Run Diagnostic

If no URL was provided in the arguments ($ARGUMENTS), ask the user for the site URL before proceeding.

Once you have the URL, execute the full diagnostic pipeline below and write the final report to a markdown file in the current working directory. Name the file diagnostic-{domain}.md (e.g. diagnostic-example-com.md, replacing dots with hyphens). Confirm the file path to the user when done.

Your reports have two purposes:

1. Give the brand a clear, honest picture of their site's health and its effect on business outcomes — revenue, organic traffic, conversion, brand perception.
2. Quantify the scale of execution required to close the gaps, making it evident that this volume of work demands automation and continuous delivery.

Every finding connects to a business outcome. Every claim traces to a data source. When data is missing, say so.

FRAMING: Always frame as opportunity, never failure.

• Banned → alternative: "zero X" / "0 X found" → "X not detected" "completely absent" → "not included" "critical failure" → "structural opportunity" "massive" / "devastating" → "significant" / "material" / "measurable"
• When something is genuinely bad, the data speaks for itself. An 11MB homepage needs no adjectives.

CONCISION:

• Each finding stated ONCE in its own section. Not restated elsewhere.
• "What This Requires" and "Strategic Context" reference TOTALS only — never re-list individual findings by name.
• Depth proportional to impact: HIGH (structured data, meta, reviews, weight/TTFB): 2-3 paragraphs. MEDIUM (editorial sitemaps, cache, cross-sell): 1-2 paragraphs. LOW (HSTS, robots, encoding): 1 paragraph, grouped.

1. discover(url) → DiscoveryResult + samples. crawl: { totalPages, pageCounts: { pdp, plp, blog, institutional, other }, sampleUrls: { pdp, plp, blog, institutional }, allUrls } sitemap: { exists, productSitemapUrls, totalProductUrls } robots: { exists, rules, sitemapUrls } homepage: { status, headers, seoMeta, links, platform, cdn } editorial: { paths: [{ path, exists, linkCount }] } samples: { homepage, pdps[3], plps[2], editorial[0-1] } Call first. All other tools depend on its output.

2. analyze_perf({ samples }) → { hars: [{ url, ttfbMs, totalRequests, totalKB, resourceBreakdown, failedRequests, thirdPartyInventory, cacheHits, cacheMisses }], lighthouses: [{ url, scores, webVitals: { lcp, cls, tbt, fcp, si, tti }, diagnostics }], screenshots: [{ url, imageUrl, device, blocked }] }

3. analyze_seo({ url, samples }) → { audit: { score, brokenLinks, duplicateMeta, missingMetadata, structuredDataCoverage, issues: [{ type, count, severity }] }, pageMeta: [{ url, title, description, h1, canonical, robots, jsonLd, ogTags }], sitemapHealth: { productCount, indexable, orphanedEstimate }, domainSignals: { ssl, sitemap, robotsTxt, http2, cms } } Takes 1-3 min. Runs synchronously.

4. analyze_content({ samples, discovery }) → { pdpScrapes: [{ url, hasReviews, hasCrossSell, hasJsonLd, jsonLdTypes, descriptionLength, imageCount, imageAlts }], editorialScrapes: [{ url, wordCount, publishDate, hasAuthor, hasSeoMeta }], screenshots: [{ url, imageUrl, device }] }

5. research({ url, discovery }) → { traffic: { globalRank, countryRank, totalVisits, bounceRate, pagesPerVisit, trafficSources, topCountries, topKeywords, monthlyVisits, aiTraffic } | null, business: { summary, marketPosition, competitors, recentNews } | null, serp: [{ keyword, results, relatedSearches, peopleAlsoAsk }], keywords: [{ keyword, volume, difficulty, cpc, competition, monthlyTrends }] }

Caching: per-domain, 24h for analyze_*, 7d for research. Repeat calls on the same URL are free.

STEP 1 — Discovery (~10-30s): Call discover(url). Read the output and establish: - SITE INVENTORY: crawl.totalPages, crawl.pageCounts (pdp, plp, blog, institutional). These are your denominators for all report math. - SITEMAP PRODUCT COUNT: sitemap.totalProductUrls (fact, not extrapolated). If sitemap.exists is false, use crawl.pageCounts.pdp and state source. - EDITORIAL: editorial.paths where exists=true AND linkCount>0. Paths that return 200 but have linkCount=0 are likely catch-all routes, not active editorial sections — do not treat them as "existing" content. - PLATFORM / CDN: homepage.platform, homepage.cdn. Status line: "Discovered {N} pages ({pdps} products, {plps} PLPs, {blog} editorial). Starting deep analysis..."

STEP 2 — Analysis (parallel, ~1-3 min): In ONE message, call all four: - analyze_perf({ samples: discovery.samples }) - analyze_seo({ url, samples: discovery.samples }) - analyze_content({ samples: discovery.samples, discovery }) - research({ url, discovery }) Pass the samples and discovery objects through unmodified. If any tool errors (API key, upstream failure): skip the dependent sections, continue with the rest. Do not abort.

STEP 3 — Report: Synthesize all outputs into the report per . Run through before finalizing. Write the report to the markdown file.

Good: "Add Product JSON-LD to product pages | 2,358 PDPs" Bad: "1 template change → 2,358 pages"

COUNTING:

• Each distinct action = 1 opportunity. Scope column = pages affected.
• {N} = distinct actions.
• {total_page_improvements} = sum of all Pages Affected.
• {unique_urls} = deduplicated page count across all actions. Present all three transparently. Ongoing work (content production, review collection) noted separately, excluded from {N}.

Date: {YYYY-MM-DD} URL: {domain} Platform: {homepage.platform} Monthly visits: ~{traffic.totalVisits} Category: {detected or traffic} Ranking global: #{traffic.globalRank} | Ranking Brasil: #{traffic.countryRank}

Health Score: {score}/100 — Structured Data {X}/20 | Content Engine {X}/15 | Product SEO {X}/15 | Performance {X}/20 | Social Proof {X}/10 | Cross-sell {X}/10 | Domain Signals {X}/10

Site inventory: {Measured counts with sources.}[^inventory] [^inventory]: {Methodology.}

{total_page_improvements} improvement opportunities identified on {domain}

We identified {N} areas of improvement representing {total_page_improvements} page-level improvements across {unique_urls} unique URLs. {2-3 sentences on the most impactful findings — stated once, not repeated.}

Opportunities

Each section: finding with inline source references → scope table → business implication.

Number sequentially (### 1., ### 2., ...). Group minor fixes in "Technical hygiene".

Screenshots inline where they belong:

• Homepage screenshot → after headline/summary.
• PLP screenshot → after the last PLP finding.
• PDP screenshot → after the last PDP opportunity. Use . Omit silently if missing.

Opportunity summary

Then one paragraph (no finding names): "What each improvement requires depends on the platform and team. The volume — {total_page_improvements} individual improvements across {unique_urls} URLs — and the ongoing nature of the work make automated execution essential."

What this requires

6-8 sentences, three short paragraphs:

1. Scale — improvements touch thousands of pages; catalog is not static; new products inherit the same gaps. TOTALS only.
2. Nature of the work — some fixes one-time; content and monitoring continuous, granular, time-sensitive.
3. deco AI Agents — specialized agents that execute continuously. What takes weeks elsewhere, deco delivers in minutes, on autopilot. Close with: "Run your digital strategy on autopilot." (or equivalent in report language)

Do NOT name specific findings. Do NOT claim agencies are slow. Do NOT quote prices.

Strategic context

3-4 focused paragraphs of EXTERNAL context (market, competition, timing). Do NOT rehash technical findings. Footnote all research.business claims. Caveat SERP positions with source/location/date.

References and methodology

Industry benchmarks cited: {list with sources} Data sources: {tool, scope, date for each} Source URLs: {footnoted references}

Report generated by the deco AI diagnostic pipeline.

PROVENANCE: every number traces to a named step tool's output field. Good: "2,358 PDPs measured from discover().sitemap.totalProductUrls" Bad: "~3,000 products estimated"

ABSENCE vs NON-DETECTION: blackbox — always say "not detected" / "not found in our analysis". Never "zero" / "none whatsoever" / "does not exist".

SITEMAPS ≠ REALITY: content can exist without being in a sitemap. Never conclude content doesn't exist from sitemap data alone.

SERP POSITIONS: include source (DataForSEO), location, date on every claim.

SAMPLING: state the sample size. "Of 3 PDPs sampled, none contained reviews." Never extrapolate a 3-sample finding to the whole catalog.

OBSERVATION vs INFERENCE: Observation: "Homepage weighs 11.2 MB" (measured) Inference: "likely an undeferred video embed" (use "likely", "suggests")

RESEARCH.BUSINESS: AI-synthesized — hedge ("approximately", "segundo pesquisa de mercado") and footnote citation URLs.

RESEARCH.TRAFFIC (Similarweb panel):

• "approximately" / "estimated" — never exact figures.
• Always footnote the data source and its limitations.
• Traffic sources are percentages summing to ~100%. Present as percentages.
• aiTraffic shares may be null → show "—", not "zero".
• Keyword CPC may be null → "—", not "free" / "zero".
• Competitor comparisons: both numbers must come from research.traffic (same snapshot). Never mix sources.
• If research.traffic is null: omit the Traffic Intelligence section entirely.

CATALOG SIZE: sitemap.totalProductUrls = fact. crawl.pageCounts.pdp = stated with crawl limit. Never extrapolate. Every catalog reference uses the same number from the same source.

BENCHMARKS — safe list (use freely):

• "Every 0.1s mobile speed improvement → +8.4% conversion (retail), +10.1% (travel)" (Deloitte, "Milliseconds Make Millions", 2020)
• "Product recommendations drive 10-30% of e-commerce revenue" (McKinsey)
• "Rich snippets increase CTR by 20-40%" (SEJ / Ahrefs)
• "Products with 50+ reviews convert at 2-3x vs. zero" (Bazaarvoice / Spiegel)
• "Unique PDP descriptions increase organic traffic 30-50%" (Ahrefs)
• "Post-purchase review emails: 5-15% response rate" (industry avg)
• "AOV uplift with cross-sell: 8-15%" (Baymard)
• "Companies with active blogs generate ~55% more visitors" (HubSpot) Any benchmark NOT on this list must be attributed to a verifiable source. If unsure: use a range or remove.

COMPETITORS: only name those present in research.business.competitors. Traffic comparisons require both numbers from research.traffic.

MISSING DATA: omit the section. 5 solid sections beat 8 with 3 padded.

1. STRUCTURED DATA (0-20) — from analyze_seo.audit.structuredDataCoverage + analyze_content.pdpScrapes[].hasJsonLd/jsonLdTypes. 0: no JSON-LD | 5: <25% or partial | 10: 25-75% with Product 15: >75% with Product + BreadcrumbList | 20: full coverage

2. CONTENT ENGINE (0-15) — from discover.editorial.paths + analyze_content.editorialScrapes. "Exists" requires: linkCount>0 on at least one editorial path, OR editorialScrapes showing multiple distinct articles with publishDates. Paths returning 200 with linkCount=0 and no post structure = NOT existing. 0: no editorial (or paths respond but no identifiable posts/links) | 3: editorial section with identifiable articles, but not in sitemaps | 5: in sitemaps, <10 posts | 10: 10-50 posts some SEO | 15: 50+ posts active, SEO-optimized

3. PRODUCT SEO (0-15) — from analyze_seo.pageMeta for PDPs + audit.duplicateMeta. 0: all generic | 5: <25% unique | 8: 25-50% | 12: 50-90% | 15: >90% unique

4. PERFORMANCE (0-20) — from analyze_perf.hars + lighthouses. = TTFB+Weight (0-10) + Caching (0-10) TTFB+Weight: 0 if >3s or >10MB | 3 if 2-3s or 5-10MB | 6 if 1-2s & 3-5MB 8 if 600ms-1s & 1.5-3MB | 10 if <600ms & <1.5MB Caching: 0 if no-cache | 3 homepage only | 6 most pages, low TTL 10 proper headers all types

5. SOCIAL PROOF (0-10) — from analyze_content.pdpScrapes[].hasReviews. 0: no reviews | 3: <5 avg | 6: 5-50 avg | 10: 50+ on most

6. CROSS-SELL (0-10) — from analyze_content.pdpScrapes[].hasCrossSell. 0: none | 3: API detected not rendered | 5: some PDPs | 10: all sampled

7. DOMAIN SIGNALS (0-10) — from analyze_seo.domainSignals. SSL +2 | sitemap +2 | robotsTxt +2 | canonicals correct +2 | no conflicting meta +2

1. LANGUAGE: .br → pt-BR. Else → English.
2. NO EMOJIS anywhere.
3. STRUCTURE follows exactly. No invented sections.
4. HEALTH SCORE max values: /20, /15, /15, /20, /10, /10, /10. Recalculated from rubric using step tool outputs.
5. DATA PROVENANCE: every number names the step tool + field it came from.
6. SAMPLING: every sampled finding states sample size. No extrapolation.
7. BENCHMARKS: safe list or verifiably attributed.
8. TONE: "Not detected" not "zero". Opportunity framing.
9. NO REPETITION: findings in their section only. Totals in summaries.
10. DEPTH: high-impact 2-3¶, low-impact 1¶ grouped.
11. SITE-SPECIFICITY: every path, example, URL comes from THIS site's step tool outputs. No content carried over from other sites.
12. TRAFFIC DATA: header monthly visits, category, rankings from research.traffic. If null, fall back to research.business and state source.
13. SERP: source + location + date on every position claim.
14. RESEARCH.BUSINESS claims hedged and footnoted.