What does this skill do, and when should you use it?
This is the market-brand skill of the AI Marketing Suite for Claude Code, triggered via `/market brand <url>`. It gathers material from a brand's homepage, blog, social profiles, and emails, scores the voice along four dimensions (formal-casual, serious-playful, technical-simple, reserved-bold) with required quoted evidence, and maps the brand to one of five personality archetypes: Authority, Innovator, Friend, Rebel, or Guide. It also runs vocabulary analysis, a competitor voice comparison, and a channel-by-channel consistency audit before writing a BRAND-VOICE.md file with Do's and Don'ts, copy samples, and a messaging hierarchy. Best suited to marketing leads who need to codify brand voice for a team, freelancers, or an agency.
Collects source material in three priority tiers (homepage/about/product pages required; blog, social, email secondary; job postings, press releases, ad copy tertiary); reads web content via browser tools or the analyze_page.py script; scores each of the four voice dimensions 1-10 with 3-5 quoted examples as evidence; maps tone shifts across contexts (homepage, error pages, CTAs, email subject lines); matches the brand to one of five archetypes; analyzes frequently used words, avoided words, and signature phrases; compares voice against 2-3 competitors in a matrix (reusing /market competitors data if available); audits consistency per channel with an overall score; drafts a five-level messaging hierarchy (tagline, value props, elevator pitch, boilerplate, brand story); produces a voice chart with Do's and Don'ts plus 5-8 sample copy pieces; and saves everything to a BRAND-VOICE.md file.
- A marketing lead documenting the current voice baseline before a rebrand or identity refresh
- A founder handing copywriting to freelancers or an agency who needs a deliverable brand voice guide
- A brand with inconsistent tone across website, social, and email that needs the gaps located and fixed
- A team comparing its voice to 2-3 competitors to find unoccupied voice territory
- A newly hired copywriter who needs a document that lets them write on-brand from day one
How do you install this skill?
- The skill instructs fetching target sites and their linked social profiles without stating data use or scope; check target-site terms of service and scraping compliance before use.
- The referenced analyze_page.py script was not provided in the reviewed evidence; its behavior on failure is unknown.
- No versioning or changelog; behavior may change on update — pin to the reviewed revision.
- Core function depends on live access to overseas websites; capability for Chinese-language and non-English brands is undeclared, and no Chinese output support is provided.
- All scoring methodology is author-asserted and unverified by independent testing; generated documents require human review.
- Network access
- Local filesystem
Optional: Python (analyze_page.py script)Browser/web access tools for fetching site content
This skill ships with the ai-marketing-claude collection. Install everything with: curl -fsSL https://raw.githubusercontent.com/zubair-trabzada/ai-marketing-claude/main/install.sh | bash, or manually: git clone https://github.com/zubair-trabzada/ai-marketing-claude.git && cd ai-marketing-claude && ./install.sh. Skills are installed under ~/.claude/skills/. Optionally pip install reportlab for PDF report support (this skill itself does not generate PDFs). 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 "$tmp/skills/market-brand" ~/.claude/skills/
rm -rf "$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 brand https://example.com (or /market brand with no URL). The skill guides Claude to fetch the brand's web and social content and work through the analysis steps. If you previously ran /market competitors <url>, the competitor voice comparison reuses that data. Output is saved as BRAND-VOICE.md in the working directory. The source does not document any offline fallback if web access is unavailable.
What are this skill's strengths and limitations?
- Complete methodology: source gathering, four-dimension scoring, archetype matching, vocabulary and consistency audits cover the full voice-analysis workflow
- Evidence-mandated scoring: every dimension rating requires 3-5 quoted examples, reducing pure subjectivity
- Actionable output: BRAND-VOICE.md includes Do's and Don'ts and 5-8 diverse copy samples a new writer can model directly
- Integrates with the suite's competitor-analysis skill so the comparison section doesn't start from scratch
- Depends on Claude actually reaching the target site and social profiles; no failure-mode guidance is documented if fetching fails or content is gated
- Voice ratings are model judgment; the 1-10 scale's reliability is untested against any evaluation set
- Competitor comparison expects 2-3 rivals but doesn't explain how they're identified automatically (relies on the user or a prior /market competitors run)
- No test suite or quality benchmark; the parallel-subagent machinery described in the repo belongs to other skills — expect no audit scores or PDFs from this one
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 |
|---|---|---|---|---|
| Brand Voice Analysis & Guidelines Skill this page | 46 · Use with care | ★ 2.8k | 7mo ago | MIT |
| Blog Persona: Structured Brand Voice Management | 48 · Use with care | ★ 2.4k | 12d ago | MIT |
| Blog Brand Context | 49 · Use with care | ★ 2.4k | 12d ago | MIT |
| Brand Language Codifier | 63 · Recommended | ★ 3k | 11d ago | Apache-2.0 |
| Brand Voice Dossier Builder | 59 · Recommended | ★ 3k | 11d ago | Apache-2.0 |
Within the same AI Marketing Suite, this is the voice-definition skill, complementary to /market competitors (competitive intelligence) and /market copy (copy generation): it defines 'how the brand sounds' while the copy skill writes the actual content. The source names no third-party alternatives.
How did FollowSkills review this skill?
A pure prompt-workflow skill with no destructive defaults, credential handling, or covert exfiltration, so the risk surface is small; however it instructs the agent to fetch target sites and linked social profiles via browser tools without disclosing data flow, scoping, or any user confirmation mechanism — risks are visible but permissions and transparency are incomplete.
The ten steps are internally self-consistent with a fully specified output format, making the happy path plausible; but the referenced analyze_page.py script is not present in the evidence, its usability cannot be statically verified, and there is no failure-feedback design for unreachable sites or empty content.
Triggers are explicit (/market brand <url>) and scenarios are clear (brand voice analysis, competitor comparison), but no non-fit boundaries are declared (e.g., brands without a public site, non-English brands), there is no Chinese-language support, and core function depends on live scraping of overseas websites.
Docs are well structured with progressive disclosure and ample examples; but there is no versioning, changelog, known-limitations disclosure, or maintenance responsibility statement. MIT license is present but publisher identity is unverified; deductions are mainly for missing governance and limitation disclosure.
The BRAND-VOICE.md output spec is complete and directly usable with real marginal value over manual analysis; but all quality claims (scoring, archetype judgment) are unverified by execution — statically only structural soundness can be confirmed, so actual output quality remains unproven.
Evidence consists only of the author's own instruction document and an illustrative README terminal transcript; no tests, no CI, no third-party execution evidence — the demo output is promotional and verifiability is thin.
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 →