What does this skill do, and when should you use it?
This is a sub-skill inside the ai-marketing-claude suite, triggered by /market competitors <url>. It works in five phases: identify 6-10 competitors, analyze messaging and pricing, run SWOT, propose steal-worthy tactics and differentiation angles, and deliver an ongoing monitoring plan. Output is written to COMPETITOR-REPORT.md in a format suited to both strategic decision-making and client presentations. It relies on web fetching of the target and competitor sites, optionally running the repo's Python scanning script.
Identifies three tiers of competitors (direct, indirect, aspirational) via keyword searches, on-site comparison pages, review platforms (G2, Capterra, Trustpilot), and Reddit/Twitter communities; scrapes each competitor's homepage copy, pricing page, blog, social profiles, and tech stack (preferring scripts/competitor_scanner.py, falling back to WebFetch); builds feature matrices, pricing matrices, social presence tables, and review intelligence matrices; plots a simplicity-power / budget-premium positioning map; produces per-competitor and aggregate SWOT; extracts 5-10 steal-worthy tactics, a differentiation framework, vs/alternatives page outlines, and switching narratives; writes the full report to COMPETITOR-REPORT.md and prints a terminal summary.
- An agency preparing a client proposal needs a polished competitor comparison to drop into a deck
- A SaaS founder wants to know if their pricing sits above or below market and where features lag
- A content lead hunting for SEO topics competitors cover but the target brand doesn't
- A marketer building [competitor]-alternative landing pages needs ready-made outlines and switching narratives
- A solopreneur who wants an executable long-term monitoring checklist and response playbook for competitor moves
How do you install this skill?
- The skill relies on scraping G2, Capterra, Trustpilot, Reddit, and Twitter/X, which may violate those platforms' terms of service; assess compliance before use.
- The referenced scripts/competitor_scanner.py was not included in the review evidence and is unaudited; read its source before running it.
- Many report figures (ratings, follower counts, posting frequency) are model estimates rather than measurements; manually verify numeric claims before client delivery.
- The core workflow depends entirely on overseas services and may largely fail on mainland-China networks; no Chinese-language support is declared.
- No versioning or changelog exists; behavior may change across updates — pin to a repository revision.
- Shell / CLI
- Network access
- Local filesystem
Python (for scripts/competitor_scanner.py)WebFetch-capable tool if the script is unavailable
Install the whole suite first: curl -fsSL https://raw.githubusercontent.com/zubair-trabzada/ai-marketing-claude/main/install.sh | bash, or git clone the repo and run ./install.sh. This skill lives at skills/market-competitors/SKILL.md and installs with the suite. Optionally pip install reportlab for the suite's PDF reporting (a suite-level capability). Uninstall with ./uninstall.sh.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/zubair-trabzada/ai-marketing-claude.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/market-competitors" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
In Claude Code, run /market competitors <url> (e.g., /market competitors https://calendly.com). The skill fetches the target site, identifies competitors, analyzes each, and saves the report as COMPETITOR-REPORT.md. If other suite outputs exist (e.g., MARKETING-AUDIT.md, COPY-SUGGESTIONS.md), the report references them; follow up with /market copy or /market ads as suggested.
What are this skill's strengths and limitations?
- Complete methodology: competitor tiering, positioning map, pricing/feature matrices, SWOT, and review mining in one pass
- Action-oriented output: vs-page outlines, switching narratives, and differentiation frameworks are directly usable on landing pages and proposals
- Includes an automated data-collection script with a documented WebFetch fallback
- Consistent report structure makes it reusable as a client deliverable
- Triggered only via the /market slash command; porting to other platforms requires rewriting the invocation
- Analysis quality depends entirely on publicly fetchable data — hidden pricing and internal data are out of reach
- No test suite in the repo; no evidence of validated script or prompt reliability
- Social follower counts and review ratings depend on third-party platform accessibility and may fail or go stale
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Competitive Intelligence Analysis this page | 41 · Not recommended | ★ 2.8k | 7mo ago | MIT |
| Blog Strategy Architect | 49 · Use with care | ★ 2.4k | 11d ago | MIT |
| Strategic SEO Planner | 47 · Use with care | ★ 19k | 20d ago | MIT |
| Blog Brief Planner | 44 · Not recommended | ★ 2.4k | 11d ago | MIT |
| Content Gap Analysis | 52 · Use with care | ★ 3k | 11d ago | Apache-2.0 |
How did FollowSkills review this skill?
The skill itself is modest in scope: it fetches target and competitor web pages and writes one COMPETITOR-REPORT.md to the working directory; no credential access, covert exfiltration, or destructive defaults. But data-flow disclosure is incomplete (scraping G2/Capterra/Trustpilot/Reddit may violate their ToS with no user notice), there is no confirmation mechanism, and the referenced scripts/competitor_scanner.py is not present in the evidence so its behavior is unauditable; the unverified publisher leaves attribution and maintenance responsibility unclear. Deducted for: scraping-compliance disclosure, confirmation, script auditability.
Instructions are internally consistent — trigger command, analysis framework, and output template correspond cleanly — and a WebFetch fallback for a missing script shows forethought. However the key script path cannot be statically verified, most data points (follower counts, ratings, posting frequency) rely on unreliable manual estimation, and there is no failure-feedback design for abnormal inputs (unreachable sites, missing pricing pages). Static cap is 10 and not reached; scored 8.
Scenario is clear (`/market competitors <url>`), inputs (a URL) and output (COMPETITOR-REPORT.md) are explicit, and semantic triggering is precise. But non-fit boundaries are undeclared (non-SaaS sites, login-walled sites), the workflow depends entirely on overseas services (Google, G2, Capterra, Twitter/X, Reddit) that are poorly reachable from mainland China, and there is no Chinese-language support. Deducted for: missing boundaries, environment fit.
Docs are well layered with category tables, output templates, and cross-skill integration notes; the repo carries MIT licensing and an uninstall script. But this skill has no versioning, no changelog, no known-limitation disclosure, dependencies are only thinly covered (reportlab), and the maintenance/update path is unclear with an unverified publisher. Deducted for: version governance, limitations disclosure, maintenance path.
Output templates are complete (pricing matrix, SWOT, positioning map, alternative-page strategy) and directly presentable to clients, giving organizational value over manual analysis. But a static read cannot verify that representative outputs are directly usable; report quality hinges on live scraping and estimated figures (scores, followers) whose inaccuracy would require heavy rework. Cap is 7 with limited evidence; scored 5.
Evidence consists of author-written instructions and a README terminal transcript that appears demonstrative rather than a real execution record; there are no tests, no CI evidence, and no third-party execution material for this skill. Fact and inference are mixed inside templates (e.g., 'Estimated impact Low/Medium/High'). Cap is 5 with only documentation covered; scored 3.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →