brand-research

brand-research is a skill for Claude Code, Codex from gooseworks-ai/goose-skills. It costs 58 tokens per session (1,383 once invoked), scanned A, original, MIT.

A research workflow that studies a company or brand from its website and creates a reusable Brand Core. The Brand Core describes what the company sells, who it serves, how it positions itself, and its messaging and visual style.

In plain words
What is it for?
Use it to document products, audiences, competitors, positioning, offers, messaging, visual identity, and research sources in Markdown and JSON files.
Why use it?
It gives later advertising, content, growth, or product work a shared evidence-based understanding of the brand. This avoids repeatedly rediscovering the same company information.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to document products, audiences, competitors, positioning, offers, messaging, visual identity, and research sources in Markdown and JSON files.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gooseworks-ai/goose-skills/brand-research
About the project

Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.

gooseworks-ai/goose-skills · 1,202 stars · on GitHub · gooseworks.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add gooseworks-ai/goose-skills --skill brand-research
Clone the repo
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for brand-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/brand-research/github.svg)](https://agentmods.dev/skills/gooseworks-ai/goose-skills/brand-research)
Your own site
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/brand-research"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/brand-research/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for brand-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/brand-research"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/brand-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,383 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00058 $0.01383
Opus 5 $0.00029 $0.00691
Sonnet 5 $0.00012 $0.00277
Haiku 4.5 $0.00006 $0.00138

Measured 12d ago against content hash 8cb7e25dd766, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

brand-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/fetch_asset.py, scripts/lib.py, scripts/register_asset.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ads/composites/brand-research/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.
  • depthquick or full (default full).
  • 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.

Read the full file on GitHub · 135 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 135 lines · 58 tokens per session scan A 8cb7e25dd766

Subscribe to this mod's changes

brand-research is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 58 tokens to every session and 1,383 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

excalidraw-ai

Create professional Excalidraw diagrams by generating JSON directly. This skill provides the Excalidraw JSON schema reference and professional icon libraries for AI agents to autonomously create diagrams without templates.

jiatastic/open-python-skills · 44 tokens

error-handling

Python error handling patterns for FastAPI, Pydantic, and asyncio. Follows "Let it crash" philosophy - raise exceptions, catch at boundaries. Covers HTTPException, global exception handlers, validation errors, background task failures. Use when: (1) Designing API error responses, (2) Handling RequestValidationError…

jiatastic/open-python-skills · 95 tokens

linting

Python linting with Ruff - an extremely fast linter written in Rust. Use when: (1) Standardizing code quality, (2) Fixing style warnings, (3) Enforcing rules in CI, (4) Replacing flake8/isort/pyupgrade/autoflake, (5) Configuring lint rules and suppressions.

jiatastic/open-python-skills · 74 tokens

logfire

Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.

jiatastic/open-python-skills · 77 tokens

commit-message

Analyze git changes and generate conventional commit messages. Supports batch commits for multiple unrelated changes. Use when: (1) Creating git commits, (2) Reviewing staged changes, (3) Splitting large changesets into logical commits.

jiatastic/open-python-skills · 49 tokens

python-backend

Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring…

jiatastic/open-python-skills · 102 tokens