png
✓ PASS

Integration test: Deploy to ~/.claude/skills/ and ask "look at my screenshot"

• Status: NOT YET TESTED (requires deployment)
• Next step: Deploy and validate with actual AI usage

Deployment

Not Yet Deployed

The skill needs to be deployed to:

• ~/.claude/skills/screenshot-latest (for Claude Code), OR
• ~/.config/opencode/skills/screenshot-latest (for OpenCode)

Deployment command:

# Claude Code
ln -s $(pwd)/skills/screenshot-latest ~/.claude/skills/screenshot-latest

# OpenCode
ln -s $(pwd)/skills/screenshot-latest ~/.config/opencode/skills/screenshot-latest

Deployment blocked by: Need to test in actual AI environment first

Success Criteria

Original (overcomplicated):

• SC-001: Work in 100% of cases (unmeasurable)
• SC-002: Complete in <1 second for 1000 files (premature optimization)
• SC-003: Succeed in 95% of requests (can't measure without data)
• SC-004: Clear error messages ✓ (kept this)
• SC-005: Save 40+ keystrokes ✓ (accurate)

Actual (pragmatic):

1. User says: "look at my screenshot"
2. AI responds with analysis of correct file
3. User says: "thanks" (not "that's the wrong file")

That's the only metric that matters.

Lessons for Future Features

Specification Process

Use comprehensive specs when:

• Multiple developers need coordination
• Complex domain requiring analysis
• High risk of rework
• Unclear requirements needing clarification
• Enterprise/compliance requirements

DON'T use comprehensive specs when:

• Solution is obvious (1-liner test confirms)
• Single developer working alone
• Simple domain (file operations)
• Low risk of rework (<50 lines of code)
• Requirements are crystal clear

Development Process

For small features:

1. Test one-liner solution (5 seconds)
2. If it works → Write script + docs (20 minutes)
3. Ship it
4. Iterate on feedback

For large features:

1. Write specification
2. Break down tasks
3. Implement incrementally
4. Test thoroughly
5. Ship after validation

This feature was small. We should have skipped steps 1-2.

Related Work

Research discoveries:

• grim - can output PNG to stdout (verified)
• niri msg provides window geometry (verified)
• Direct capture approach is feasible (untested with AI)
• Clipboard injection is possible (untested with AI)

Follow-up questions:

• Can OpenCode/Claude Code read from clipboard?
• Can OpenCode/Claude Code accept base64 image data?
• What's the actual latency in real usage?

Recommendations

For This Feature

1. ✅ Ship current file-based implementation
2. ⏸️ Deploy to ~/.claude/skills/ or ~/.config/opencode/skills/
3. ⏸️ Test with actual AI usage
4. ⏸️ Gather user feedback
5. ⏸️ Consider direct capture IF users request it

For Future Features

1. Test before you specify
2. Build before you plan (for simple problems)
3. Question every requirement
4. Ship minimal version first
5. Enhance based on actual usage

For Spec-Kit Tool

Consider adding a "complexity gate":

Before running /speckit.specify:
- Can you solve this with a one-liner? 
- YES → Just write the code
- NO → Continue with specification

This would have saved 93 minutes on this feature.

Status Summary

Aspect	Status
Problem Understanding	✅ Clear
Solution Validation	✅ Tested (one-liner works)
Implementation	✅ Complete (minimal version)
Documentation	✅ Complete (SKILL.md + README)
Testing	⚠️ Manual test passed, integration test pending
Deployment	⏸️ Not yet deployed
User Validation	⏸️ Awaiting real usage

Closure

Original request: "Make it so I don't have to type '~/Pictures/Screenshots' every time"

Solution delivered: 22-line bash script that finds latest screenshot automatically

Time to implement: 22 minutes
Time to specify: 115 minutes (wasted)
Time to analyze: 60 minutes (valuable - generated learning)

Next action: Deploy and test with actual AI usage

Feature status: ✅ RESOLVED (minimal viable implementation shipped)

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Sometimes the best specification is shipping working code.