SEO Content Brief Generator
AgriciDaniel/claude-seo
Builds research-backed SEO content briefs with competitor scoring, per-section word counts and page-type templates, for new pages or improving existing ones.
Discover all customers of a given company by scanning websites, case studies, review sites, press, social media, job postings, and more.
Install Customer Discovery
$ npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills customer-discovery --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/capabilities/customer-discovery .claude/skills/customer-discovery && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "customer-discovery" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/customer-discovery into .claude/skills/customer-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-discovery", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/customer-discoveryType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills customer-discovery --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/research/capabilities/customer-discovery .agents/skills/customer-discovery && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "customer-discovery" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/customer-discovery into .agents/skills/customer-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-discovery", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills customer-discovery --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/research/capabilities/customer-discovery .cursor/skills/customer-discovery && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "customer-discovery" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/customer-discovery into .cursor/skills/customer-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-discovery", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/gooseworks-ai/goose-skills.git --path skills/research/capabilities/customer-discovery--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills customer-discovery --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/research/capabilities/customer-discovery .gemini/skills/customer-discovery && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "customer-discovery" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/customer-discovery into .gemini/skills/customer-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-discovery", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install gooseworks-ai/goose-skills customer-discoveryInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/research/capabilities/customer-discovery .github/skills/customer-discovery && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "customer-discovery" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/customer-discovery into .github/skills/customer-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-discovery", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills customer-discovery --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/research/capabilities/customer-discovery .opencode/skills/customer-discovery && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "customer-discovery" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/customer-discovery into .opencode/skills/customer-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-discovery", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
customer-discovery- GitHub stars
- 1.2k
- Used in
- 1 other repo
- Token cost
- ~2.1k tokens
- SKILL.md length
- 745 words
- Files
- 5 (incl. scripts)
- Skills in repo
- 273
- Repo updated
- First seen
- Licence
- MIT
At a glance
Discover all customers of a given company by scanning websites, case studies, review sites, press, social media, job postings, and more.
- Works in 6 steps: Gather Inputs → Create Output Directory → Run Sources for Selected Tier → …
- You need competitive intelligence on who a company sells to
- SKILL.md covers Quick Start, Inputs, Procedure and Scripts Reference, plus 1 more section
- Runs Python scripts from its folder; calls python3 and pip3
What it does
Customer Discovery is an agent skill from gooseworks-ai/goose-skills. Discover all customers of a given company by scanning websites, case studies, review sites, press, social media, job postings, and more. Use when you need competitive intelligence on who a company sells to.
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts (for example `scripts/scrape_wayback_logos.py`, `scripts/scrape_website_logos.py` and `scripts/search_builtwith.py`).
It sits in Marketing & SEO, covering Competitor analysis. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.
When your agent uses it
- You need competitive intelligence on who a company sells to
- Tasks that involve Competitor analysis
Example prompts
- “/customer-discovery”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4bbe1ef. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3pip3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip3, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Customer Discovery loads about 2.1k tokens when it runs. Until then it costs about 56 tokens; SKILL.md has 745 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from gooseworks-ai/goose-skills at commit 4bbe1ef, republished under its MIT licence (© gooseworks-ai). 745 words, ~2,099 tokens.
Download SKILL.mdSave it as
.claude/skills/customer-discovery/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- customer-discovery
- description
- Discover all customers of a given company by scanning websites, case studies, review sites, press, social media, job postings, and more. Use when you need competitive intelligence on who a company sells to.
Customer Discovery
Find all customers of a company by scanning multiple public data sources. Produces a deduplicated report with confidence scoring.
Quick Start
Find all customers of DatadogWho are Notion's customers? Use deep mode.Inputs
| Input | Required | Default | Description |
|---|---|---|---|
| Company name | Yes | — | The company to research |
| Website URL | No | Auto-detected | The company's website URL |
| Depth | No | standard | quick, standard, or deep |
Procedure
Step 1: Gather Inputs
Ask the user for:
- Company name (required)
- Company website URL (optional — if not provided, WebSearch for it)
- Depth tier — present these options, default to Standard:
- Quick (~2-3 min): Website logos, case studies, G2 reviews, press search
- Standard (~5-8 min): Quick + blog posts, Wayback Machine, LinkedIn, Twitter, Reddit, HN, job postings, YouTube
- Deep (~10-15 min): Standard + SEC filings, podcasts, GitHub, integration directories, BuiltWith, Crunchbase
Step 2: Create Output Directory
mkdir -p customer-discovery-[company-slug]Step 3: Run Sources for Selected Tier
Collect all results into a running list. For each customer found, record:
- name: Company name
- confidence: high / medium / low
- source_type: e.g., "logo_wall", "case_study", "g2_review", "press", "job_posting"
- evidence_url: URL where the evidence was found
- notes: Brief description of the evidence
Quick Sources
1. Website logo wall
Run the scrape_website_logos.py script:
python3 skills/capabilities/customer-discovery/scripts/scrape_website_logos.py \
--url "[company-url]" --output jsonParse the JSON output and add each result to the customer list.
2. Case studies page
Use WebFetch on the company's case studies page (try /case-studies, /customers, /resources/case-studies). Extract customer names from page headings and content.
3. G2/Capterra reviews
If the review-site-scraper skill is available, use it to find reviewer companies:
python3 skills/capabilities/review-site-scraper/scripts/scrape_reviews.py \
--platform g2 --url "[g2-product-url]" --max-reviews 50 --output jsonFirst, WebSearch for the company's G2 page: site:g2.com "[company]". Extract reviewer company names from review author info.
4. Web search for press
WebSearch these queries and extract customer mentions from results:
"[company]" customer OR "case study" OR partnership"[company]" "we use" OR "switched to" OR "chose"
Standard Sources (in addition to Quick)
5. Company blog posts
WebSearch: site:[company-domain] customer OR "case study" OR partnership OR "customer story"
6. Wayback Machine logos
Run the scrape_wayback_logos.py script:
python3 skills/capabilities/customer-discovery/scripts/scrape_wayback_logos.py \
--url "[company-url]" --output jsonLogos marked still_present: false are especially interesting — they indicate former customers.
7. Founder/exec LinkedIn posts
WebSearch: site:linkedin.com "[company]" customer OR "excited to announce" OR "welcome"
8. Twitter/X mentions
WebSearch: site:twitter.com "[company]" "we use" OR "just switched to" OR "loving"
9. Reddit/HN mentions
WebSearch these queries:
site:reddit.com "we use [company]" OR "[company] customer"site:news.ycombinator.com "[company]" customer OR user
10. Job postings
WebSearch: "experience with [company]" site:linkedin.com/jobs OR site:greenhouse.io OR site:lever.co
Companies requiring experience with the product are likely customers.
11. YouTube testimonials
WebSearch: site:youtube.com "[company]" customer OR testimonial OR review
Deep Sources (in addition to Standard)
12. SEC filings
WebSearch: site:sec.gov "[company]" — Look for mentions in 10-K and 10-Q filings.
13. Podcast transcripts
WebSearch: "[company]" podcast customer OR transcript OR interview
14. GitHub usage signals
WebSearch: site:github.com "[company-package-name]" in dependency files, package.json, requirements.txt, etc.
15. Integration directories
WebFetch marketplace pages where the company lists integrations:
- Salesforce AppExchange
- Zapier integrations page
- Slack App Directory
- Any marketplace relevant to the company
16. BuiltWith detection
python3 skills/capabilities/customer-discovery/scripts/search_builtwith.py \
--technology "[company-slug]" --max-results 50 --output json17. Crunchbase
WebSearch: site:crunchbase.com "[company]" customers OR partners
Show full SKILL.md (268 more words)Show less
Step 4: Deduplicate Results
Merge results by company name using fuzzy matching:
- Normalize: lowercase, strip suffixes (Inc, Corp, LLC, Ltd, Co., GmbH)
- Treat "Acme Inc" = "Acme" = "ACME Corp" = "acme.com" as the same company
- When merging, keep the highest confidence level and all evidence URLs
Step 5: Assign Confidence
Apply these rules:
High confidence:
- Logo on current website (from scrape_website_logos.py with confidence "high")
- Published case study or customer story
- Direct quote or testimonial on the company's site
- Official partnership page listing
Medium confidence:
- G2/Capterra review (reviewer's company)
- Press article mentioning customer relationship
- Job posting requiring experience with the product
- YouTube testimonial or video review
- Logo found only in Wayback Machine (was on site, now removed)
Low confidence:
- Single social media mention (tweet, Reddit post)
- Indirect reference ("heard good things about X")
- BuiltWith detection only (technology on site doesn't mean they're a paying customer)
- HN discussion mention
Step 6: Generate Report
Create two output files:
customer-discovery-[company]/report.md:
# Customer Discovery: [Company Name]
**Date:** YYYY-MM-DD
**Depth:** quick | standard | deep
**Total customers found:** N
## High Confidence (N)
| Customer | Source | Evidence |
|----------|--------|----------|
| Shopify | Case study | [link] |
| ... | ... | ... |
## Medium Confidence (N)
| Customer | Source | Evidence |
|----------|--------|----------|
| ... | ... | ... |
## Low Confidence (N)
| Customer | Source | Evidence |
|----------|--------|----------|
| ... | ... | ... |
## Sources Scanned
- Website logo wall: [url] — N customers found
- G2 reviews: N reviews analyzed — N companies identified
- Wayback Machine: N snapshots checked — N logos found (N removed)
- Web search: N queries — N mentions
- ...
## Methodology
This report was generated using the customer-discovery skill, which scans
public data sources to identify companies that use [Company Name]. Confidence
levels reflect the strength and directness of the evidence found.customer-discovery-[company]/customers.csv:
CSV with columns: company_name,confidence,source_type,evidence_url,notes
Write the CSV using a code block or Python script.
Scripts Reference
| Script | Purpose | Key flags |
|---|---|---|
scrape_website_logos.py | Extract logos from current website | --url, --output json|summary |
scrape_wayback_logos.py | Find historical logos via Wayback Machine | --url, --paths, --output json|summary |
search_builtwith.py | BuiltWith technology detection (deep mode) | --technology, --max-results, --output json|summary |
All scripts require requests: pip3 install requests
External skill scripts (use if available):
skills/capabilities/review-site-scraper/scripts/scrape_reviews.py— G2/Capterra/Trustpilot reviews (requires Apify token)skills/capabilities/linkedin-post-research/scripts/search_posts.py— LinkedIn post search (requires Apify token)
Cost
- Quick / Standard: Free (uses WebSearch + free APIs like Wayback Machine CDX)
- Deep: Mostly free. BuiltWith paid API is optional (
--api-keyflag); free scraping is used by default. - External skills (review-site-scraper, linkedin-post-research) may require paid API tokens.
© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts) in skills/research/capabilities/customer-discovery of gooseworks-ai/goose-skills.
- SKILL.md
- scripts/scrape_wayback_logos.py
- scripts/scrape_website_logos.py
- scripts/search_builtwith.py
- skill.meta.json
Open the folder on GitHubat commit 4bbe1ef
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 9, 2026.
Compare with similar skills
Customer Discovery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Customer Discovery this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| SEO Content Brief GeneratorAgriciDaniel/claude-seo | 19k | 2 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Getxapi ConnectLeoYeAI/openclaw-marketing-skills | 1k | 1 repos | ~715 | Automated safety check: Pass | Custom licence | |
| Strategic SEO PlanningAgriciDaniel/claude-seo | 19k | 5 repos | ~1.1k | Automated safety check: Pass | MIT | |
| X Twitter ConnectLeoYeAI/openclaw-marketing-skills | 1k | 1 repos | ~1.6k | Automated safety check: Pass | Custom licence | |
| Competitive Report Structureaffaan-m/ECC | 277k | 1 repos | ~2.1k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Customer Discovery
What does Customer Discovery do?
Discover all customers of a given company by scanning websites, case studies, review sites, press, social media, job postings, and more. Customer Discovery is an agent skill from gooseworks-ai/goose-skills. Discover all customers of a given company by scanning websites, case studies, review sites, press, social media, job postings, and more.
When should I use Customer Discovery?
Customer Discovery fits situations like: you need competitive intelligence on who a company sells to; tasks that involve Competitor analysis.
How do I install Customer Discovery in Claude Code?
Run `npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a claude-code`. Or copy the skill folder (skills/research/capabilities/customer-discovery in gooseworks-ai/goose-skills) into .claude/skills/customer-discovery in your project. Claude Code loads it when a task matches its description.
How do I install Customer Discovery in Codex?
Run `npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a codex`. Or copy the skill folder (skills/research/capabilities/customer-discovery in gooseworks-ai/goose-skills) into .agents/skills/customer-discovery in your project. Codex loads it when a task matches its description.
Can I use Customer Discovery in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill customer-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/customer-discovery, .gemini/skills/customer-discovery, .github/skills/customer-discovery and .opencode/skills/customer-discovery in your project.
What does Customer Discovery need to run?
Going by SKILL.md and its folder, Customer Discovery needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and pip3). Our summary lists: Python 3.
Does Customer Discovery access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Customer Discovery safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Customer Discovery use?
Customer Discovery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Customer Discovery use?
About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Customer Discovery?
Skills that share tags, products or a category with Customer Discovery: SEO Content Brief Generator (AgriciDaniel/claude-seo, 19k stars), Getxapi Connect (LeoYeAI/openclaw-marketing-skills, 1k stars), Strategic SEO Planning (AgriciDaniel/claude-seo, 19k stars) and X Twitter Connect (LeoYeAI/openclaw-marketing-skills, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Customer Discovery?
gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,242 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 10, 2026.
Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.