Competitor intelligence system. Research competitors across web, Reddit, Twitter/X, LinkedIn, and blogs. Build deep competitor profiles, monitor content and positioning changes, track what gets traction, and identify competitive gaps. Covers data collection, content tracking, and strategy analysis. Pure research skill — uses web search, web fetch, and optionally Apify for social scraping. No scripts required.
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Research and monitor competitors across multiple channels. Build profiles, track changes, and translate competitor moves into strategic recommendations.
Three layers:
For each competitor, research across these dimensions using web search and web fetch:
If Apify is available, scrape deeper data:
Without Apify, web search covers the basics — just less structured.
For each competitor, produce a structured profile:
# Competitor Profile: [Company Name]
**Last updated:** [DATE]
**Website:** [URL]
## Overview
- **What they do:** [1-2 sentences]
- **ICP:** [who they sell to]
- **Stage:** [funding, headcount]
- **Pricing:** [model + price points]
## Positioning
- **Value prop:** [their core claim]
- **Key differentiators:** [what they emphasize]
- **Positioning against us:** [how they frame the comparison, if any]
## Product
- **Core features:** [list]
- **Recent launches:** [last 6 months]
- **Integrations:** [key partners]
## Content & Marketing
- **Blog frequency:** [posts/month]
- **Top topics:** [themes they write about]
- **Social activity:** [LinkedIn, Twitter/X presence and engagement level]
- **Content strategy:** [what type of content dominates — thought leadership, SEO, product marketing]
## Customer Evidence
- **Notable customers:** [logos]
- **G2/Capterra rating:** [score + review count]
- **Common praise:** [what customers love]
- **Common complaints:** [what customers dislike]
## Signals
- **Hiring:** [what roles, what it signals]
- **Partnerships:** [recent partnerships]
- **News:** [recent press/announcements]
## Strengths & Weaknesses (vs. You)
### Where they're strong:
- [strength 1]
- [strength 2]
### Where they're weak:
- [weakness 1]
- [weakness 2]
### Your opportunity:
- [gap you can exploit]
After profiling all competitors, produce a landscape view:
# Competitive Landscape — [Your Company] — [DATE]
## Positioning Map
| Company | Core Claim | ICP Focus | Price Point | Key Differentiator |
|---------|-----------|-----------|-------------|-------------------|
| You | [claim] | [ICP] | [price] | [differentiator] |
| Comp 1 | [claim] | [ICP] | [price] | [differentiator] |
| Comp 2 | ... | ... | ... | ... |
## Content Comparison
| Company | Blog Frequency | Top Topics | Social Presence |
|---------|---------------|-----------|-----------------|
| You | [X/month] | [topics] | [LinkedIn/Twitter activity] |
| Comp 1 | [X/month] | [topics] | [activity] |
## Feature Comparison
| Feature | You | Comp 1 | Comp 2 | Comp 3 |
|---------|-----|--------|--------|--------|
| [feature 1] | ✓/✗ | ✓/✗ | ✓/✗ | ✓/✗ |
## Key Takeaways
1. [Most important competitive insight]
2. [Second]
3. [Third]
## Recommended Actions
1. [What to do based on competitive gaps]
2. [Positioning adjustment]
3. [Content/feature opportunity]
For recurring competitive tracking, set up a periodic review:
Monthly check:
What to monitor:
| Component | Cost |
|---|---|
| Web search + fetch (all research) | Free |
| Apify social scraping (optional) | ~$0.50-2.00 per competitor |
| Analysis | Free (LLM reasoning) |
| Total per competitor (baseline) | Free |
| Total per competitor (with Apify) | ~$0.50-2.00 |
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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