Lazyweb Design Create

Lazyweb Deep Design Research

Evidence-backed design research that reads the user's current screen, names its frictions, forms 2-4 genuinely divergent redesign bets, and renders a visual-first HTML report where the recommended prototype sits side by side with the control.

People learn by seeing. Every claim in the report is carried by a large, legible visual; nothing important hides behind a click. Chrome stays quiet: no chip clutter, no legend tables, no explanatory paragraphs next to the proof.

MCP plan responses — check before the normal workflow

Inspect every data-bearing tool result before applying its normal search, render, or report schema:

• MCP_PRO_REQUIRED: relay the server message and returned intent-bound upgrade_url to the user, then stop. Do not retry another data tool or fall back to web/manual output.
• FREE_REPORT_DAILY_LIMIT: relay the server message and returned intent-bound upgrade_url to the user, then stop. Do not retry another data tool or fall back to web/manual output.
• Successful status: "locked_preview": relay display_to_user verbatim (or the returned MCP text if that is all the client exposes), including the preview and upgrade links. It is terminal and contains no generated research; do not poll, render or assemble a report, retry another data tool, or fall back.

CRITICAL: Output Behavior

This skill produces FILES, not a plan. Regardless of whether you are in plan mode or not, ALWAYS:

1. Author the report content as .lazyweb/deep-design-research/{topic}-{date}/work/report-data.json (structured content, NOT HTML)
2. Embed Lazyweb references directly with their returned imageUrl/image_url; save only current-state and web-captured screenshots under .lazyweb/deep-design-research/{topic}-{date}/references/
3. Do NOT create report.md, report.html, or any other report artifact by hand — the server renders the report
4. Do NOT write research content into a plan file
5. Render and host the report with lazyweb_render_report (see "Render and host the report" below) — this single call IS the deliverable; producing the report and hosting it are the same action, so there is nothing to skip
6. After the render call returns, show the user a concise summary, the recommended bet, and the shareable link (the report lives only at that URL)
7. Ask the user if the research looks good
8. If in plan mode, exit plan mode after the user confirms - the research is done
9. Suggest next steps: "You can now use this research to inform your implementation, ask /lazyweb to improve your current design, or start building."

The visible report is: Agent Instructions, Goal, Recommendation, and optional Inspo — in that order. Do not produce the older busy structure with key examples, findings, sources, broad recommendation lists, or long prose analysis sections.

The Recommendation is built like lazyweb-design's hypothesis engine, with screenshot evidence taking the role experiment evidence plays there: read the control, name its specific frictions, form 2-4 falsifiable and structurally divergent bets (Safe bet / Bold bet / Wild card — a thinking discipline, not visible chips), prototype each as a generated image, and carry the decision. When a current page or screenshot exists, render Control and the recommended prototype side by side in equal, height-locked frames with a ◀ ▶ variant switcher on the right frame so the user can flip through the other bets in place; runner-up bets also appear in a snap carousel of same-size cards. Prefer generated bitmap prototype images over hand-coded HTML mockups when image generation is available; use HTML/CSS only as a fallback or when the user asks for implementation-ready code. Generate prototype images in parallel at medium effort by default, or low effort when the user asks for speed/exploration.

Render and host the report (the single deliverable)

The report is rendered and hosted server-side. You author the report content as work/report-data.json, then call lazyweb_render_report ONCE. That call fills the canonical template on the server, validates it, hosts it at https://www.lazyweb.com/report/lazyweb/{id}/, and returns the shareable link. There is no local report.html to write, no separate publish step, and no token to read — producing the report and hosting it are the same action, so a finished report is always a shared report.

Call it once work/report-data.json and every references/ image exist. The report dir is $REPORT_DIR = .lazyweb/deep-design-research/{topic-slug}-{YYYY-MM-DD}.

Arguments:

• report_data: the parsed work/report-data.json object (see "Author report-data.json" below).
• assets: every file in $REPORT_DIR/references/ as { "name": <filename>, "b64": <base64 of the bytes> } — the control screenshot and each generated prototype image the report points at via references/{name}. Lazyweb references embedded by absolute imageUrl are NOT assets; only locally saved files. (Note: migrating these render assets off inline base64 to the presigned upload flow is Phase 2 — out of scope here; keep assets:[{b64}] as-is.)
• report_skill: "deep-design-research".
• idempotency_key: the report dir slug, e.g. deep-design-research/{topic-slug}-{YYYY-MM-DD}. Send the SAME value on every call for this report so a retry returns the same link instead of a duplicate.
• version: the value you read from ~/.lazyweb/VERSION at skill start.

Handle the result:

• { ok: true, url } — the report is live. Show "Shareable link: {url} (unlisted - anyone with the link can view)", then open "{url}" in the user's browser (skip open in a headless/CI/no-GUI environment and just print the link).
• { ok: false, code: "REPORT_RENDER_ERROR", detail } — detail names the missing or invalid report_data field (e.g. missing data.topic, bets must have 2-4 entries). Fix that field in work/report-data.json and call ONCE more.
• { ok: false, code: "REPORT_TOO_LARGE" } — the embedded screenshots are too large. Reduce their number/size and retry once.
• any other { ok: false } — tell the user hosting failed and why (the error field). There is no local copy, so they need the link or the reason.

The server fills a fixed, render-tested template and rejects an incomplete report_data (missing fields → REPORT_RENDER_ERROR), so a partial or skeleton report can never be hosted — that replaces the old client-side contract gate. Never hand-render HTML or fall back to a local file.

Image references in report-data.json

You never write HTML — you only choose image src values in report-data.json:

• Lazyweb references: the absolute imageUrl/image_url URL Lazyweb returns.
• Locally saved screenshots (current-state, web captures, generated prototypes): a relative references/{filename} path, with that file uploaded as an asset in the render call.
• Never use file:// URLs or absolute local paths (/Users/..., C:\...).

When to Use This

• User wants to understand a design space before building
• User needs competitive analysis for a feature
• User asks "what are best practices for X"
• User wants to see how the best apps solve a specific problem

When NOT to Use This

• User just wants to see a few screenshots quickly -> route to lazyweb-quick-search
• User has an existing design and wants to optimize or improve it -> route to lazyweb-design (objective optimize or improve)

Lazyweb MCP Setup

Use the hosted Lazyweb MCP tools at https://www.lazyweb.com/mcp for all Lazyweb database access.

Downloading or updating the skill pack, installing or configuring a client, and creating or reusing a bearer token are available to everyone without charge. Setup, health, and workflow discovery remain usable regardless of plan; real data-bearing MCP tool availability and usage limits depend on the account's persisted experiment assignment and plan.

Required MCP tools:

• lazyweb_search - text search over mobile and desktop screenshots
• lazyweb_find_similar - more results like a returned Lazyweb imageUrl or image payload
• lazyweb_compare_image - visual search from an image_url (the control reaches it via the presigned upload flow; see "Send the control via presigned upload")
• lazyweb_request_image_upload / lazyweb_resolve_image_upload - presigned upload for the control screenshot: request a { upload_url, key }, PUT the bytes, resolve to an image_url (spec: specs/image-upload-architecture.md)
• lazyweb_health - connectivity check
• lazyweb_render_report - render + host the finished report from report_data + reference images, returns the shareable link (the deliverable; see "Render and host the report" above)

Optional MCP tools:

• lazyweb_search_ab_tests - mobile-only supporting experiment evidence for pricing, paywall, checkout, onboarding, and other growth/monetization screens when the live schema exposes it

Pass skill: "lazyweb-design-create" on every Lazyweb call. Include "skill": "lazyweb-design-create" in the arguments of each lazyweb_* tool call - for example {"query": "pricing page", "limit": 30, "skill": "lazyweb-design-create"}. This is optional analytics metadata; never drop or change a real argument for it. (Keep report_skill="deep-design-research" on lazyweb_render_report — that backend/report tag stays legacy; only the analytics skill slug moves to lazyweb-design-create.)

Also pass version: "<x.y.z>" on every call. Read ~/.lazyweb/VERSION once per session at skill start (e.g. cat "$HOME/.lazyweb/VERSION" 2>/dev/null || echo 0.0.0); fall back to "0.0.0" if the file is missing or unreadable — never block on this. Include "version": "<that-value>" in the arguments of every lazyweb_* tool call alongside the existing skill arg — for example {"query": "pricing page", "limit": 30, "skill": "lazyweb-design-create", "version": "0.4.5"}. Optional analytics metadata Lazyweb uses to track which skill-pack versions are running; never drop or change a real argument for it.

Also pass integrity verbatim on every call. Read it once per session at skill start (cat "$HOME/.lazyweb/INTEGRITY" 2>/dev/null). Pass that exact string as the integrity arg on every lazyweb_* tool call — it is an opaque install fingerprint; copy it verbatim and do NOT interpolate, edit, parse, or reconstruct it. If the file is absent, OMIT the integrity field entirely — never invent one. This is the field that tells Lazyweb your pack is current.

These are the current public gateway names. Backend/internal surfaces may also expose canonical tools such as search_screenshots, list_filters, vision_screenshots, and metadata_screenshots; prefer the lazyweb_* names in this skill. Use high_design_bar: true only when the live tool schema exposes it and the user asks for high-design-bar companies, premium examples, best-designed apps, or stronger visual-quality filtering. That filter is backed by companies.high_design_bar = true.

Before searching, verify MCP is available by listing tools and running lazyweb_health.

If a Lazyweb tool is refused as OUT OF DATE — the result has code: "skill_deprecated" / action_required: "upgrade" / do_not_fallback: true, or says your skill pack is below the required version — this is a HARD STOP, not a fallback case. Do NOT proceed in web-research-only or any degraded mode; that output is deprecated and ungrounded. STOP the task, tell the user they must upgrade, and have them run /lazyweb-update (or curl -fsSL https://www.lazyweb.com/install.sh | bash), reload the client, and retry. The design tools are now unified under /lazyweb-design.

If Lazyweb MCP is genuinely not installed or auth fails (a connection/auth error — NOT a version refusal): Tell the user: "Lazyweb MCP is not installed. Run curl -fsSL https://www.lazyweb.com/install.sh | bash, reload this client, then rerun this skill. Installing and receiving a bearer token do not require payment; real data-bearing MCP access and limits depend on your assigned experiment and plan. Keep the token in ignored local config." Then proceed with web research only - the skill still works, just without Lazyweb's database.

Browse Setup (run BEFORE any web capture)

LB=""
# Check the standalone Lazyweb checkout first
for _P in "$(pwd)/.lazyweb/repos/lazyweb-skill/browse/dist/browse" ~/.lazyweb/repos/lazyweb-skill/browse/dist/browse; do
  [ -x "$_P" ] && LB="$_P" && break
done
# Fall back to gstack browse
if [ -z "$LB" ]; then
  _ROOT=$(git rev-parse --show-toplevel 2>/dev/null)
  [ -n "$_ROOT" ] && [ -x "$_ROOT/.claude/skills/gstack/browse/dist/browse" ] && LB="$_ROOT/.claude/skills/gstack/browse/dist/browse"
  [ -z "$LB" ] && [ -x ~/.claude/skills/gstack/browse/dist/browse ] && LB=~/.claude/skills/gstack/browse/dist/browse
fi
[ -x "$LB" ] && echo "BROWSE_READY: $LB" || echo "NO_BROWSE"

Immediately after BROWSE_READY, set a real viewport — the daemon's default window can be arbitrarily small and silently produces unusable captures:

$LB viewport 1440x900

Use $LB screenshot --viewport <path> for viewport-window shots; the default screenshot is full-page.

If NO_BROWSE: Web screenshot capture is unavailable. Lazyweb results still work - just describe web examples in text without screenshots. To enable web captures, run: cd ~/.lazyweb/repos/lazyweb-skill/browse && ./setup

Workflow

0. Ground the search

Before searching, ground the work in what the user is building:

1. Run lazyweb-context-detect (on PATH when installed by setup; otherwise ~/.lazyweb/repos/lazyweb-skill/bin/lazyweb-context-detect). Use its project/platform/stack output to bias the platform filter and captions.
2. Clarify only what cannot be inferred. If platform is unknown, or the product/screen/outcome is unclear, ask the user ONE short clarifying question to pin down product/screen, mobile vs desktop, and the specific outcome.

1. Understand the research question

Pin down:

• The specific screen, flow, or feature
• The product type, audience, and platform
• The design outcome the recommendation should improve

2. Capture current state (if applicable)

If the user is researching a specific page or app they are building, capture the current state:

• Running dev server or URL available: use preview/browse tools to screenshot it
• Mobile app: ask the user to provide a screenshot
• General topic only: skip this step

Define the report directory FIRST (steps 2-7 write into it):

REPORT_DIR="$(pwd)/.lazyweb/deep-design-research/{topic-slug}-{YYYY-MM-DD}"
mkdir -p "$REPORT_DIR/references" "$REPORT_DIR/work"

Save as $REPORT_DIR/references/current-state.png. This image becomes Control in the side-by-side Recommendation comparison. Do not create a separate visible "Current State" section.

3. Read the control (required when a current state exists)

Before any searching or ideation, read the control the way lazyweb-design reads a paywall. Identify:

• Components present: header, hero, value prop, proof, pricing, CTAs, trust signals, navigation, FAQ, footer — whatever the screen type implies
• Layout pattern: single-column stack, hero + grid, comparison layout, dashboard shell, feed, wizard, etc.
• Strategic moves: what the screen is trying to do — anchoring, social proof, demonstration, urgency, curiosity, authority, personalization
• Audience and user state: who lands here and how warm they are
• Named frictions: 2-5 specific, observable weaknesses of THIS screen ("proof arrives below the fold", "CTA copy is generic", "hero asserts value without showing the product"). Every later hypothesis must attack one of these by name.

If there is no current state (greenfield research), substitute a baseline read: the convention set the category expects, and which conventions the user's product can or cannot honor. Hypotheses then attack gaps between that baseline and the strongest references.

4. Identify competitors and adjacent companies

Think about two groups:

• Direct competitors - apps that solve the same problem
• Adjacent companies with great design - apps in related spaces known for excellent UX

5. Search Lazyweb (go deep — the corpus is the product)

Fast path (default): run the evidence script, not agent gatherers. A deterministic fetcher ships next to this skill: fetch-evidence.py (python3 stdlib only). Build the full Pass A + Pass B query plan as JSON first, then run it once — all queries fire in parallel (capped at 6 in-flight, 20s timeouts, one Retry-After-honoring retry on 429/5xx):

cat > "$REPORT_DIR/work/query-plan.json" <<'PLAN'
{"skill":"lazyweb-design-create","version":"<from ~/.lazyweb/VERSION>","queries":[
 {"id":"a1","pass":"A","tool":"lazyweb_search","args":{"query":"<screen/component>","platform":"desktop","limit":15}},
 {"id":"b1","pass":"B","tool":"lazyweb_search","args":{"query":"<underlying function>","platform":"desktop","limit":15}}
]}
PLAN
python3 "{skill-base-dir}/fetch-evidence.py"   --plan "$REPORT_DIR/work/query-plan.json"   --out  "$REPORT_DIR/work/evidence.json" || echo "FETCH_FALLBACK"

On success, work/evidence.json holds merged, same-company-deduped references (imageUrl + visionDescription verbatim) plus a coverage_summary, and work/evidence-summary.json holds a compact no-URL digest. Then:

1. One selection + clustering pass (you, the main agent): READ ONLY evidence-summary.json (indices + truncated descriptions — a fraction of the tokens), select 12-20 references and form the 2-4 clusters, then pull just the selected indices' full records from evidence.json for embedding. You may view at most the top ~10 candidate images before the final pick — never the whole corpus.
2. One bounded top-up round — ALSO through the script, never via raw MCP tool calls (the v3.4 timed run lost 12 minutes to MCP token dumps here). Write a second small plan and run fetch-evidence.py again to work/evidence-topup.json:
   • lazyweb_find_similar on the 2-3 strongest results, passing each reference's imageUrl string as image_url, "limit": 5;
   • lazyweb_compare_image is OMITTED from the fast path (measured: low yield). Only the agent-fallback path may use it, sending the control via the presigned image-upload flow (request -> PUT -> resolve -> image_url; see "Send the control via presigned upload" below, spec: specs/image-upload-architecture.md) — never inline base64. Read ONLY the script's stderr verdict line (TOPUP_SATURATED: / TOPUP: N attachable) and evidence-topup-summary.json — never the raw top-up file (its signed URLs are payload-hostile). Expect description-less near-dupes more often than not: budget at most 2 vision-verifications, and treat an empty yield as saturation confirmation (your corpus was already complete), not failure. When search_ab_tests returns 0 references, its prose learnings are in the queries' analysis fields.
3. Coverage honesty: if coverage_summary shows failed or low_coverage queries — even when the script exits 0 — carry that into the report's .corpus banner when the selected corpus lands under 8 references or a whole pass came back thin.

Agent fallback (REQUIRED to keep working — do not remove): when the script exits non-zero, prints FETCH_FALLBACK, emits invalid JSON, or python3 is missing, gather via the Lazyweb MCP tools yourself instead: run the same Pass A/Pass B plan as batched agent tool calls — three roles (median mapper / edge hunter / web + control) dispatched as parallel subagents when the host has an Agent tool, sequential phases otherwise. Gatherer prompts MUST state: (a) the output directory already exists — use the Write tool only, never Bash/mkdir; (b) copy each returned imageUrl string VERBATIM — a reference without it cannot be embedded; (c) expansion results lacking a visionDescription are kept (top ≤5) as pending_vision entries for the main agent to vision-verify after the merge.

Text before image (hard rule, applies to every gatherer): select and rank references from TEXT — visionDescription, captions, coverage, warnings, similarity scores — before fetching or viewing ANY image. An image may be viewed only after its text fields qualify it for the report (or when vision-verifying an agent-described result). Viewing images first is the single biggest avoidable token-and-time cost in this phase.

Search discipline: never repeat an identical query; results are deterministic. Page deeper with offset and follow the response's pagination.next_offset. Read coverage and warnings on every response. On no_matches/low_coverage, use the closest result, strip the query to its core 2-6 word UI pattern, or note the coverage gap in the report. On company_not_in_library, use a suggested company or drop the filter.

Keep a running search log at $REPORT_DIR/work/search-log.json — append every query with its filters/offset as you run it (gatherers append to their own work/gatherer-{n}.json; the merge step consolidates). This is what makes a crashed run resumable and is the ground truth for "never repeat an identical query".

Run 6-10 searches minimum, split into two mandatory passes:

Pass A — map the median (2-4 searches). The in-category baseline: what everyone in the user's space does. This is what the Safe bet completes and what the Bold bet must NOT resemble.

{"query":"<specific screen/component>","limit":15}
{"query":"<screen type>","company":"<competitor>","limit":15}
{"query":"<screen type>","category":"<category>","limit":15}
{"query":"<different description of same thing>","limit":15}

Pass B — hunt the edges (4-6 searches, REQUIRED — never skip). Deliberately search OUTSIDE the obvious category and BELOW the screen-name level. This pass exists to feed the Bold and Wild-card bets; a corpus that only contains the median can only produce median recommendations.

{"query":"<the underlying FUNCTION, not the screen name — 'data visualization with gamification' not 'dashboard'>","limit":15}
{"query":"<same screen type>","category":"<deliberately unrelated category: Gaming, Entertainment, Music, Editorial...>","limit":15}
{"query":"<the persuasion mechanism itself, e.g. 'live activity feed', 'interactive product demo'>","limit":15}
{"query":"<a second unrelated category doing the same job>","limit":15}

Cross-pollination routing: finance → look at Gaming/Entertainment/Music; productivity → Fitness/Travel/Social; e-commerce → Education/Health; developer tools → Editorial/Games. The more distant the category, the more novel the transferable mechanism. Yield ranking from live runs: function-level and mechanism-level queries find the most usable outliers; screen-type + unrelated-category is the weakest shape (often low coverage) — run it last and drop it first when budget-constrained. While reading Pass B results, collect outliers: references that do something structurally unlike everything in Pass A. Outliers are the raw material of the Bold and Wild-card bets — note for each one the mechanism (what it DOES, not what it looks like), why it works in its home context, and what would have to adapt to transfer.

Then expand with lazyweb_find_similar on the 2-3 strongest results (highest similarity + best visionDescription fit) to pull in their visual neighbors. This is how the corpus gets from "three or four screenshots" to a real reference set.

When a current-state screenshot exists, also run lazyweb_compare_image with it via the presigned image-upload flow (see "Send the control via presigned upload" below; spec: specs/image-upload-architecture.md) and fold the top structural matches into the reference set — visual similarity from the control itself is the strongest grounding move available. Pass the resolved image_url to lazyweb_compare_image; never inline a full-res screenshot as image_base64.

Send the control via presigned upload

The control screenshot is uploaded once and referenced by URL — large base64 sent inline through chat gets corrupted by the LLM. Use the Phase 0 MCP upload tools (spec: specs/image-upload-architecture.md):

1. Determine the mime_type of $REPORT_DIR/references/current-state.png (image/png, image/jpeg, or image/webp).
2. lazyweb_request_image_upload({ mime_type }) -> { upload_url, key } (authed by the MCP session; no ~/.lazyweb token needed).
3. PUT the bytes with NO credentials: curl -fsS -X PUT -H "content-type: <mime>" --data-binary @"$REPORT_DIR/references/current-state.png" "<upload_url>"
4. lazyweb_resolve_image_upload({ key }) -> { image_url }.
5. Call lazyweb_compare_image({ image_url, skill, version }) with that image_url and fold the top structural matches into the reference set.

lazyweb_compare_image and lazyweb_find_similar results often come back without a visionDescription and sometimes with null/near-duplicate metadata. Handle them explicitly:

• A result with no visionDescription is usable ONLY if you view the image yourself (vision) and write the caption from what you actually see — tag it "agent-described". Never attach it unviewed.
• Skip entries with null siteId/pageUrl AND no description.
• Dedupe same-company near-duplicates: keep at most one screen per company per cluster unless the duplicates demonstrate different patterns.

Keep limit at 15 (10-20 band): larger results overflow many hosts' tool-result cap, forcing a dump-to-file + re-read round trip that costs more time than a second page. Page with offset when you genuinely need more. The control reaches lazyweb_compare_image through the presigned upload flow above (request -> PUT -> resolve -> image_url), so there is no inline base64 to crop or downscale — upload the full-resolution current-state.png and let the server work from the hosted asset.

Platform routing:

• SaaS, web, desktop app, admin surface, or marketing page -> use platform: "desktop"
• iPhone/Android app -> use platform: "mobile"
• General research or cross-platform -> omit platform and judge returned images

Assess quality:

• matchCount 2/3 or 3/3 = strong
• matchCount 1/3 = weak
• similarity > 0.4 = good

Selection target: 12-20 references for a normal run (floor: 8 before the report can claim a healthy corpus; if fewer survive screening, add a .corpus thin-evidence banner and say so). Relevance is the only bar — more relevant references is strictly better; padding with loose matches is worse than fewer.

Rules for attaching references to the report:

1. Read visionDescription before using ANY screenshot.
2. The screenshot MUST directly illustrate the point it supports.
3. If visionDescription does not match your suggestion, do not use it.
4. Never guess what is in a screenshot. If there is no visionDescription, skip it (or vision-verify it yourself per the rule above).
5. Use visionDescription to write accurate captions and alt text.

Mismatched references destroy user trust faster than anything else.

6. Search connected inspiration libraries

Check if ~/.lazyweb/libraries.json exists and has connected libraries:

cat ~/.lazyweb/libraries.json 2>/dev/null

If libraries are configured, search each one using the browse tool. For each library:

1. Navigate to the library search URL: $LB goto "{searchUrl}"
2. Snapshot the page: $LB snapshot -i
3. Search for the research query: $LB fill @eN "{query}"
4. Submit and wait: $LB press Enter then $LB snapshot -i
5. Screenshot only the most relevant results: $LB screenshot "$REPORT_DIR/references/{library}-{company}-{screen}.png"
6. Label all library-sourced references in the report with [Mobbin], [Savee], etc.

If a library session has expired, tell the user and skip it. Do not block the run.

7. Web research and live screenshot capture

Lazyweb gives curated screenshots. Web captures give the latest competitor state. Do both unless MCP is unavailable and the user wants a web-only fallback.

Find URLs via WebSearch — cover both the median and the edges:

• Search for "[topic] UX best practices [current year]"
• Search for "[competitor name] [screen type]"
• Search for "best [screen type] examples"
• Search for "unconventional [screen type] design" and "creative [screen type] examples [current year]" — Awwwards / FWA / CSS Design Awards winners and experimental sites are often the strongest Bold/Wild-card seeds, because nobody in the user's category is looking at them

Collect 3-8 URLs. For the most useful ones, capture viewport screenshots into work/ first; move (or trim) a capture into references/ only once the report actually embeds it:

if [ -x "$LB" ]; then
  $LB goto "https://example.com/pricing"
  $LB screenshot "$REPORT_DIR/work/example-pricing-page.png"
fi

If browse capture is unavailable, include web evidence only when you can describe it accurately from a reliable source. Do not invent a screenshot.

Inspect every capture before using it. If a capture is defective (cookie/email modal covering the page, blank below the fold, half-loaded), dismiss the modal via browse and recapture, or trim a copy to the loaded region. Never present a broken capture as evidence; keep originals in $REPORT_DIR/work/, not references/.

8. Experiment evidence (growth/monetization screens only)

For landing pages, pricing, paywalls, checkout, onboarding, referral, and other growth/monetization screens, call lazyweb_search_ab_tests when available to validate or challenge a bet you already formed from reading the control. Treat learnings as directional unless the tool returns measured lift. If the tool is unavailable or returns no on-context experiments, say so in the relevant card ("design-prevalence signal") — never imply measured lift.

Run it THROUGH fetch-evidence.py (add it as an entry in the top-up plan — the script speaks generic tools/call) so the response lands in a file instead of a tool-result dump; even capped calls (include_images: false, analysis_experiment_limit: 8) have returned 98KB, past most hosts' caps.

Context traps with this tool:

• Discard off-context experiments (wrong platform or screen type, e.g. mobile paywall tests for a web landing page) instead of citing them.
• The tool's own confidence field grades corpus retrieval, not evidence strength — your evidence wording comes from the honesty taxonomy, never from that field.
• Use category as the industry filter. Do not pass the user's product name as a company filter; treat product as target context only, and check the response warnings for silently-applied filters before trusting a zero-result answer.

9. Cluster the corpus and prepare references

$REPORT_DIR was created in step 2 (create it now with the same mkdir if step 2 was skipped).

Group the selected references into 2-4 named clusters of similar approaches ("Proof-wall heroes", "Product-demo-first", "Editorial minimal", "Data-dense operator"). Clusters drive both the Inspo map (cluster labels over neighboring points) and the bets (each bet should draw mainly on one cluster). A cluster needs 2+ members; singletons are outliers — usable as a Wild-card seed or a pattern, but not a cluster.

Do not download Lazyweb database images. Use the returned imageUrl/image_url directly in HTML. Supabase storage-backed image URLs are signed for 365 days and intended for report embedding. If a selected Lazyweb result has no returned image URL, omit the image and rely on visionDescription plus text.

For web-captured examples, save descriptive filenames such as stripe-pricing-page.png or linear-onboarding-step1.png.

Keep references/ clean: the render call uploads every file in references/ as an asset. Only files actually referenced by report-data.json belong there; working files (full-page originals, base64 payloads, untrimmed captures, search logs) live in $REPORT_DIR/work/, which is never uploaded.

Hypothesis Engine (the core of the Recommendation)

The unit of analysis is a falsifiable bet, not a component list and not a theme. This mirrors lazyweb-design, with the screenshot corpus playing the role of the experiment corpus — and it indexes on creativity: the value of this report over a competent designer's first instinct is the bets a median competitor would never generate. A set of three reasonable suggestions is a failed run, even if every section renders perfectly.

A good hypothesis takes this form:

Making [specific change] should [specific outcome] because [specific mechanism].

Good: "Replacing the testimonial-quote hero with a numbers-first proof wall (subscriber count, named outcomes, logos) should lift email signups because this audience buys evidence of results, not promises." Bad: "Improve the hero." / "Make it more premium."

Grounding (required)

Every hypothesis must be anchored to a named friction from the control read (step 3) — not to a reference you happened to like. References and prevalence support a hypothesis; they never originate it. Anti-hybrid checksum: before writing each bet, confirm it answers "what would you change about THIS screen, and why" — not "what does reference X look like". If a bet reads as a description of someone else's screenshot, rewrite it.

Creativity engine (mandatory ideation pass — run BEFORE choosing bets)

LLMs and corpora both regress to the mode: left alone, every "option" becomes a tasteful rearrangement of the category median. This pass exists to fight that. Do it in working notes, before committing to bets:

1. Name the dominant convention set from Pass A — the 3-5 moves everyone in this category makes ("testimonial hero", "3-column feature grid", "logos + CTA"). This is the median you must beat, not the menu you pick from.
2. Overgenerate: draft 8-12 candidate moves, forcing coverage of these operators (at least one candidate per operator):
   • Inversion — do the opposite of a dominant convention (everyone claims value in copy → remove the copy and show only the product; everyone gates content → give the best content away on the landing page).
   • Format transplant — rebuild the page as a different artifact: a live feed, an interactive demo, a terminal, a letter from the founder, a quiz, a gallery, a receipt, a game. The page stops being "a landing page that describes X" and becomes "X itself".
   • Cross-category mechanism transfer — take an outlier from Pass B, extract what it DOES (not what it looks like), and apply it here.
   • Extremify — find the category's most timid version of a promising idea and push it to its logical extreme (one testimonial → a wall of 400; one stat → the entire hero is the live number).
   • Constraint flip — delete a "required" element entirely (no hero, no nav, no pricing table, no sign-up form) and design what fills the void.
3. Score each candidate on novelty-in-category (would any Pass A reference do this?) × mechanism fit (is there a real reason it converts HERE?). Discard weird-for-weird's-sake (high novelty, no mechanism) and median-with-makeup (mechanism, no novelty).
4. The Bold and Wild-card slots MUST be filled from the surviving high-novelty candidates. If none survive, the corpus is the problem — go back to Pass B and the unconventional web search, don't ship three reasonable bets.

Bet archetypes (forced divergence — a thinking tool, not visible chips)

Produce 2-4 bets and assign each exactly one archetype. The archetypes exist to force divergence during ideation; they are NOT rendered as chips in the report — the option card's one-to-two-sentence description carries the idea in plain words.

• Safe bet — completes the highest-prevalence conventions the control is missing or mis-using. Low risk, evidence-rich, ships fastest. Cite the prevalence count ("7 of 14 references do X; control does not"). This is the ONLY bet allowed to sound reasonable on first read.
• Bold bet — breaks or inverts a dominant category convention, or restructures the page around a model no direct competitor uses. NOT "the strongest cluster's strategy" — that is the median of the best, and it belongs in the Safe bet. Prevalence ceiling: if more than ~20% of the in-category corpus already does it, it is not bold — relabel it Safe and ideate again. Apply the ceiling to the bet's actual structural move at the granularity the bet specifies (e.g. "renders a FULL issue as the page", not the broader "shows content previews"), and state that slice explicitly in the evidence line so the count is checkable. Evidence for a Bold bet is mechanism proof (outlier or cross-category references showing the mechanism working), plus the in-category absence stated as the opportunity ("0 of 14 in-category references do this — whitespace, not risk-free").
• Wild card — a full cross-category or format transplant from the creativity engine: the kind of move that makes the reader pause. Cite the off-category source honestly in the description ("single source, outside this category") and name the risk. Grounded novelty means the MECHANISM has proof somewhere real — it does not mean the move is common anywhere.

A normal run ships one Safe bet, one Bold bet, and a Wild card (optionally a second Bold with a different mechanism). Never ship two bets with the same persuasion mechanism, regardless of archetype.

Anti-collapse rules (the reason options used to look the same)

• Each bet must differ from every other bet on at least two of: page strategy, persuasion mechanism, information architecture, trust source, and primary component set.
• Reject bets that only vary palette, typography, density, theme, or tone.
• Reject a bet that recommends a convention the control already uses unless it changes how that convention is used — verify against the step-3 control read.
• The reasonableness test: read the three bets cold. If every one of them sounds obviously sensible — if nothing makes you pause — the set has collapsed to the median. Regenerate the Bold and Wild slots from the creativity engine. A good set has exactly one bet that reads "of course", one that reads "that's a real swing", and one that reads "wait — really?" (and survives the mechanism question). The test grades the MOVE, not the proof: strong evidence never makes a wild move "too reasonable" — attach maximum proof to the wildest bets, never under-evidence one to keep it sounding daring.
• The planning-meeting test (Bold/Wild): would the move survive a median competitor's planning meeting without anyone calling it risky? If yes, it is not bold. "Add social proof", "clarify the value prop", "restructure the hero" sail through unchallenged — they fail. "Delete the signup form above the fold" gets someone saying "wait, is that safe?" — it passes.
• Pre-imagegen self-check: if all prompts could plausibly produce the same hero/form/card layout with different colors, rewrite them before generating.
• Each bet should draw mainly on a different slice of the corpus (Safe ← Pass A median, Bold ← outliers/edges, Wild ← cross-category). If two bets cite the same three references, they are probably one bet.

Each bet must carry (in the working notes)

1. Its archetype (Safe / Bold / Wild card)
2. The hypothesis sentence ("Making X should Y because Z")
3. The named control friction it attacks
4. Evidence, matched to the archetype: a Safe bet cites prevalence ("7 of 14 do X"); a Bold/Wild bet cites

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