功能描述

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

使用说明 (SKILL.md)

Competitive Teardown

Name: competitive-teardown
Author: alirezarezvani

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:

Define competitors — List 2–4 competitors to analyze. Confirm which is the primary focus.
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.
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.
Generate outputs — Populate the templates in references/analysis-templates.md (Feature Matrix, Pricing Analysis, SWOT, Positioning Map, UX Audit).
Build action plan — Translate findings into the Action Items template (quick wins / medium-term / strategic).
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=\x3Ccompetitor_name>&entity=software&limit=1
# Extract trackId, then:
GET https://itunes.apple.com/rss/customerreviews/id=\x3CtrackId>/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)

Executive Summary — Threat level (LOW/MEDIUM/HIGH/CRITICAL), top strength, top opportunity, recommended action
Market Position — 2x2 positioning map
Feature Scorecard — 12-dimension radar or table, total scores
Pricing Analysis — Comparison table + key insight
UX Highlights — What they do better (3 bullets) vs. where we win (3 bullets)
Voice of Customer — Top 3 review complaints (quoted or paraphrased)
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

安全使用建议

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.

功能分析

Type: OpenClaw Skill Name: competitive-teardown Version: 1.0.0 The skill bundle is a legitimate tool for competitive analysis, consisting of a Python scoring script (competitive_matrix_builder.py), markdown templates, and strategic frameworks. The Python script performs standard data processing and reporting without any risky system calls or network activity. While SKILL.md contains a minor documentation discrepancy claiming 'executable scripts' exist in a reference file that only contains text-based guidance, there is no evidence of malicious intent, data exfiltration, or prompt injection attacks.

能力评估

✓ 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).

ℹ 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.

ℹ 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.

版本历史

v1.0.0

Initial publish

v2.1.1

v2.1.1: optimization, reference splits

元数据

Slug competitive-teardown

版本 1.0.0

许可证 MIT-0

累计安装 4

当前安装数 4

历史版本数 2

常见问题