brand-research
Research a company or brand from its website and produce a reusable Brand Core covering products, audience, competitors, positioning, offers, messaging evidence, voice, and visual identity. Use before growth, ad, content, creator, or product work when reliable brand context is missing.
Install / Use
npx skills add gooseworks-ai/goose-skills --skill brand-researchInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Our assessment of brand-research
brand-research scores 90/100 on our quality scale, 1274th of 2,912 Automation skills we index (top 44%).
Its SKILL.md is 6.3 KB long, well organised into 15 sections with 2 code examples: a thorough specification that gives an agent plenty to work with.
With 1,222 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 33 days ago, so brand-research is actively maintained.
- Our last check on 2026-10-06 found the source still online.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
brand-research compared with similar skills
All 4 of these similar skills score higher than brand-research; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| brand-research (this skill)by gooseworks-ai | 90 | 1.2k | 33d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 102.4k | 1d ago | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 89.7k | 3d ago | MCP Server |
| LocalAIby mudler | 100 | 50.0k | today | MCP Server |
| rufloby ruvnet | 100 | 79.3k | today | MCP Server |
Frequently asked questions
- How do I install brand-research?
- Run
npx skills add gooseworks-ai/goose-skills --skill brand-research. The install tabs above show the steps for each supported agent. - Which AI agents does brand-research work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is brand-research safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is brand-research still maintained?
- The repository was last updated 33 days ago, so brand-research is actively maintained.
Skill content
View source on GitHubname: brand-research description: Research a company or brand from its website and produce a reusable Brand Core covering products, audience, competitors, positioning, offers, messaging evidence, voice, and visual identity. Use before growth, ad, content, creator, or product work when reliable brand context is missing.
Brand Research
Build a sourced Brand Core that future research, analysis, and creative workflows can reuse without rediscovering the company every time.
The required path is research-only and works with local files. GooseWorks sync, ad imports, and paid asset generation are optional extensions—not prerequisites for a complete result.
Inputs
website— required canonical company or brand website.focus— optional product, collection, market, or campaign to prioritize.output_dir— optional; defaults to a clearly named local brand folder.depth—quickorfull(defaultfull).sync_to_gooseworks— optional, default false.include_existing_ads— optional, default true in full mode.generate_assets— optional paid extension, default false.
Brand Core output
Create:
brand-core/
summary.md
products.md
audience.md
competitors.md
positioning-and-offers.md
messaging.md
visual-identity.md
sources.md
brand-core.json
Use local paths that work outside GooseWorks. brand-core.json is a structured echo for other agent skills; the Markdown remains the human-readable source of truth.
Workflow
1. Resolve the entity
Open the provided website and confirm the company name, canonical domain, market, and focus product. If the site is inaccessible or the identity remains ambiguous, ask for the minimum clarification instead of researching the wrong entity.
2. Research the first-party source
Review the homepage, product/collection pages, about page, pricing or offer pages, FAQ, policies, store navigation, social links, and press/brand resources. Capture:
- what the company sells and how the catalog is organized;
- prices, offers, bundles, guarantees, subscriptions, and availability;
- product claims, ingredients/materials, use cases, and differentiators;
- stated audiences and customer outcomes;
- brand voice, visual system, proof, and trust markers.
Do not turn marketing claims into facts. Label them as brand-stated claims until corroborated.
3. Research audience evidence
Use reviews, forums, search, social posts, and comments to identify pains, desired outcomes, triggers, objections, alternatives, product language, and use contexts. Preserve short representative language with source links. Use comment-mining for relevant public social threads.
4. Map competitors
Identify direct competitors, substitutes, and reference brands. For each, record positioning, key offer, price band when visible, proof style, and how the focus brand plausibly wins or loses. Separate a verified competitor from a likely competitor inferred from category overlap.
5. Inspect current creative when useful
In full mode, use competitor-ad-intelligence with the brand as advertiser to inspect current Meta, Google, or LinkedIn ads through ScrapeCreators. Extract recurring hooks, offers, proof, product presentation, formats, CTAs, and landing destinations. Do not infer spend or conversion performance from ad-library presence.
6. Synthesize the Brand Core
Write:
summary.md: company, category, markets, business model, brand promise, voice in three words, and important unknowns.products.md: product/collection catalog with source URL, price/offer, claims, use cases, and priority.audience.md: audience segments, jobs-to-be-done, triggers, pains, objections, alternatives, and exact sourced language.competitors.md: direct competitors, substitutes, reference brands, positioning comparison, and evidence.positioning-and-offers.md: value proposition, differentiators, offers, proof, guarantees, and gaps.messaging.md: repeated claims, hooks, objections, proof points, CTAs, useful angles, and what the brand should not say.visual-identity.md: logo use, colors, typography when identifiable, photography, layout, product presentation, and off-brand patterns.sources.md: URL, access date, source type, and which claims it supports.
7. Confirm uncertainty
Show a concise confirmation summary: company, priority products/services, audience, likely competitors, offers, and messaging angles. Mark low-confidence findings and ask only about material gaps.
8. Optional GooseWorks sync
Only when requested and the GooseWorks MCP tools are available:
- Reuse an existing brand when the domain matches; otherwise create one.
- Import the ecommerce catalog through the existing product import tools when relevant.
- Update the Brand Kit/Core using only confirmed findings.
- Never overwrite stronger first-party brand data without showing the change.
The local Brand Core remains usable even if sync fails.
9. Optional paid assets
Asset generation is never required to finish brand research. If the user explicitly asks:
- use the
goose-product-photosskill for product photography; - use
goose-graphicsfor branded graphics; - quote credits and confirm before any paid generation.
brand-core.json
{
"brand": {"name":"","website":"","category":"","markets":[]},
"products": [{"name":"","url":"","price":"","claims":[],"offers":[]}],
"audiences": [{"segment":"","jobs":[],"pains":[],"objections":[],"language":[]}],
"competitors": [{"name":"","url":"","relationship":"direct|substitute|reference","evidence":""}],
"positioning": {"promise":"","differentiators":[],"proof":[],"offers":[]},
"messaging": {"hooks":[],"angles":[],"claims":[],"ctas":[],"never_say":[]},
"visual_identity": {"colors":[],"typography":[],"photography":[],"off_limits":[]},
"sources": [{"url":"","accessed_at":"","supports":[]}],
"unknowns": []
}
Omit unknown values rather than inventing them.
Quality checks
- Every material claim is attributable to a source or clearly labeled as an inference.
- Products and offers match the live site and include canonical URLs.
- Audience conclusions include customer evidence, not only brand copy.
- Competitors are classified as verified or inferred.
- Messaging angles trace back to repeated evidence.
- No paid generation ran without explicit confirmation.
- The local output is complete even when GooseWorks is not connected.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
