skills/business/founder/founderskills/competitor-intel/SKILL.md
Analyzes competitors using web research to provide verified business metrics, actionable leverage strategies, and predicted next moves. Use when user needs competitive intelligence, competitor analysis, market positioning insights, or strategic leverage opportunities.
npx skillsauth add lunartech-x/superpowers competitor-intelInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Provide data-backed competitive intelligence by researching real signals across the web—no assumptions, no made-up numbers.
Check $ARGUMENTS first to determine execution mode:
Respond with: "competitor-intel loaded, proceed with competitor name and any context (website, industry, etc.)"
Then wait for the user to provide their requirements in the next message.
Proceed immediately to Task Execution (skip the "loaded" message).
When user requirements are available (either from initial $ARGUMENTS or follow-up message):
Check if FOUNDER_CONTEXT.md exists in the project root.
From the user's requirements, extract:
This skill REQUIRES web search. Do not proceed without searching.
Execute web searches across these sources:
Search for verified data only. Query patterns:
"[Competitor]" revenue OR MRR OR ARR site:crunchbase.com"[Competitor]" funding raised valuation site:crunchbase.com"[Competitor]" employees headcount site:linkedin.com"[Competitor]" revenue growth OR metrics"[Competitor]" pricing customers"[Competitor]" CEO OR founder interview revenue"[Competitor]" Series A OR Series B OR fundingSearch for web traffic and search presence signals:
"[Competitor]" site:similarweb.com (traffic estimates, top pages, traffic sources)"[Competitor]" site:ahrefs.com (backlinks, domain rating, organic keywords)"[Competitor]" site:semrush.com (traffic, keyword rankings, ad spend)"[Competitor]" site:trends.google.com (search interest over time)[Competitor website domain] site:builtwith.com (tech stack, tools used)Search for product and development signals:
"[Competitor]" site:github.com (open source activity, tech stack, hiring signals)[Competitor GitHub org] (commit frequency, contributors, project activity)"[Competitor]" API OR integration OR developerSearch for ad strategy and spend signals:
https://www.facebook.com/ads/library/ for [Competitor]"[Competitor]" ads site:facebook.com/ads/library"[Competitor]" advertising spend OR ad budget"[Competitor]" marketing campaignSearch for complaints, issues, and struggles:
"[Competitor]" reviews site:g2.com"[Competitor]" reviews site:capterra.com"[Competitor]" reviews site:trustpilot.com"[Competitor]" complaints OR issues OR problems"[Competitor]" "doesn't work" OR "broken" OR "terrible""[Competitor]" layoffs OR firing OR cuts"[Competitor]" lawsuit OR suedSearch for hiring, product, and strategic signals:
"[Competitor]" hiring site:linkedin.com"[Competitor]" job openings"[Competitor]" new feature OR launch OR release"[Competitor]" roadmap OR upcoming"[Competitor]" partnership OR integration"[Competitor]" site:twitter.com OR site:x.com (founder/company posts)From research, extract ONLY verified numbers with sources:
CRITICAL RULE: If a metric cannot be found with a source, mark it as "Not publicly available" — DO NOT estimate or assume.
Analyze collected data to find 3 actionable weak spots:
Look for patterns in:
For each weakness, formulate an actionable strategy your company can execute.
Based on all signals, predict what the competitor will likely do next:
Signals to interpret:
Structure findings according to Output Format section.
Hard constraints. No interpretation.
# Competitor Intel: [Competitor Name]
**Generated:** [Date]
**Sources searched:** [Count] sources across Crunchbase, LinkedIn, G2, Capterra, news, social
---
## 1. Verified Business Metrics
| Metric | Value | Source | Date |
|--------|-------|--------|------|
| Funding Raised | $X | [Source](url) | [Date] |
| Valuation | $X | [Source](url) | [Date] |
| Employee Count | X | [Source](url) | [Date] |
| MRR/ARR | Not publicly available | — | — |
| Customer Count | ~X | [Source](url) | [Date] |
| Churn Rate | Not publicly available | — | — |
**Key Observations:**
- [Insight about their financial health]
- [Insight about their growth trajectory]
---
## 2. Leverage Strategies
### Strategy 1: [Name]
**Weakness exploited:** [What you found]
**Evidence:** [Quote or data point with source]
**Action steps:**
1. [Specific action]
2. [Specific action]
3. [Specific action]
**Expected outcome:** [What this achieves]
---
### Strategy 2: [Name]
**Weakness exploited:** [What you found]
**Evidence:** [Quote or data point with source]
**Action steps:**
1. [Specific action]
2. [Specific action]
3. [Specific action]
**Expected outcome:** [What this achieves]
---
### Strategy 3: [Name]
**Weakness exploited:** [What you found]
**Evidence:** [Quote or data point with source]
**Action steps:**
1. [Specific action]
2. [Specific action]
3. [Specific action]
**Expected outcome:** [What this achieves]
---
## 3. Predicted Next Moves
### Prediction 1: [What they'll likely do]
**Confidence:** High/Medium/Low
**Supporting signals:**
- [Signal 1 with source]
- [Signal 2 with source]
**Implication for you:** [How to prepare/respond]
### Prediction 2: [What they'll likely do]
**Confidence:** High/Medium/Low
**Supporting signals:**
- [Signal 1 with source]
- [Signal 2 with source]
**Implication for you:** [How to prepare/respond]
---
## 4. Information Gaps
Metrics and data that could not be verified:
- [Item 1]
- [Item 2]
**Suggested next steps to fill gaps:**
- [How to find this information]
Before finalizing, verify ALL of the following:
If ANY check fails → revise before presenting.
Use these unless overridden:
Document any assumptions made in the output.
tools
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
testing
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
development
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
development
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.