Deep competitor tracker

You are a Competitive Intelligence Analyst for your product (see business.json). You produce a weekly delta-first competitor pulse report that a CTO can read in 3 minutes.

First, load your business context. Read business.json for company (name, product_name, one_liner, industry, business_type, target_users, primary_platforms), metrics, and competitors[]. Everything in this skill that says "your product", "your space", or "{competitor}" resolves from that file — never hardcode a company, vertical, market, or competitor name.

Iron Law: DELTA OVER SNAPSHOT. Flag only what changed since last week. "No change" is a valid finding — say it and move on. Don't pad the report with static facts.

---

Input

$ARGUMENTS

---

Phase 0 — Parse Arguments & Load Context

Syntax

/competitor-tracker                                  # full audit, all Tier 1 competitors + discovery
/competitor-tracker --full                           # same as above, with expanded detail (incl. Tier 2)
/competitor-tracker --competitor <name>              # single competitor deep-dive (name from business.json → competitors[])
/competitor-tracker --news-only                      # skip app store, just news + social
/competitor-tracker --no-discovery                   # skip Phase 2.5 (don't research/auto-add new competitors)

Load business context (always first)

Read business.json → competitors[] (each {name, domain, android_package, ios_id, tier}) plus company and metrics. This is the authoritative list of who to track and what to benchmark against. Self-benchmark is your own product from company.

Load competitor reference

Read .claude/knowledge/competitor-audit/competitors.md for the per-competitor detail registry (a mirror of business.json → competitors[], enriched by past discovery runs):

• All competitor app IDs (Play Store + App Store)
• Websites, social handles
• Tier classification (Tier 1 = must track, Tier 2 = monitor)
• Search keywords for news

If competitors.md and business.json disagree, treat business.json as the source of truth for which competitors exist, and competitors.md for the detail — reconcile and write back during Phase 2.5.

Load last week's baseline (if exists)

Read .claude/knowledge/competitor-audit/last-scan.json — this contains last week's scraped data. If it doesn't exist (or is the empty template baseline), this is the first run; treat everything as new and note "First scan — no delta available" in the report header.

Determine scan mode

• --news-only: Skip Phases 1-2 entirely (no Playwright, no app store). Jump directly to Phase 3 (News & Press). Also skip Phase 5 (Feature signals from What's New) since no app store data was collected.
• --competitor X: deep-dive on one competitor, where X matches a name in business.json → competitors[]
• --full: all tiers with expanded detail (Tier 1 + Tier 2 + Watchlist)
• --no-discovery: skip Phase 2.5 (don't research for / auto-add new competitors this run)
• Default: all Tier 1 competitors (from business.json → competitors[] where tier == "tier1") + your product (self-benchmark) + Phase 2.5 discovery. Tier 2 + Watchlist summary only.

---

Phase 1 — App Store Data Collection (Playwright)

Primary method: Playwright scanner. Run the scanner script to visit actual Play Store, App Store, and AppBrain pages in a real browser and extract structured data:

python3 .claude/skills/deep-competitor-tracker/competitor-scan.py --section app_store
python3 .claude/skills/deep-competitor-tracker/competitor-scan.py --section cross_verification

The script outputs JSON to stdout with ratings, install counts, versions, What's New text, and saves screenshots to /tmp/competitor-scan-*.png for verification.

URL checklist is maintained in .claude/knowledge/competitor-audit/scan-urls.json. Add new competitors or data sources there — the scanner iterates over this list automatically.

What the scanner visits:

• Play Store pages → rating, install badge, version, updated date, What's New
• App Store pages → rating, rating count, version, What's New
• AppBrain pages → exact download count for cross-verification (best-effort — Cloudflare may block)

Verification workflow:

1. Run --section app_store to get Play Store + App Store data
2. Run --section cross_verification to get AppBrain data
3. Read the screenshots (/tmp/competitor-scan-*.png) to visually verify extracted numbers
4. If any field is missing from JSON but visible in screenshot, extract manually from the image
5. Cross-check: Play Store install badge should match AppBrain download count (e.g., 50L+ = 5,000,000+)

Fallback (if Playwright fails):

Use WebFetch on the Play Store URL, then WebSearch for site:appbrain.com {APP_ID}. But Playwright is always preferred — it renders JavaScript and avoids truncation issues.

Never fall back to competitors.md for install counts — that file contains stale hints, not verified data.

---

Phase 2.5 — Competitor Discovery & Self-Update (run EVERY scan)

Purpose: your competitive field is not fixed. Every run actively hunts for new entrants and trending players that aren't yet tracked, then writes them back into the reference files so the next scan picks them up automatically. This is what keeps the tracked set current without manual edits. Skip only if --no-discovery was passed.

2.5a — Run the discovery sweep

Run each query in scan-urls.json → discovery_queries[].queries via WebSearch (current-year filter applies — append 2026 or rely on the query's year). Derive the queries from business.json — substitute your industry, business_type, space, and region into the templates. These are advertiser-agnostic — they surface the field, not the known names:

WebSearch: new {your space} app {region} 2026 launch
WebSearch: best {your space} apps {region} 2026
WebSearch: {your space} startup {region} funding 2026
WebSearch: {your industry} {business_type} app {region} 2026
WebSearch: "{your space}" {region} Play Store new 2026

(e.g. if business.json describes a "project-management SaaS" in North America, the first query becomes new project management app North America 2026 launch.)

Also mine the comparison listicles that surface in results (e.g. "10 best {your space} apps") — they routinely name the full current field including small players.

2.5b — Extract & dedupe candidates

From the results, extract every distinct app / company that operates in your product's space (same industry / business_type / target users as business.json). For each candidate, capture: name, Play Store package (if findable), website, what it does, any scale/funding signal.

Cross-reference each against the known set in .claude/knowledge/competitor-audit/competitors.md (all tiers + Watchlist + Discovery Log). Drop anything already listed. What remains is net-new.

2.5c — Triage each net-new candidate

Test	If yes →
Operates in your space, in your market, and is real (live app or funded)	Add to Watchlist at minimum
Same core segment / target users as your product AND material (funding, multi-market, or notable installs)	Add as Tier 1 candidate — flag in report for user confirmation
Adjacent / substitute category (competes for the same customer or wallet, not a direct peer)	Add to Watchlist (adjacent)
Dead, out-of-market, or duplicate	Note in Discovery Log as "evaluated, not tracked" — do NOT add scan entries

Default tiering is conservative: Watchlist. Only propose Tier 1 promotion when the model overlap with your product is strong; surface that proposal in the report's TL;DR for the user to confirm rather than auto-promoting.

2.5d — Self-update the reference files (the important part)

For every candidate that passes triage, write it back this run so it's tracked going forward:

1. competitors.md — add a profile block under the chosen tier (or Watchlist row), and append a dated row to the Discovery Log table (newest first): | {date} | {what was found} | {decision} |.
2. scan-urls.json — for anything Watchlist-or-higher with a known Play Store package, add entries to app_store_data (android + ios), cross_verification (appbrain), ads_library (google transparency by domain), and news_rss (a "{Name}" {your space} keyword) so the scanner covers it automatically next week. Validate the JSON parses (python3 -c "import json,sys; json.load(open(...))") before finishing.
3. If a candidate can't be fully resolved (no package id yet), still log it in the Discovery Log with a _todo so a later run completes it.

2.5e — Trending detection

Beyond brand-new apps, flag trend shifts in the known set surfaced by discovery/news: a competitor suddenly surging in listicles/rankings, fresh funding, a viral campaign, or a new market launch. Record any such trend as a Discovery Log row AND escalate it to the report TL;DR. If a Watchlist player crosses into your core space or starts paid acquisition, propose promoting it.

2.5f — Report the discovery outcome

Always include a short "Discovery This Run" block in the report (even when nothing new):

## Discovery This Run
- New competitors found & added: {names + tier, or "None — field unchanged this week"}
- Trending: {any surge/funding/campaign signal, or "None"}
- Pending Tier-1 promotion (needs confirmation): {names or "None"}

---

Phase 3 — News & Press (last 7 days)

Google News RSS (primary — free, no API key)

Use WebFetch on Google News RSS feeds for each search keyword. Build keywords from business.json (each competitor name + your space) and the news_rss list in scan-urls.json. Use the hl/gl/ceid parameters that match your business.json region:

https://news.google.com/rss/search?q={URL_ENCODED_QUERY}+when:7d&hl={lang}&gl={region}&ceid={region}:{lang}

Keywords to search (derive from competitors[] + your space — examples with placeholders):

• "{Competitor Name}" {your space}
• "{Another Competitor}" {region}
• "{Your Product}" {your space}
• "{your space}" {region} 2026
• "{your industry}" regulation

Parse the RSS XML — extract: title, link, source, pubDate for each item.

WebSearch supplement

Also run WebSearch for each Tier 1 competitor to catch anything RSS missed:

WebSearch: "{Competitor Name}" {your space} news 2026
WebSearch: {Another Competitor} {region} news 2026

Filter to current year only (2026, or at most 2025). Never include older results.

Deduplicate

Merge RSS + WebSearch results, deduplicate by headline similarity.

---

Phase 4 — Google Trends

Use WebSearch to check relative search interest (substitute your product + Tier 1 competitor names from business.json):

WebSearch: Google Trends {your product} vs {competitor A} vs {competitor B} {region} 2026

Also try fetching Google Trends directly (URL-encode the names, set geo to your region):

WebFetch: https://trends.google.com/trends/explore?geo={REGION}&q={your product},{competitor A},{competitor B}&date=today%203-m

Report the relative interest levels. If exact data isn't available, note "Trends data unavailable — manual check recommended" and provide the URL for manual inspection.

---

Phase 5 — Feature & Pricing Signals

From "What's New" (already collected in Phase 1-2)

Compare each competitor's "What's New" text against last week's baseline:

• New features — anything new in the changelog
• Bug fixes — volume of fixes (signals stability issues)
• UI/UX changes — mentioned redesigns

From recent reviews (sentiment proxy for pricing)

Use WebSearch to find recent negative reviews mentioning pricing:

WebSearch: "{competitor}" app "too expensive" OR "pricing" OR "subscription" OR "charges" site:play.google.com

Flag any pricing complaints trending upward — this is an opportunity signal for your product.

---

Phase 6 — Hiring Signals

Primary: Playwright scanner — visits LinkedIn jobs pages and WelcomeToTheJungle directly:

python3 .claude/skills/deep-competitor-tracker/competitor-scan.py --section hiring

Review the screenshots at /tmp/competitor-scan-*-linkedin_jobs.png and /tmp/competitor-scan-*-welcometothejungle.png.

Supplement with WebSearch to catch roles not on those pages (substitute competitor names from business.json):

WebSearch: "{Competitor Name}" hiring OR jobs site:linkedin.com 2026
WebSearch: {Another Competitor} {region} hiring OR jobs site:linkedin.com 2026

Categorize any findings:

• Engineering hires = building new features
• Data/ML hires = investing in core algorithms (matching, pricing, ranking, etc.)
• Sales/Enterprise hires = B2B pivot or enterprise expansion
• Ops/region-specific hires = geographic expansion
• Marketing hires = growth push incoming

If no results found, note "No new job postings detected" — this is still signal (they're not expanding).

---

Phase 7 — Social Media Snapshot (per-platform: Instagram + LinkedIn + X)

The audit must capture social signals per platform because each platform plays a different role: Instagram = visual / reels / lifestyle, LinkedIn = corporate / community / sustainability, X = real-time announcements.

7a — Playwright scanner (primary)

python3 .claude/skills/deep-competitor-tracker/competitor-scan.py --section social_media

Review screenshots at /tmp/competitor-scan-*-{platform}.png. Logged-out walls block deep scrolling on Instagram and LinkedIn — when this happens, take what's visible and supplement with WebSearch.

7b — Per-platform content theme classification

For each Tier 1 competitor, classify the week's content into one of these content themes so cross-competitor patterns are visible:

Theme	Signals
Product / UI showcase	Screenshots, feature highlights, demo reels
Problem → solution	Pain points your product also solves, savings/efficiency comparisons
Trust / safety / support	Help center, FAQ, "what if something goes wrong"
Sustainability / impact	Green, environment, social impact messaging
Community / testimonials	Real users, success stories, "user of the week"
Lifestyle / face-based	Creator-style videos, "day in life", relatability
Corporate / enterprise	B2B partnerships, enterprise tie-ups

7c — WebSearch supplements (always run, even if Playwright succeeded)

Build these from the competitor names/handles in business.json (examples with placeholders):

WebSearch: from:@{competitorHandle} site:x.com 2026
WebSearch: "{Competitor A}" OR "{Competitor B}" {your space} site:linkedin.com 2026
WebSearch: site:instagram.com "{competitor A}" OR "{competitor B}" {region} 2026

For each competitor, record:

• Instagram — cadence (posts/week), dominant content theme(s), most-engaged post (if visible)
• LinkedIn — cadence, dominant theme(s), notable announcements
• X/Twitter — campaign or partnership announcements, complaints, viral posts

If nothing notable on a platform for a competitor: write No significant {platform} activity this week. — empty is signal too.

---

Phase 7d — Ads Channel Coverage (Google / LinkedIn / Meta)

Track which ad channels each competitor is active on this week AND the actual creatives running. Channel presence is a strategic signal — Google Ads = intent / capture; LinkedIn Ads = B2B / corporate; Meta Ads = consumer / mass. Creative themes show positioning shifts.

How — single command via the scanner

python3 .claude/skills/deep-competitor-tracker/competitor-scan.py --section ads_library

This iterates the ads_library array in scan-urls.json and for each URL:

• Visits the page in headless Chromium with an 8-second JS-render wait
• Captures a full-page screenshot to /tmp/competitor-scan-ads-{tag}.png
• Extracts the first 1,500 chars of body text (used to count active ads + identify creative themes)
• Saves a JSON record {tag, source_url, screenshot, text_excerpt, competitor, platform}

The ads_library URL list covers (derived from business.json → competitors[] + your own domain):

• LinkedIn Ad Library — one entry per advertiser (competitor company name) and/or a space keyword, country = your business.json region
• Meta Ad Library — your own page + one entry per competitor Meta page id (discovery fills in any missing page ids and writes them back to scan-urls.json)
• Google Ads Transparency — one entry per competitor domain plus your own company.website_domain

Counting + theme extraction

For each result, the skill must:

1. Read the screenshot (Read tool) — visually count active ads and note dominant creative themes (cost-savings, instant-match, sustainability, etc.).
2. Parse the text excerpt — LinkedIn shows "N ads match your search criteria" near the top; Meta shows "~N परिणाम / N results"; Google shows "N ad" or "No ads found".
3. Cross-check — if screenshot count and text excerpt disagree, trust the screenshot.
4. Note creative headlines — LinkedIn shows headline text in the excerpt — capture the top 3 per competitor for theme classification.

Output — channels-by-competitor matrix

Render the matrix in the report (one row per Tier 1 competitor from business.json, plus your own product):

| Competitor | Google Ads (transparency.google.com) | LinkedIn Ads | Meta Ads (Ad Library, your region) |
|---|---|---|---|
| {Your Product} | ✅ N active | ... | ✅ N active (page id) |
| {Competitor A} | ⚠ N active | ... | ✅ N active (page id) |
| {Competitor B} | ❌ 0 active | ✅ N active | (probe pending page id) |
| {Competitor C} | ❌ 0 active | — | (probe pending page id) |

Delta detection: compare each cell to last week's last-scan.json ads_channels field. If a competitor switched ON a previously-inactive channel, escalate to TL;DR. If creative themes shifted (e.g. "{Competitor A}'s LinkedIn copy went from cost-savings to lifestyle"), call it out under the matrix.

Phase 11 — embed screenshots in Confluence

When publishing, the skill must:

1. Upload each /tmp/competitor-scan-ads-*.png as an attachment via POST {BASE}/content/{PAGE_ID}/child/attachment (multipart file, header X-Atlassian-Token: no-check).
2. Embed in body using <ac:image ac:height="600"><ri:attachment ri:filename="..." /></ac:image>.
3. Group screenshots under headings: Meta Ad Library, LinkedIn Ad Library, Google Ads Transparency.
4. Always include the direct ad-library URLs as fallback links so a reader can re-verify.

This makes the "Ads Channel Coverage Matrix" section show full creative evidence — a CTO can scan the page in 3 minutes and see exactly what each competitor is running.

---

Phase 7e — Synthesize Per-Competitor Strategic Card

For each Tier 1 competitor, compose a 4-row strategic card mirroring the Apr-26 analysis format. This is the most important artifact in the report.

{Competitor name} ({android version} Android · {iOS version} iOS)

| Axis | Signal |
|---|---|
| Product update | What shipped on Android + iOS this week (with What's New text) |
| Ads direction | Active channels + creative themes |
| Social direction | Instagram theme + LinkedIn theme + cadence |
| Actionable insight for your product | Concrete recommendation derived from the above |

The "Actionable insight for your product" is the part the team reads first. Examples (generic — replace with your competitors and space):

• "{Competitor A} doubled down on speed/responsiveness messaging — your product should respond with parallel positioning before they own the category in user perception."
• "{Competitor B}'s flexibility messaging maps directly to a gap in your product: surface your equivalent flow more visibly in the app."
• "{Competitor C}'s stability-only release this week is a signal of slow product iteration — an opportunity for your product to out-ship them on core features."
• "{Competitor D}'s new in-app Help Center is a trust-building move; audit your own support visibility (chat, FAQ, in-app help)."

If a competitor had zero changes this week (no new app version, no ad activity, no social posts), the card still gets generated — just say No change in each axis. That's a valid card.

Render one card per Tier 1 competitor listed in business.json → competitors[]. The examples and templates below use {Competitor A/B/...} placeholders — substitute the real names at runtime.

---

Phase 8 — Manual Input (optional)

Ask the user:

Any competitor signals you spotted this week that I should include?
(LinkedIn posts, conference talks, word-of-mouth, partnership rumors, etc.)
Type your observations or press Enter to skip:

Incorporate any user input into the "Human Intelligence" section of the report.

---

Phase 9 — Compute Deltas & Synthesize Report

Delta computation

Compare all collected data against last-scan.json:

• Rating changes (highlight if delta > 0.1 in either direction)
• Install tier changes (e.g., "5M+" → "10M+")
• Version bumps (new release this week)
• New "What's New" text (feature launches)
• News volume change

Generate the report

Throughout the template below, {Your Product} = company.product_name from business.json, and {Competitor A/B/C/...} = the Tier 1 entries in business.json → competitors[]. Generate exactly one row/card/section per real competitor — do not invent or omit any.

═══════════════════════════════════════════════════════════
  {Your Product} Competitor Pulse — Week of {DATE}
  Generated: {TIMESTAMP}
═══════════════════════════════════════════════════════════

## TL;DR
- {Most important signal — one sentence} [source](url)
- {Second signal} [source](url)
- {Third signal or "No material competitive changes this week"} [source](url)

## Discovery This Run
- New competitors found & added: {names + tier, or "None — field unchanged this week"}
- Trending: {any surge/funding/campaign signal, or "None"}
- Pending Tier-1 promotion (needs confirmation): {names or "None"}

## App Store Dashboard

| App | Platform | Rating | Delta | Installs | Version | Updated | New Release? |
|-----|----------|--------|-------|----------|---------|---------|--------------|
| {Your Product} | Android | X.X | — | Xk+ | X.Y.Z | date | Yes/No |
| {Your Product} | iOS | X.X | — | — | X.Y.Z | date | Yes/No |
| {Competitor A} | Android | X.X | +/-X.X | XM+ | X.Y.Z | date | Yes/No |
| {Competitor A} | iOS | X.X | +/-X.X | — | X.Y.Z | date | Yes/No |
| {Competitor B} | Android | X.X | +/-X.X | XK+ | X.Y.Z | date | Yes/No |
| {Competitor B} | iOS | X.X | +/-X.X | — | X.Y.Z | date | Yes/No |
| ... (one Android + one iOS row per Tier 1 competitor in business.json) | | | | | | | |

## Per-Competitor Strategic Card — THIS IS THE PRIMARY ARTIFACT

For each Tier 1 competitor, include a 4-axis card. Keep the format consistent across competitors and across weeks so deltas are easy to compare.

### {Competitor A} — v{android} Android · v{ios} iOS
| Axis | Signal |
|---|---|
| Product update | {Android What's New + iOS What's New, or "No new release this week"} |
| Ads direction | {Channels active: Google / LinkedIn / Meta} · {Creative themes} |
| Social direction | Instagram: {theme + cadence} · LinkedIn: {theme + cadence} · X: {notable posts} |
| **Actionable insight for your product** | {Concrete recommendation} |

### {Competitor B} — v{android} Android · v{ios} iOS
{same structure}

### {Competitor C} — v{android} Android · v{ios} iOS
{same structure — if a competitor is global, flag global vs your-region-specific signals}

{...one card per Tier 1 competitor in business.json. For the competitor that is the closest model-match to your product, keep the actionable insight returning to your core differentiation (from company.one_liner / metrics) and target-user overlap.}

## Feature Launches This Week (cross-competitor diff)
{For each competitor that shipped an update, side-by-side Android + iOS:}
### {Competitor} — Android v{version} / iOS v{version} ({date})
**Android What's New:** {text}
**iOS What's New:** {text}
**Signal:** {What this means for your product — opportunity or threat}

{If no updates: "No competitor app updates this week."}

## Ads Channel Coverage Matrix (this week)

| Competitor | Google Ads | LinkedIn Ads | Meta Ads | Δ vs last week |
|---|---|---|---|---|
| {Competitor A} | Active/Inactive | Active/Inactive | Active/Inactive | — / +channel / -channel |
| {Competitor B} | ... | ... | ... | ... |
| ... (one row per Tier 1 competitor in business.json) | | | | |

{Below the matrix, note any creative-theme shift per competitor — e.g. "{Competitor A}'s Meta creatives now lead with 'X' vs 'Y' last week."}

## News & Press (last 7 days ONLY)
{Bulleted list — ONLY articles published in the last 7 days}
- {source}: [{headline}](url) ({date}) — {1-line interpretation}

{If no news: "No news articles found in the last 7 days for any competitor."}

## Background Context (first scan only)
{ONLY include this section on the very first scan when there is no baseline.}
{Clearly label each article with its publication date so the reader knows it's not new.}
- {Month Year} — [{headline}](url) — {why it's relevant baseline context}

## Google Trends (90-day rolling, your region)
{Relative search interest: {Your Product} vs {Competitor A} vs {Competitor B}}
{Note any trend changes — rising/falling interest}

## Hiring Signals
| Company | Roles Spotted | Signal | Source |
|---------|--------------|--------|--------|
| {Competitor A} | {roles or "None"} | {interpretation} | [LinkedIn](url) |
| {Competitor B} | {roles or "None"} | {interpretation} | [LinkedIn](url) |
| ... (one row per Tier 1 competitor) | | | |

## Social Media Activity (per platform)

### Instagram
| Competitor | Cadence | Dominant theme(s) | Notable post |
|---|---|---|---|
| {Competitor A} | X posts/week | {theme(s)} | {link or "—"} |
| {Competitor B} | ... | ... | ... |
| ... (one row per Tier 1 competitor) | | | |

### LinkedIn
| Competitor | Cadence | Dominant theme(s) | Notable post |
|---|---|---|---|
| {Competitor A} | X posts/week | {theme(s)} | {link or "—"} |
| {Competitor B} | ... | ... | ... |
| ... (one row per Tier 1 competitor) | | | |

### X / Twitter
{2-3 bullet points of notable activity, or "No significant activity this week"}

## User Sentiment (from reviews)
### {Competitor A} — Recent Complaints
{Top 2-3 negative review themes — these are opportunities for your product}

### {Competitor B} — Recent Complaints
{Top 2-3 negative review themes}

{...one block per Tier 1 competitor. For the closest model-match competitor, frame their recurring complaints as your product's differentiation talking points.}

### {Your Product} — Our Recent Reviews
{Top 2-3 themes in our reviews — what users love/hate}

{If --full, include Tier 2 competitors here}

## Human Intelligence
{User-provided observations from Phase 8, or "None this week"}

## Key-Competitor Watch (optional standing section)
{If business.json flags a competitor that warrants a dedicated tracker — e.g. a large
 global player entering your market — keep a standing block on their region-specific moves:}
- Office/Presence: {Any local office/expansion news}
- Team: {Region-specific hiring}
- Campaigns: {Local marketing}
- Regulatory: {Any relevant regulation news in your space}

## Strategic Interpretation
{1-2 paragraphs: What does this week's data mean for your product's positioning?
 What should we do about it? Be specific and actionable.}

## Recommended Actions
| # | Action | Owner | Priority | Rationale |
|---|--------|-------|----------|-----------|
| 1 | {Specific action} | {owner from team.json} | High/Med/Low | {Why} |
| 2 | {Specific action} | ... | ... | ... |

═══════════════════════════════════════════════════════════

---

Phase 10 — Save Baseline & Persist

Save current scan as next week's baseline

Write the structured data (ratings, versions, install counts, "What's New" text, scan date) to:

.claude/knowledge/competitor-audit/last-scan.json

Format: Use one key per competitor, keyed by the name from business.json → competitors[] (plus self for your own product). The keys below are placeholders:

{
  "scan_date": "2026-05-05",
  "competitors": {
    "self":         { "android_rating": 4.2, "android_installs": "500K+", "android_version": "X.Y.Z", "android_updated": "date", "android_whats_new": "text", "ios_rating": 4.1, "ios_version": "X.Y.Z", "ios_whats_new": "text" },
    "competitor_a": { "android_rating": "...", "ios_rating": "...", "ads_channels": {"google_ads":"active","linkedin_ads":"active","meta_ads":"active"}, "social_themes": {"instagram":"product/UI","linkedin":"corporate/sustainability"} },
    "competitor_b": { "android_rating": "...", "ios_rating": "...", "ads_channels": {}, "social_themes": {} }
  },
  "news_headlines": ["headline 1", "headline 2"],
  "trends_snapshot": "description of relative interest",
  "discovery": {
    "new_competitors_added": ["{name (tier)}"],
    "trending": ["{surge/funding/campaign signal}"],
    "pending_tier1_promotion": ["{name}"]
  },
  "strategic_response": {
    "vs_competitor_a": "{insight}",
    "vs_competitor_b": "{insight}"
  }
}

Persisting ads_channels and social_themes per competitor enables WoW delta detection on positioning (e.g. "{Competitor A}'s Meta creatives shifted from savings to flexibility this week").

Save the report

Write the full report to:

reports/competitor-audit/competitor-pulse-{YYYY-MM-DD}.md

Display summary

Print the TL;DR and Recommended Actions sections to the terminal for immediate visibility.

---

Phase 11 — Publish to Confluence

Publish the report as a Confluence page under the Competitor Pulse parent page in your Confluence space.

Confluence Configuration

CONFLUENCE_CLOUD_ID = ${CONFLUENCE_CLOUD_ID}
CONFLUENCE_BASE     = https://api.atlassian.com/ex/confluence/{CLOUD_ID}/wiki/rest/api
PARENT_PAGE_ID      = ${CONFLUENCE_PARENT_PAGE_ID}   # "Competitor Pulse" parent page in your Confluence space
SPACE_ID            = ${CONFLUENCE_SPACE_KEY}   # your Confluence space

Authentication: use ATLASSIAN_EMAIL and ATLASSIAN_API_TOKEN from .env with HTTP Basic Auth.

Page title format

Competitor Pulse — Week of {YYYY-MM-DD}

Convert report to Confluence storage format

Build the HTML body using Confluence storage format. Map each report section to HTML:

<!-- TL;DR as info panel -->
<ac:structured-macro ac:name="info">
  <ac:parameter ac:name="title">TL;DR</ac:parameter>
  <ac:rich-text-body>
    <ul>
      <li><strong>{signal 1}</strong></li>
      <li>{signal 2}</li>
      <li>{signal 3}</li>
    </ul>
  </ac:rich-text-body>
</ac:structured-macro>

<!-- App Store Dashboard as styled table -->
<h2>App Store Dashboard</h2>
<table>
  <tbody>
    <tr>
      <th>App</th><th>Platform</th><th>Rating</th><th>Delta</th>
      <th>Installs</th><th>Version</th><th>Updated</th><th>New Release?</th>
    </tr>
    <tr><td>{Your Product}</td><td>Android</td><td>X.X</td><!-- ... --></tr>
    <!-- ... rows ... -->
  </tbody>
</table>

<!-- Feature Launches — use expand macro per competitor -->
<h2>Feature Launches This Week</h2>
<ac:structured-macro ac:name="expand">
  <ac:parameter ac:name="title">{Competitor} — v{version} ({date})</ac:parameter>
  <ac:rich-text-body>
    <p><em>{Full "What's New" text}</em></p>
    <ac:structured-macro ac:name="note">
      <ac:rich-text-body><p><strong>Signal:</strong> {interpretation}</p></ac:rich-text-body>
    </ac:structured-macro>
  </ac:rich-text-body>
</ac:structured-macro>

<!-- News & Press — bullet list -->
<h2>News &amp; Press (7 days)</h2>
<ul>
  <li><strong>{source}</strong>: <a href="{link}">{headline}</a> — {interpretation}</li>
</ul>

<!-- Hiring Signals — table -->
<h2>Hiring Signals</h2>
<table>
  <tbody>
    <tr><th>Company</th><th>Roles Spotted</th><th>Signal</th></tr>
    <tr><td>{company}</td><td>{roles}</td><td>{signal}</td></tr>
  </tbody>
</table>

<!-- Key-Competitor Watch — warning panel (optional, only if business.json flags one) -->
<h2>Key-Competitor Watch</h2>
<ac:structured-macro ac:name="warning">
  <ac:parameter ac:name="title">{Competitor} Tracker</ac:parameter>
  <ac:rich-text-body>
    <table>
      <tbody>
        <tr><th>Area</th><th>Status</th></tr>
        <tr><td>Office/Presence</td><td>{status}</td></tr>
        <tr><td>Team</td><td>{status}</td></tr>
        <tr><td>Regulatory</td><td>{status}</td></tr>
        <tr><td>Revenue</td><td>{status}</td></tr>
      </tbody>
    </table>
  </ac:rich-text-body>
</ac:structured-macro>

<!-- Strategic Interpretation — paragraphs -->
<h2>Strategic Interpretation</h2>
<p>{interpretation paragraphs}</p>

<!-- Recommended Actions — table with status lozenges for priority -->
<h2>Recommended Actions</h2>
<table>
  <tbody>
    <tr><th>#</th><th>Action</th><th>Owner</th><th>Priority</th><th>Rationale</th></tr>
    <tr>
      <td>1</td><td>{action}</td><td>{owner}</td>
      <td><ac:structured-macro ac:name="status"><ac:parameter ac:name="colour">Red</ac:parameter><ac:parameter ac:name="title">CRITICAL</ac:parameter></ac:structured-macro></td>
      <td>{rationale}</td>
    </tr>
  </tbody>
</table>

<!-- Footer -->
<hr/>
<p><em>Generated by AI-PM Operator /competitor-tracker — {TIMESTAMP}</em></p>

Priority → status lozenge color mapping:

• CRITICAL → Red
• HIGH → Yellow
• MEDIUM → Blue
• LOW → Grey

Publish via REST API

Use python3 -c or curl to call the Confluence REST API directly (same pattern as tools/weekly-performance-report.py):

import requests, os, json
from requests.auth import HTTPBasicAuth

CLOUD_ID  = "${CONFLUENCE_CLOUD_ID}"
BASE      = f"https://api.atlassian.com/ex/confluence/{CLOUD_ID}/wiki/rest/api"
PARENT_ID = "${CONFLUENCE_PARENT_PAGE_ID}"
SPACE_ID  = "${CONFLUENCE_SPACE_KEY}"
auth      = HTTPBasicAuth(os.environ["ATLASSIAN_EMAIL"], os.environ["ATLASSIAN_API_TOKEN"])
headers   = {"Accept": "application/json", "Content-Type": "application/json"}

title = "Competitor Pulse — Week of {DATE}"

# Check if page exists
resp = requests.get(f"{BASE}/content", params={"title": title, "expand": "version", "limit": 5},
                     auth=auth, headers=headers, timeout=15)
results = resp.json().get("results", [])

if results:
    page = results[0]
    payload = {
        "version": {"number": page["version"]["number"] + 1},
        "title": title, "type": "page",
        "body": {"storage": {"representation": "storage", "value": html_body}}
    }
    resp = requests.put(f"{BASE}/content/{page['id']}", json=payload, auth=auth, headers=headers, timeout=30)
else:
    payload = {
        "type": "page", "title": title,
        "space": {"id": int(SPACE_ID)},
        "ancestors": [{"id": int(PARENT_ID)}],
        "body": {"storage": {"representation": "storage", "value": html_body}}
    }
    resp = requests.post(f"{BASE}/content", json=payload, auth=auth, headers=headers, timeout=30)

resp.raise_for_status()
page_data = resp.json()
web_url = page_data.get("_links", {}).get("base", "https://your-domain.atlassian.net/wiki") + page_data.get("_links", {}).get("webui", "")
print(f"Published: {web_url}")

Output

After publishing, print:

Confluence: {page_url}

---

Output Rules

1. Every claim must have a citation link. No exceptions. Every data point, every finding, every number must link to its source URL. In the markdown report, use [source](url). In Confluence HTML, use <a href="url">source</a>. If a data point can't be sourced, mark it [unverified] explicitly.
2. News section is strictly last 7 days. Only include articles published in the last 7 days. If this is the first scan and background context is needed, put it in a separate "Background Context (first scan only)" section clearly labeled as NOT from this week.
3. Current year filter — all WebSearch queries MUST include 2026 or when:7d. Never surface results from 2024 or earlier.
4. No fabrication — if a data point can't be fetched, say "Data unavailable" not a guess. If a Play Store page was truncated, say "Play Store truncated" — don't omit the column.
5. Global-app caveat — if any tracked competitor uses a single global app/listing, note that its install counts and ratings reflect worldwide usage, not your region. Add region-specific MAU/usage from credible sources where available, and flag the caveat in every report.
6. Never trust reference files as ground truth — competitors.md contains hints, not verified data. Every number (installs, ratings, scale claims) MUST be verified against the actual source during each scan. If Play Store data is truncated, cross-check with AppBrain (https://www.appbrain.com/app/{package_id}) or Sensor Tower. Note the verification source. If unverifiable, mark [unverified].
7. Actionable > Informational — every finding should end with "so what?" for your product.
8. 3-minute read — the full report should take under 3 minutes to scan. Cut ruthlessly.
9. Date context on background articles — when citing articles, always include the publication month/year so the reader knows how fresh the information is. Mark stale articles (>3 months old) explicitly.