competitive-teardown

Analyzes competitor products and companies by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into struc...

Security Scan

The skill's files and runtime instructions are consistent with a competitive-intelligence tool; nothing in the package indicates it is trying to do something other than collect and synthesize publicly available signals—but there are a few small inconsistencies you should review before running it.

Purpose & Capability

The name/description (competitive teardown) matches the included templates, rubric, and a local Python matrix-builder script. Requested resources are minimal (no env vars, no binaries). The references discuss the exact data sources the skill claims to use (pricing pages, app stores, job postings, SEO, social).

Instruction Scope

SKILL.md stays on-task: it instructs collecting public signals, applying a 12-dimension rubric, and filling templates. It references using APIs (iTunes, Twitter/X API v2, Reddit, LinkedIn) and promises 'full executable scripts' in the data-collection reference, but the packaged references are prose (no network-fetching scripts for each source). The guidance includes ethical/TOS cautions. Nothing instructs reading unrelated system files or exfiltrating secrets.

Install Mechanism

No install spec; instruction-only plus one local Python script. That is low-risk from an installation perspective because nothing is auto-downloaded or extracted.

Credentials

The skill declares no required credentials or env vars, which is proportionate for the stated purpose. However, the instructions expect use of external APIs (Twitter/X, iTunes, LinkedIn, etc.) that will typically require API keys or scraping tools; the skill does not declare or manage those credentials—users or the agent environment will need to provide them. Confirm how you or your agent will supply API credentials and that you won't expose unrelated secrets.

Persistence & Privilege

always is false and model invocation is allowed (platform default). The skill does not request persistent system presence, modify other skills, or claim system-wide changes.

Assessment

This package looks like a legitimate competitive-intelligence helper, but review a few things before using it: 1) The SKILL.md mentions 'full executable scripts' for data collection but the included data-collection reference is descriptive rather than containing fetch/scrape scripts—expect to supply your own collectors or API keys. 2) If you intend to pull data from Twitter/X, LinkedIn, App Stores, or other APIs, ensure you have appropriate API keys and that use complies with each service's terms of service; do not feed private/internal documents or unrelated credentials to the agent. 3) Inspect the included Python script (scripts/competitive_matrix_builder.py) before running; the repository shows the script truncated at the end (an apparent cut in the main() arg parsing) so verify the script is complete and safe to execute. 4) If you want the agent to fetch data autonomously, restrict which credentials are available to the agent and log/examine network calls during initial runs. If you need higher assurance, have a developer do a quick code review of the script and any data-collection code you plan to run.

Tier: POWERFUL
Category: Product Team
Domain: Competitive Intelligence, Product Strategy, Market Analysis

---

When to Use

• Before a product strategy or roadmap session
• When a competitor launches a major feature or pricing change
• Quarterly competitive review
• Before a sales pitch where you need battle card data
• When entering a new market segment

---

Teardown Workflow

Follow these steps in sequence to produce a complete teardown:

1. Define competitors — List 2–4 competitors to analyze. Confirm which is the primary focus.
2. Collect data — Use references/data-collection-guide.md to gather raw signals from at least 3 sources per competitor (website, reviews, job postings, SEO, social).
   Validation checkpoint: Before proceeding, confirm you have pricing data, at least 20 reviews, and job posting counts for each competitor.
3. Score using rubric — Apply the 12-dimension rubric below to produce a numeric scorecard for each competitor and your own product.
   Validation checkpoint: Every dimension should have a score and at least one supporting evidence note.
4. Generate outputs — Populate the templates in references/analysis-templates.md (Feature Matrix, Pricing Analysis, SWOT, Positioning Map, UX Audit).
5. Build action plan — Translate findings into the Action Items template (quick wins / medium-term / strategic).
6. Package for stakeholders — Assemble the Stakeholder Presentation using outputs from steps 3–5.

---

Data Collection Guide

Full executable scripts for each source are in references/data-collection-guide.md. Summaries of what to capture are below.

1. Website Analysis

Key things to capture:

• Pricing tiers and price points
• Feature lists per tier
• Primary CTA and messaging
• Case studies / customer logos (signals ICP)
• Integration logos
• Trust signals (certifications, compliance badges)

2. App Store Reviews

Review sentiment categories:

• Praise → what users love (defend / strengthen these)
• Feature requests → unmet needs (opportunity gaps)
• Bugs → quality signals
• UX complaints → friction points you can beat them on

Sample App Store query (iTunes Search API):

GET https://itunes.apple.com/search?term=<competitor_name>&entity=software&limit=1
# Extract trackId, then:
GET https://itunes.apple.com/rss/customerreviews/id=<trackId>/sortBy=mostRecent/json?l=en&limit=50

Parse entry[].content.label for review text and entry[].im:rating.label for star rating.

3. Job Postings (Team Size & Tech Stack Signals)

Signals from job postings:

• Engineering volume → scaling vs. consolidating
• Specific tech mentions → stack (React/Vue, Postgres/Mongo, AWS/GCP)
• Sales/CS ratio → product-led vs. sales-led motion
• Data/ML roles → upcoming AI features
• Compliance roles → regulatory expansion

4. SEO Analysis

SEO signals to capture:

• Top 20 organic keywords (intent: informational / navigational / commercial)
• Domain Authority / backlink count
• Blog publishing cadence and topics
• Which pages rank (product pages vs. blog vs. docs)

5. Social Media Sentiment

Capture recent mentions via Twitter/X API v2, Reddit, or LinkedIn. Look for recurring praise, complaints, and feature requests. See references/data-collection-guide.md for API query examples.

---

Scoring Rubric (12 Dimensions, 1-5)

#	Dimension	1 (Weak)	3 (Average)	5 (Best-in-class)
1	Features	Core only, many gaps	Solid coverage	Comprehensive + unique
2	Pricing	Confusing / overpriced	Market-rate, clear	Transparent, flexible, fair
3	UX	Confusing, high friction	Functional	Delightful, minimal friction
4	Performance	Slow, unreliable	Acceptable	Fast, high uptime
5	Docs	Sparse, outdated	Decent coverage	Comprehensive, searchable
6	Support	Email only, slow	Chat + email	24/7, great response
7	Integrations	0-5 integrations	6-25	26+ or deep ecosystem
8	Security	No mentions	SOC2 claimed	SOC2 Type II, ISO 27001
9	Scalability	No enterprise tier	Mid-market ready	Enterprise-grade
10	Brand	Generic, unmemorable	Decent positioning	Strong, differentiated
11	Community	None	Forum / Slack	Active, vibrant community
12	Innovation	No recent releases	Quarterly	Frequent, meaningful

Example completed row (Competitor: Acme Corp, Dimension 3 – UX):

Dimension	Acme Corp Score	Evidence
UX	2	App Store reviews cite "confusing navigation" (38 mentions); onboarding requires 7 steps before TTFV; no onboarding wizard; CC required at signup.

Apply this pattern to all 12 dimensions for each competitor.

---

Templates

Full template markdown is in references/analysis-templates.md. Abbreviated reference below.

Feature Comparison Matrix

Rows: core features, pricing tiers, platform capabilities (web, iOS, Android, API).
Columns: your product + up to 3 competitors.
Score each cell 1–5. Sum to get total out of 60.
Score legend: 5=Best-in-class, 4=Strong, 3=Average, 2=Below average, 1=Weak/Missing

Pricing Analysis

Capture per competitor: model type (per-seat / usage-based / flat rate / freemium), entry/mid/enterprise price points, free trial length.
Summarize: price leader, value leader, premium positioning, your position, and 2–3 pricing opportunity bullets.

SWOT Analysis

For each competitor: 3–5 bullets per quadrant (Strengths, Weaknesses, Opportunities for us, Threats to us). Anchor every bullet to a data signal (review quote, job posting count, pricing page, etc.).

Positioning Map

2x2 axes (e.g., Simple ↔ Complex / Low Value ↔ High Value). Place each competitor and your product. Bubble size = market share or funding. See references/analysis-templates.md for ASCII and editable versions.

UX Audit Checklist

Onboarding: TTFV (minutes), steps to activation, CC-required, onboarding wizard quality.
Key workflows: steps, friction points, comparative score (yours vs. theirs).
Mobile: iOS/Android ratings, feature parity, top complaint and praise.
Navigation: global search, keyboard shortcuts, in-app help.

Action Items

Horizon	Effort	Examples
Quick wins (0–4 wks)	Low	Add review badges, publish comparison landing page
Medium-term (1–3 mo)	Moderate	Launch free tier, improve onboarding TTFV, add top-requested integration
Strategic (3–12 mo)	High	Enter new market, build API v2, achieve SOC2 Type II

Stakeholder Presentation (7 slides)

1. Executive Summary — Threat level (LOW/MEDIUM/HIGH/CRITICAL), top strength, top opportunity, recommended action
2. Market Position — 2x2 positioning map
3. Feature Scorecard — 12-dimension radar or table, total scores
4. Pricing Analysis — Comparison table + key insight
5. UX Highlights — What they do better (3 bullets) vs. where we win (3 bullets)
6. Voice of Customer — Top 3 review complaints (quoted or paraphrased)
7. Our Action Plan — Quick wins, medium-term, strategic priorities; Appendix with raw data

Related Skills

• Product Strategist (product-team/product-strategist/) — Competitive insights feed OKR and strategy planning
• Landing Page Generator (product-team/landing-page-generator/) — Competitive positioning informs landing page messaging