#+TITLE: Screenshot Analysis Feature: Over-Engineering Discovery and Wayland Capture Research
#+DATE: 2025-11-08
#+KEYWORDS: screenshot, wayland, grim, niri, over-engineering, specification, direct-capture
#+COMMITS: 1
#+COMPRESSION_STATUS: uncompressed

* Session Summary
** Date: 2025-11-08 (Day 2 of screenshot-analysis feature)
** Focus Area: Screenshot analysis skill implementation - discovered massive over-engineering, pivoted to minimal implementation and Wayland direct capture research

* Accomplishments
- [X] Identified severe over-engineering in specification (635 lines of planning for 22 lines of code)
- [X] Built minimal viable screenshot-latest skill (185 lines total including docs)
- [X] Tested and verified find-latest.sh script works correctly
- [X] Researched Wayland screencopy protocol capabilities with grim
- [X] Discovered niri overview mode enables capturing inactive workspace windows
- [X] Verified AI can read PNG images directly from temp files
- [X] Created comprehensive analysis documents (RESET.md, COMPARISON.md, RESOLUTION.md)
- [X] Documented future enhancement path for direct screen capture
- [ ] Deploy skill to ~/.claude/skills/ (pending user testing)
- [ ] Test skill in actual AI workflow (pending deployment)

* Key Decisions

** Decision 1: Abort 82-task specification, ship minimal implementation
- Context: Previous session generated 635 lines of specification with 82 implementation tasks for what turned out to be a 22-line bash script
- Options considered:
  1. Continue with comprehensive specification approach (4 scripts, full test coverage, config system)
  2. Build minimal version first, validate with users, enhance if needed
  3. Abandon feature entirely as over-engineered
- Rationale: One-liner test `ls -t ~/Pictures/Screenshots/*.png | head -1` proved the core functionality already works. User requested "don't make me type paths" - minimal solution solves exactly that.
- Impact: Reduced implementation from estimated 200 lines of code + tests to 22 lines of working bash + 83 lines of documentation. Saves ~2-3 hours of implementation time.

** Decision 2: Use file-based approach instead of direct capture for MVP
- Context: Discovered `grim - ` can output PNG to stdout, enabling clipboard or direct injection workflows
- Options considered:
  1. File-based: `ls -t ~/Pictures/Screenshots/*.png | head -1` (proven to work)
  2. Clipboard-based: `grim - | wl-copy` then AI reads from clipboard (unknown if AI supports)
  3. Direct injection: `grim - | base64 | <inject to AI>` (unknown if possible)
  4. Temp file capture: `grim /tmp/screen.png` (works but adds file I/O)
- Rationale: File-based approach is proven, solves stated user problem, no unknown dependencies. Direct capture requires AI integration research that blocks MVP.
- Impact: Can ship working solution immediately. Direct capture documented as future enhancement if users request lower latency or real-time capture.

** Decision 3: Document over-engineering lessons rather than hide the mistake
- Context: Spent 115 minutes on specification vs 22 minutes on implementation (5.2x waste)
- Options considered:
  1. Delete spec files and pretend they never happened
  2. Keep spec files but don't document the failure
  3. Create detailed analysis documents showing what went wrong and why
- Rationale: This is valuable learning about when to specify vs when to code first. Future features can reference this decision framework.
- Impact: Created RESET.md, COMPARISON.md, RESOLUTION.md documenting the over-engineering trap and how to avoid it. These become reference material for future scope decisions.

** Decision 4: Investigate Wayland capture limitations vs compositor capabilities
- Context: User asked if inactive workspace windows can be captured - unclear if limitation is "not rendered" vs "security restriction"
- Options considered:
  1. Accept that Wayland can't capture inactive workspaces
  2. Research compositor-specific capabilities (niri overview mode)
  3. Look for alternative protocols or tools
- Rationale: Understanding the actual limitation determines what's possible. If compositor renders it for overview, we can capture it.
- Impact: Discovered niri overview mode DOES render inactive workspace windows, making multi-workspace capture possible via brief overview toggle. Opens up new use cases like "find window with error message across all workspaces".

* Problems & Solutions

| Problem | Solution | Learning |
|---------|----------|----------|
| 635 lines of specification for 22 lines of code - massive scope creep | Tested one-liner solution first: `ls -t ~/Pictures/Screenshots/*.png \| head -1` works perfectly. Shipped minimal implementation. | Always validate problem with simplest solution before writing comprehensive specs. For obvious problems (file finding), code IS the specification. |
| Spec template drove over-engineering - filling sections created unnecessary requirements | Created "complexity gate" recommendation: ask "can you solve this with a one-liner?" before running /speckit.specify | Spec tools are powerful but dangerous for simple problems. Template-driven development can create work that doesn't need to exist. |
| Unclear if Wayland screencopy limitation is rendering or security | Researched protocol, tested niri overview mode. Found overview renders ALL workspace windows, enabling capture via `niri msg action toggle-overview && grim && toggle-overview` | Wayland limitation is "not rendered" not "security blocked". Compositor design choice (keeping thumbnail buffers) determines what's capturable. |
| Don't know if AI can read from clipboard or stdin for images | Tested with temp file: `grim /tmp/test.png` → Read tool successfully loads and displays image | AI (OpenCode/Claude) CAN read PNG files directly. File-based approach works, no need to research clipboard/stdin for MVP. |
| Overview mode toggle causes ~450ms visible flicker | Measured timing, checked animation config. Flicker is inherent to rendering overview for capture. | Invisible capture requires either: 1) compositor thumbnail buffers (not in niri), 2) metadata only (no visuals), or 3) accept brief flicker. Physics/Wayland security model - can't capture what's not rendered. |

* Technical Details

** Code Changes
- Total files created: 9 (4 implementation, 5 analysis)
- Key files created:
  - `skills/screenshot-latest/SKILL.md` - Agent instructions for finding latest screenshot (83 lines)
  - `skills/screenshot-latest/scripts/find-latest.sh` - Bash script to find most recent screenshot (22 lines)
  - `skills/screenshot-latest/README.md` - User documentation
  - `skills/screenshot-latest/examples/example-output.txt` - Example output
  - `specs/001-screenshot-analysis/RESET.md` - Over-engineering analysis
  - `specs/001-screenshot-analysis/COMPARISON.md` - Spec vs implementation reality check (1400 lines)
  - `specs/001-screenshot-analysis/RESOLUTION.md` - Feature closure document
  - `specs/001-screenshot-analysis/FUTURE-ENHANCEMENT.md` - Direct capture research
  - `AGENTS.md` - Auto-generated agent context file
- Spec files archived but not deleted:
  - `specs/001-screenshot-analysis/spec.md` (165 lines - over-specified)
  - `specs/001-screenshot-analysis/plan.md` (139 lines - premature)
  - `specs/001-screenshot-analysis/tasks.md` (331 lines - 82 unnecessary tasks)

** Commands Used

Finding latest screenshot (the core solution):
```bash
ls -t ~/Pictures/Screenshots/*.{png,jpg,jpeg} 2>/dev/null | head -1
# Returns: /home/dan/Pictures/Screenshots/Screenshot from 2025-11-08 14-06-33.png
```

Testing grim stdout capability:
```bash
grim -g "0,0 100x100" - | file -
# Output: /dev/stdin: PNG image data, 174 x 174, 8-bit/color RGBA, non-interlaced
# Proves grim can output PNG to stdout for direct capture workflows
```

Testing grim to base64 pipeline:
```bash
grim -g "0,0 100x100" - | base64 | head -c 80
# Output: iVBORw0KGgoAAAANSUhEUgAAAK4AAACuCAYAAACvDDbuAAAgAElEQVR4nO2dd3hc1Z33P7dMk6ao...
# Proves base64 encoding works for potential direct injection
```

Capturing during niri overview mode:
```bash
niri msg action toggle-overview
sleep 0.1
grim /tmp/overview-test.png
niri msg action toggle-overview
# Successfully captured all workspace windows in overview (~450ms flicker)
```

Getting window metadata from niri:
```bash
niri msg --json windows | jq -r '.[] | "\(.id) - \(.title) - Workspace: \(.workspace_id)"'
# Lists all windows with IDs, titles, workspace assignments
# Metadata available without visual capture
```

** Architecture Notes

Skills structure (validated):
- Each skill is a directory under `skills/`
- `SKILL.md` with YAML frontmatter contains agent instructions
- Optional `scripts/` directory for helper scripts
- Optional `templates/` and `examples/` directories
- Skills deployed to `~/.claude/skills/` or `~/.config/opencode/skills/`
- Agent auto-discovers based on `description` field and "When to Use" section

Wayland screencopy protocol limitations:
- Only captures currently visible screen buffers
- Windows on inactive workspaces are not rendered → not capturable
- Compositor design choice whether to maintain thumbnail buffers
- niri overview mode IS a render pass → windows become capturable during overview
- No way to capture without making content visible (security by design)

Direct capture workflow possibilities:
1. Temp file (proven): `grim /tmp/screen.png` → AI reads with Read tool
2. Clipboard (untested): `grim - | wl-copy` → AI reads with `wl-paste`?
3. Base64 stdin (untested): `grim - | base64` → AI accepts as image data?
4. Overview toggle (proven): Brief flicker enables multi-workspace capture

* Process and Workflow

** What Worked Well
- Testing one-liner solution BEFORE writing comprehensive spec (should have done this in session 1)
- Creating analysis documents (RESET.md, COMPARISON.md) to capture learning
- Using actual numbers (635 lines spec vs 22 lines code) to demonstrate over-engineering
- Hands-on testing with grim, niri, and Read tool to validate capabilities
- Documenting future enhancements separately so they don't block MVP
- Keeping spec files as "what not to do" examples rather than deleting

** What Was Challenging
- Recognizing the over-engineering early enough (took 5 sessions to catch it)
- Resisting the pull to "do it properly" with comprehensive specs
- Admitting that 115 minutes of specification work should be abandoned
- Distinguishing between "thorough planning" and "planning theater"
- Balancing documentation quality (these analysis docs are also long!) with shipping
- Investigating Wayland compositor internals to understand actual limitations

** What I Would Do Differently
- Test the one-liner solution in Session 1 before opening the spec template
- Use complexity gate: "Can this be solved with <50 lines of code? Just write it."
- Question every spec template section: "What happens if I skip this?"
- Ship code first for simple problems, document after it works
- Research actual constraints (Wayland protocol) before designing solutions

* Learning and Insights

** Technical Insights

Wayland security model and rendering:
- Wayland's "not rendered = not capturable" is a feature, not a bug
- Prevents background window spying (security win)
- Compositors choose whether to keep thumbnail buffers (GNOME/KDE do, niri doesn't by default)
- Overview modes are actual render passes, making capture possible
- ~450ms flicker is unavoidable if overview has animations

grim capabilities:
- Can output PNG to stdout with `grim -` (opens direct injection possibilities)
- Supports region capture with `-g "x,y WxH"` syntax
- Supports specific output/monitor capture with `-o <output-name>`
- Supports window capture with `-T <toplevel-id>` IF window is visible
- Works with any Wayland compositor supporting screencopy protocol

AI image handling:
- Read tool can directly ingest PNG files from any path
- No need for clipboard or base64 encoding for file-based approach
- Temp file approach (`/tmp/screen-*.png`) works perfectly
- Opens door to "capture now, analyze immediately" workflows

** Process Insights

Specification vs implementation balance:
- Comprehensive specs valuable when: multiple teams, complex domain, high rework risk, unclear requirements
- Code-first appropriate when: obvious solution, single developer, simple domain, low rework risk
- This feature was code-first scenario treated as spec-first (root cause of waste)
- 5.2x time waste (115 min spec vs 22 min implement) is the cost of wrong approach

Template-driven development risks:
- Templates create pressure to fill in every section
- Answering template questions feels productive but may create unnecessary work
- `/speckit.specify` tool powerful but needs complexity gate
- "Did you test if this already works?" should be first question

Over-engineering indicators:
- Task breakdown longer than expected code (82 tasks for 22-line script)
- Configuration system for single constant value
- Comprehensive test coverage before code exists
- Features user didn't request ("time-based filtering", "Nth screenshot")
- Specification longer than implementation (635 vs 185 lines)

** Architectural Insights

Skills as agent interface:
- SKILL.md is essentially an API contract for agent behavior
- "When to Use" section is trigger detection logic
- Helper scripts are implementation details agent can invoke
- Skills compose (can reference other skills)
- Deployment via symlink enables version control + system integration

Direct capture architectural patterns:
- File-based: Proven, simple, works now (chosen for MVP)
- Clipboard-based: Unknown AI support, worth testing
- Stdin-based: Unknown AI support, more complex
- Overview-toggle: Works but causes visible flicker
- Metadata-only: No visuals but no flicker (niri windows JSON)

Future enhancement paths:
- Real-time screen analysis (capture current screen on demand)
- Multi-workspace search (toggle overview, capture, analyze all windows)
- Window-specific capture (use niri window geometry + grim region)
- Clipboard workflow (if AI supports wl-paste)
- Zero-file capture (if AI supports stdin/base64 images)

* Context for Future Work

** Open Questions

Direct capture capabilities:
- Can OpenCode/Claude Code read images from clipboard via `wl-paste`?
- Can OpenCode/Claude Code accept base64-encoded image data as input?
- Can OpenCode/Claude Code read image data from stdin?
- What's actual latency difference: file-based vs clipboard vs temp-file?

niri compositor capabilities:
- Can overview mode be triggered without animations for faster capture?
- Does niri maintain any thumbnail buffers we could access directly?
- Can we hook into niri's IPC to get notified when overview is fully rendered?
- Are there niri config options to reduce overview transition time?

Skill deployment and usage:
- How do users actually trigger skills in practice?
- Is natural language detection reliable ("look at my screenshot")?
- Should skill be invokable via explicit command ("/screenshot-latest")?
- How to handle skill updates (symlink means changes propagate)?

Specification methodology:
- How to formalize "complexity gate" for spec tool?
- What metrics indicate spec-first vs code-first approach?
- Can we detect over-engineering automatically (tasks > expected LOC)?
- Should spec tool warn when solution already exists (grep codebase)?

** Next Steps

Immediate (pending user decision):
1. Deploy skill to `~/.claude/skills/screenshot-latest` or `~/.config/opencode/skills/screenshot-latest`
2. Test with actual AI usage: "look at my last screenshot"
3. Gather user feedback on whether it solves the problem
4. Decide if direct capture enhancements are needed

Future enhancements (only if requested):
1. Test clipboard-based workflow: `grim - | wl-copy` → AI reads
2. Implement overview-toggle capture for multi-workspace analysis
3. Add custom directory support if users request it
4. Add Nth screenshot lookup if users request it
5. Investigate zero-file direct injection if latency becomes issue

Process improvements:
1. Add complexity gate to spec-kit tool usage documentation
2. Create decision framework flowchart (when to spec vs when to code)
3. Document this as case study in WORKFLOW.md
4. Consider adding "test-first" step to specification workflow

** Related Work
- Skills repository: `/home/dan/proj/skills`
- Worklog skill: `~/.claude/skills/worklog/` (used to generate this document)
- Spec-kit framework: `.specify/` directory
- Screenshot specification (archived): `specs/001-screenshot-analysis/spec.md`
- Screenshot implementation: `skills/screenshot-latest/`
- OpenCode documentation: https://opencode.ai/docs (for future AI capability research)
- Wayland screencopy protocol: https://gitlab.freedesktop.org/wayland/wayland-protocols (for understanding capture limitations)
- niri compositor: https://github.com/YaLTeR/niri (for overview mode and IPC capabilities)

* Raw Notes

User interaction highlights:
- Session started with reviewing previous session's over-engineering summary
- User immediately caught new over-engineering: "you're overengineering our overengineering fix"
- Pivoted to focus on direct capture possibilities instead of analysis documents
- User interested in capturing windows from inactive workspaces ("what about for what's not on the active workspace/windows")
- Key question: "Is the problem 'Not rendered' or 'Not viewable because of security'"
- Exploring Alt-Tab style live previews of workspaces/windows
- Pivoted again when overview capture showed 450ms flicker: "preferred scenario would be making it invisible to the user"
- User requested worklog at end of session

Research discoveries this session:
- grim can output to stdout (verified with `file -`)
- base64 encoding works for grim output
- wl-copy/wl-paste work on the system
- niri has overview mode (Mod+O keybinding)
- Overview mode DOES render inactive workspace windows
- Overview capture works but causes ~450ms visible flicker
- AI Read tool successfully ingests PNG files directly
- niri provides JSON metadata for all windows (IDs, titles, workspaces)

Key insight: Wayland limitation is rendering, not security
- Compositors only render visible content by design (performance)
- Alt-Tab previews on Windows work because DWM maintains thumbnail buffers
- GNOME/KDE do maintain thumbnails for workspace switchers
- niri doesn't maintain thumbnails BUT overview mode IS a render pass
- This means capture IS possible via brief overview toggle
- Tradeoff: visual content requires making it visible (Wayland by design)

Alternatives explored:
1. Fast flicker (~450ms overview toggle) - works, visible to user
2. Metadata only (niri JSON) - invisible, no visual content
3. Individual window capture - requires workspace switching, still visible
4. Invisible capture - not possible without compositor thumbnail buffers

Decision point reached: User wants invisible capture, which conflicts with Wayland's render-to-capture model. Options are:
- Accept brief flicker for visual capture
- Use metadata-only for invisible queries
- Request/implement thumbnail buffer support in niri (major undertaking)

Session ended with request for worklog before deciding on approach.

Metrics and scale:
- Specification documents: 635 lines (spec.md + plan.md + tasks.md)
- Implementation: 185 lines total (22 lines code + 83 lines SKILL.md + 80 lines README + examples)
- Analysis documents created: 5 files, ~2000+ lines documenting the learning
- Time spent: Session 1-4 (spec) ~115 min, Session 5-6 (implement + research) ~90 min
- Ratio: 3.4x more spec than implementation, 5.2x more time on spec than coding
- Potential tasks avoided: 82 tasks from original breakdown

File tree created:
```
skills/screenshot-latest/
├── SKILL.md (83 lines - agent instructions)
├── README.md (user documentation)
├── scripts/
│   └── find-latest.sh (22 lines - the actual solution)
└── examples/
    └── example-output.txt

specs/001-screenshot-analysis/
├── spec.md (165 lines - archived as over-engineered)
├── plan.md (139 lines - archived as premature)
├── tasks.md (331 lines - archived as unnecessary)
├── RESET.md (analysis of over-engineering)
├── COMPARISON.md (spec vs implementation comparison)
├── RESOLUTION.md (feature closure)
└── FUTURE-ENHANCEMENT.md (direct capture research)
```

* Session Metrics
- Commits made: 1 (initial commit)
- Files touched (uncommitted): 9 new files
- Lines added: ~4500+ (implementation + analysis + worklog)
- Lines of actual code: 22 (find-latest.sh)
- Lines of documentation: ~4000+
- Tests added: 0 (manual testing only)
- Tests passing: 1/1 (manual test of find-latest.sh successful)