Bright Data MCP
brightdata/skills
Bright Data MCP handles ALL web data operations. An agent skill from brightdata/skills.
Builds personalized demo assets for top prospects using the founder's product API/MCP/SDK.
$ npx skills add gooseworks-ai/goose-skills --skill demo-builder -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills demo-builder --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/lead-generation/packs/lead-gen-devtools/demo-builder .claude/skills/demo-builder && 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 "demo-builder" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/packs/lead-gen-devtools/demo-builder into .claude/skills/demo-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demo-builder", 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/lead-generation/packs/lead-gen-devtools/demo-builderType 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 demo-builder -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills demo-builder --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/lead-generation/packs/lead-gen-devtools/demo-builder .agents/skills/demo-builder && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "demo-builder" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/packs/lead-gen-devtools/demo-builder into .agents/skills/demo-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demo-builder", 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 demo-builder -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills demo-builder --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/lead-generation/packs/lead-gen-devtools/demo-builder .cursor/skills/demo-builder && 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 "demo-builder" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/packs/lead-gen-devtools/demo-builder into .cursor/skills/demo-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demo-builder", 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/lead-generation/packs/lead-gen-devtools/demo-builder--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 demo-builder -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills demo-builder --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/lead-generation/packs/lead-gen-devtools/demo-builder .gemini/skills/demo-builder && 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 "demo-builder" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/packs/lead-gen-devtools/demo-builder into .gemini/skills/demo-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demo-builder", 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 demo-builderInstalls 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 demo-builder -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/lead-generation/packs/lead-gen-devtools/demo-builder .github/skills/demo-builder && 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 "demo-builder" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/packs/lead-gen-devtools/demo-builder into .github/skills/demo-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demo-builder", 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 demo-builder -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 demo-builder --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/lead-generation/packs/lead-gen-devtools/demo-builder .opencode/skills/demo-builder && 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 "demo-builder" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/lead-generation/packs/lead-gen-devtools/demo-builder into .opencode/skills/demo-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "demo-builder", 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.
demo-builderBuilds personalized demo assets for top prospects using the founder's product API/MCP/SDK.
Demo Builder is an agent skill from gooseworks-ai/goose-skills. Builds personalized demo assets for top prospects using the founder's product API/MCP/SDK. Researches prospect, proposes demo concepts, builds working prototype, tests it, and generates comparison report with live demo link.
Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Marketing & SEO, covering Lead generation and MCP servers. It works with Model Context Protocol. 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.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
BashReadWriteEditGrepGlobWebFetchWebSearchFrom allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Demo Builder loads about 5k tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 2,513 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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Bash, Read, Write, Edit, Grep, Glob, WebFetch, WebSearchAutomated 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); files beside SKILL.md are not scanned.
The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 2,513 words, ~5,031 tokens.
.claude/skills/demo-builder/SKILL.md (or your agent's skills folder).Build personalized demo assets for prospects using the founder's product API/MCP/SDK. Send a working prototype that solves the prospect's actual problem — with a comparison report and live demo link.
This skill supports two input paths:
If the user provides a company name, website URL, or prospect details:
Ask the user:
"I'll build a working demo for [Company]. Before I start, I need to know:
- What does your product do? (one-liner)
- What problem does it solve for companies like [Company]?
- Where are your API docs / SDK / MCP tools?"
If the user has already provided product context (from lead-discovery or prior conversation), skip questions they've already answered.
If the user has csv outputs from signal skills, help them pick the best prospect:
Only build for ONE prospect initially — this is a trial run to validate the approach before scaling.
Research the selected prospect company thoroughly. Use web search, their website, and any data from the signal outputs.
Gather:
Key sources to check:
Present a brief to the user:
"Here's what I found about [Company]. Based on this, here are my ideas for what we could build."
Based on the intersection of (a) what the user's product does and (b) what the prospect needs, propose 2-3 demo concepts.
Framework for generating ideas:
Ask yourself: "If I were a solutions engineer at [user's company] preparing for a meeting with [prospect], what would I build to show them the product solves their specific problem?"
The demo should:
Common demo patterns by product type:
| Product Type | Demo Pattern | Example |
|---|---|---|
| Voice/Communication API | Build an AI agent for the prospect's use case | Appointment scheduler for a healthcare company |
| Observability tool | Set up monitoring on a sample app mimicking the prospect's stack | Dashboard showing the metrics they'd care about |
| API platform | Build a small integration the prospect would actually need | Connect two tools they use and show data flowing |
| Developer tool / SDK | Build a mini-app using the prospect's domain | A prototype feature their customers would use |
| Infrastructure / DevOps | Configure a deployment pipeline for their stack | Working CI/CD or infra-as-code for their tech |
| Data / Analytics | Build a dashboard or pipeline using their public data | Analysis of their public metrics, app store data, etc. |
| Security tool | Run a scan or audit on their public-facing assets | Security report on their website or API |
| AI/ML platform | Build a model or agent for their domain | Trained on their public content, solving their problem |
Present to the user:
DEMO CONCEPT 1: [Name]
What it does: [2-3 sentences]
Why it resonates: [connects to the signal that surfaced this prospect]
Prospect's reaction: [what they'll think when they see this]
Complexity: [LOW / MEDIUM / HIGH]
Build time: [rough estimate]
DEMO CONCEPT 2: [Name]
...
DEMO CONCEPT 3: [Name]
...Ask:
"Which concept should I build? I'll use your [API/MCP/SDK] to create a working version. Once it's ready, I'll test it and generate a comparison report you can send to [prospect contact name]."
Once the user picks a concept, confirm the scope:
"I'll build [concept]. To do this, I need:
- Access to your API docs at [URL] (or confirm MCP tools are available)
- Any API keys or credentials I should use
- Any constraints — features to highlight, competitors to compare against, branding preferences
Anything else I should know before I start building?"
Before writing any code, thoroughly understand the user's product capabilities.
If the product has API docs:
If the product has MCP tools:
If the product has an SDK:
Important: Do NOT guess at API behavior. Read the docs. If docs are unclear, ask the user. A broken demo is worse than no demo.
Build the working demo. The implementation depends entirely on the product type and demo concept.
General principles:
Save all demo artifacts to .tmp/demos/[prospect-company-name]/
After building, test the demo yourself to verify it works.
Test checklist:
If the product supports call/interaction recording:
Document the test results:
TEST RESULTS
Primary use case: PASS/FAIL — [details]
Edge case 1 ([describe]): PASS/FAIL — [details]
Edge case 2 ([describe]): PASS/FAIL — [details]
Interactive element: WORKING/BROKEN — [details]
Recording available: YES/NO — [path or link]Fix any failures before proceeding. If a critical feature is broken and unfixable, discuss with the user before continuing.
The demo alone is strong. The demo PLUS a comparison report that positions the user's product against alternatives is a complete sales package.
Research 3-5 competitors relevant to the prospect's use case:
For each competitor, gather:
Important: Be factual. Pull from public documentation, pricing pages, and published reviews. Do not fabricate competitor weaknesses. The report should be defensible if the prospect checks the claims.
From the test in Step 9, gather quantitative metrics to include in the report:
Common metrics by product type:
These real numbers from your actual test are what make the report credible. "We measured 1.65s average latency" beats "low latency" every time.
Generate a polished HTML report. The report is the deliverable the user sends to the prospect.
Report structure:
1. HEADER
- Title: "[Product Category] for [Prospect's Use Case]"
- Subtitle: "Technical Comparison Report"
- Prepared for: [Prospect Company] Engineering / [Prospect Contact Name]
- Date
2. KEY METRICS (4 big numbers)
- Pick the 4 most impressive metrics from your test
- These should be the first thing the prospect sees
3. LIVE TEST RESULTS
- What you built: 2-3 sentence description using the prospect's terminology
- Test results grid: PASS/FAIL for each scenario tested
- If applicable: latency distribution, quality metrics
- If applicable: sample transcript or interaction log
4. COMPARISON MATRIX
- Feature-by-feature table: user's product vs. 3-5 competitors
- Focus on features relevant to THIS prospect (not a generic feature list)
- Use clear visual indicators: YES/NO/PARTIAL with color coding
5. PLATFORM DEEP DIVES (optional, if report needs more substance)
- 2-3 sentence summary of each platform
- Best-for statement for each
6. INDUSTRY-SPECIFIC REQUIREMENTS (if applicable)
- Compliance table (HIPAA, SOC 2, etc.)
- Industry-specific features
7. RECOMMENDATION
- Clear statement: "For [prospect]'s [use case], [product] is the strongest fit"
- 3 numbered reasons, tied to the prospect's specific needs
- Cost projection if data is available
8. CTA
- Link to the live demo
- "Try it yourself" messaging
- Contact info or next stepDesign principles for the HTML:
The report needs a working link where the prospect can try the demo.
Depending on the product type:
If the user's product has a sharing/demo feature (like VAPI's share links), use it. If not, discuss with the user how to make the demo accessible.
Update the CTA section of the report with the correct demo link.
Show the user everything that was created:
DEMO PACKAGE FOR [PROSPECT COMPANY]
====================================
1. Working Demo
- [Description of what was built]
- Demo link: [URL]
- Test recording: [path, if available]
2. Comparison Report
- Report file: [path to HTML]
- Key metrics highlighted: [list the 4 headline numbers]
- Competitors compared: [list]
3. Outreach Context
- Prospect contact: [name, role, email]
- Signal that surfaced them: [the original signal]
- Suggested angle: [1-2 sentences on how to frame the outreach]Draft a short outreach message the user can send to the prospect. Tailor it based on the signal that surfaced this prospect.
Outreach message framework:
Subject: [Something specific to their situation, not generic]
[First name],
[One sentence connecting to the signal — how you know about their need.
Do NOT say "I found you through GitHub scraping." Instead, reference
the underlying need: their job posting, their open-source usage,
their forum post, their conference talk.]
[One sentence about what you built for them.]
[Link to the report or demo.]
[One sentence CTA — specific and low-friction.]
[Signature]Signal-specific angles:
| Signal Source | Outreach Angle |
|---|---|
| GitHub repo signals | "I saw your team is building with [technology]. We built [demo] that shows how [product] handles [their use case]." |
| Job signals (competitor mentioned) | "I noticed you're hiring for [role] with [competitor] experience. We put together a comparison showing how [product] compares for [their use case]." |
| Job signals (build vs buy) | "I saw you're building [capability] in-house. Before your team invests the engineering time, here's a working version we built in 60 seconds using [product]." |
| Community signals (pain) | "I came across your post about [problem]. We built a working solution for [their company] on [product] — here's a demo you can try." |
| Competitor signals (switching) | "I noticed you're evaluating alternatives to [competitor]. We put together a hands-on comparison for [their specific use case]." |
| Event signals (speaker) | "Loved your talk on [topic] at [event]. We built a [demo] that solves the [problem] you mentioned — would love your take on it." |
Important: The outreach message is a draft. Tell the user to personalize it further. The agent should NOT send anything on behalf of the user without explicit permission.
After the trial run is complete and the user has reviewed the package:
"This was a trial run for one prospect. Here's how to scale this:
- Pick your next 5-10 prospects from the lead list — I can build demo packages for each
- Templatize what worked — the report structure and demo pattern can be reused with prospect-specific customization
- Track responses — which prospects opened the report, tried the demo, replied
Want me to build the next one?"
The demo must be real. A working prototype on the user's actual platform, not a mockup, screenshot, or slide deck. The whole point is proving the product works for the prospect's use case.
Use the prospect's context everywhere. Their company name, their industry terms, their specific workflows, their compliance requirements. Generic demos don't close deals.
Factual competitor comparisons only. Every claim in the comparison report should be verifiable from public documentation. If you're unsure about a competitor's capability, say "unconfirmed" rather than guessing.
Get user approval before building. Present the concept, confirm the scope, then build. Don't spend 30 minutes building something the user didn't want.
One prospect at a time to start. The first demo is a trial run. Get the user's feedback, refine the approach, then scale to more prospects.
Never send outreach without permission. Draft messages, don't send them. The user decides when and how to reach out.
Adapt to the product type. This skill works with any product that has API/MCP/SDK access. The demo patterns, metrics, and report structure should adapt to what the product actually does — not force every product into the same template.
Cost awareness. If building the demo costs money (API usage, compute, etc.), estimate the cost and confirm with the user before proceeding. A demo that costs $0.50 to build is fine. A demo that costs $50 needs a conversation first.
© 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
Just SKILL.md in skills/lead-generation/packs/lead-gen-devtools/demo-builder of gooseworks-ai/goose-skills.
Open the folder on GitHubat commit c650c6d
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 7, 2026.
Demo Builder 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 |
|---|---|---|---|---|---|---|
| Demo Builder this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~5k | Automated safety check: Notes | MIT | |
| Bright Data MCPbrightdata/skills | 264 | 1 repos | ~3.7k | Automated safety check: Pass | MIT | |
| Commodities ListOctagonAI/skills | 127 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Google Mapscablate/mcp-google-map | 469 | — | ~909 | Automated safety check: Pass | MIT | |
| Reddit InsightsBrianRWagner/ai-marketing-claude-code-skills | 441 | 1 repos | ~3.1k | Automated safety check: Pass | None | |
| CanonryCanonry/canonry | 171 | — | ~3.9k | Automated safety check: Pass | MIT |
brightdata/skills
Bright Data MCP handles ALL web data operations. An agent skill from brightdata/skills.
OctagonAI/skills
Retrieve the full catalog of tradable commodities across energy, metals, and agriculture using Octagon MCP.
cablate/mcp-google-map
Search places, resolve addresses, compare routes, inspect neighborhoods, and retrieve geographic or environmental facts through the standalone @cablate/mcp-google-map CLI.
BrianRWagner/ai-marketing-claude-code-skills
Search and analyze Reddit content using semantic AI search via reddit-insights.com MCP server.
Canonry/canonry
Navigate Canonry through connected MCP tools or the cnry CLI to inspect evidence, diagnose changes, plan measurement, review integrations, and report results.
seranking/seo-skills
SE Ranking API integration architect. An agent skill from seranking/seo-skills.
gooseworks-ai/goose-skills
Scrape and search Reddit posts using Apify. An agent skill from gooseworks-ai/goose-skills.
gooseworks-ai/goose-skills
Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent.
gooseworks-ai/goose-skills
Replace an existing video's opening with a supplied clip or free kinetic text hook while retaining and verifying every original body frame, audio, captions and ending.
gooseworks-ai/goose-skills
Scrape blog posts via RSS feeds (free, no API key) with Apify fallback for JS-heavy sites.
gooseworks-ai/goose-skills
Find leads by scraping engagers from a competitor's top LinkedIn posts.
gooseworks-ai/goose-skills
Assemble a ChatGPT chat-reveal video ad from a thread + timeline JSON — one continuous Playwright recording of a ChatGPT mobile chat (user types with the iOS keyboard up → taps send → keyboard…
Works with
Categories
Builds personalized demo assets for top prospects using the founder's product API/MCP/SDK. Demo Builder is an agent skill from gooseworks-ai/goose-skills. Builds personalized demo assets for top prospects using the founder's product API/MCP/SDK.
Demo Builder fits situations like: tasks that involve Lead generation; tasks that involve MCP servers.
Run `npx skills add gooseworks-ai/goose-skills --skill demo-builder -a claude-code`. Or copy the skill folder (skills/lead-generation/packs/lead-gen-devtools/demo-builder in gooseworks-ai/goose-skills) into .claude/skills/demo-builder in your project. Claude Code loads it when a task matches its description.
Run `npx skills add gooseworks-ai/goose-skills --skill demo-builder -a codex`. Or copy the skill folder (skills/lead-generation/packs/lead-gen-devtools/demo-builder in gooseworks-ai/goose-skills) into .agents/skills/demo-builder in your project. Codex loads it when a task matches its description.
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 demo-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/demo-builder, .gemini/skills/demo-builder, .github/skills/demo-builder and .opencode/skills/demo-builder in your project.
SKILL.md names no scripts, command-line tools or credentials: Demo Builder is instructions for the agent only. Its frontmatter pre-approves these tools: Bash, Read, Write, Edit, Grep, Glob, WebFetch, WebSearch.
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.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Demo Builder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Demo Builder: Bright Data MCP (brightdata/skills, 264 stars), Commodities List (OctagonAI/skills, 127 stars), Google Maps (cablate/mcp-google-map, 469 stars) and Reddit Insights (BrianRWagner/ai-marketing-claude-code-skills, 441 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 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.