Agent-First Screenshots
SkillProductivityYour AI can drive a real app in a browser and capture clean, defect-free product screenshots for landing pages, newsletters, social posts, decks, and PR materials. It checks every capture against the page and the final image, then retakes shots until they come out right. Use it for any task where screenshots of the app need to be taken or redone.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill, then ask your AI to take or redo screenshots of your app and tell it which pages or formats you need.
Then ask your AI: use the Agent-First Screenshots skill
What your AI can do with it
- Take clean screenshots of landing pages, newsletters, social posts, decks, and PR materials
- Operate a real app in a browser to set up each shot before capturing
- Check every capture for visual defects before delivering it
- Retake shots automatically until they are defect-free
- Redo existing screenshots when something changes
What this skill tells your AI
The instructions your AI receives, as published by devin-axis/ipollowork in .opencode/skills/agent-first-screenshots/SKILL.md and read by ahel’s review.
An agent-first screenshot is produced by an agent driving the real app via CDP, gated by structural + pixel + vision verification before it ships. It turns screenshots from hand-crafted artifacts into a regenerable pipeline.
Use this for "take/redo screenshots / make marketing images" tasks. It is NOT
e2e evidence — for pass/fail proof use the fraimz skill.
The Zero-Defect Bar
A shippable screenshot has ZERO obvious visual defects — the bar is not "the content is present", it is: would a human glancing at it immediately spot something broken? Overlapping or clipped text, misaligned/collapsed layout, garbled or mid-transition content, blank regions, stray modals/tooltips — any of these disqualifies the frame. Full stop.
The One Method
Operate the app like a power user preparing a demo, not like a developer hacking the DOM. The app already looks great: get it into a real, settled state through the UI and capture it cleanly. Never rebuild or fake it.
Golden Rules
- Only remove, never add. Hiding a leaf distraction (notification badge,
"Sign in" footer link, status text) via
display:noneis safe. Never inject fake HTML, override flex/height/width on structural containers, add fixed-position overlays, or mutate the DOM tree — the layout engine will collapse (scroll areas shrink to 0, content disappears). - If a feature isn't available, don't fake it. Enable it through the settings UI like a real user, or skip the shot and say so.
- Each shot starts from a clean reload.
location.reload(), wait for full render, navigate through the UI, minimal leaf cleanup, verify, capture. Never carry CSS hacks across shots. - Get state naturally. Click the real tabs/buttons/pickers; close panels via their close buttons (not CSS); run real multi-turn tasks so content is impressive — no toy data, no mid-stream captures.
- Match the target aspect ratio via CDP metrics override (e.g. 1440x900
at
deviceScaleFactor: 2), then wait ~1s and re-verify — the override can trigger re-layout.
Verify with three channels, in a loop
innerText existing does not mean visible; a healthy DOM rect does not mean it
rendered. Every frame must pass all three before it ships:
-
DOM pre-check (cheap, before capture): scroll area height > 100px (not collapsed), hero text inside the viewport rect, no unexpected modal/overlay. If it fails: reload and redo — never fix with more CSS.
-
Pixel post-check (truth, after capture): decode the PNG and sample the DOM-derived hero rects. Calibration: for text-on-white UI, background ratio is a bad signal (85–92% background is normal). Variance is the reliable signal: content-filled region variance > ~200 (often 1000+); blank/flat region < 50. Whole-image
bgRatio > 0.97→ blank frame. -
Vision check (mandatory gate): deterministic stats CANNOT catch the defects that matter most — overlap, clipping, misalignment, double-rendering. Hand the PNG to a vision-capable model with a zero-defect rubric returning JSON:
{ any_obvious_defect: bool, // the gate — if true, REJECT overlapping_text_or_elements: bool, clipped_or_cutoff: bool, misaligned_or_broken_layout: bool, blank_or_empty_regions: bool, stray_modal_tooltip_or_panel: bool, legible: bool, polish_score: 1-5, defects: ["..."] }If
any_obvious_defectis true the frame is rejected regardless of layers 1–2. Diagnose, fix the root cause (settle the state, close the picker, reload), recapture.
Reusable scripts in this skill's directory: screenshot-verify.mjs (verify an
existing PNG) and capture-verify.mjs (capture at 2x + verify regions + save
only on pass); both use sharp.
Common failure modes
| Symptom | Fix |
|---|---|
| Overlapping/clipped text (layers 1–2 pass) | Unsettled/edit-mode/wrong-width state — settle, use preview mode, gate on vision |
| Blank screenshot / 32px scroll area | Structural element was CSS-hidden — reload, close panels via UI |
| Overlay (picker/modal) on every shot | It was opened and never closed — Escape before capturing |
| In DOM but not in pixels | Clipped/blank render — trust variance + vision, not innerText |
Anti-patterns
Injecting fake components; overriding flex properties; fixed-position
overlays; carrying state across shots; verifying via innerText only;
proof-frame mindset (the goal is "I want that", not "it didn't crash").
Signals
- GitHub stars
- 6k
- Forks
- 1k
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
agent-first-screenshots- Source
- github.com/devin-axis/ipollowork