GEO Service Proposal Generator
zubair-trabzada/geo-seo-claude
Builds a client-ready AI-search-optimization proposal from an existing GEO audit, with pricing tiers, an ROI estimate and a markdown document ready to send.
Lookalike company API skill for Claude Code and Codex: find companies that resemble a seed account by reconstructing its firmographic, technographic, and industry profile, then surfacing similar…
Install Lookalike Accounts
$ npx skills add explorium-ai/gtm-skills --skill lookalike-accounts -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install explorium-ai/gtm-skills lookalike-accounts --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/explorium-ai/gtm-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/lookalike-accounts .claude/skills/lookalike-accounts && 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 "lookalike-accounts" agent skill from https://github.com/explorium-ai/gtm-skills/tree/main/skills/lookalike-accounts into .claude/skills/lookalike-accounts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lookalike-accounts", 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/explorium-ai/gtm-skills/tree/main/skills/lookalike-accountsType 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 explorium-ai/gtm-skills --skill lookalike-accounts -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install explorium-ai/gtm-skills lookalike-accounts --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/explorium-ai/gtm-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/lookalike-accounts .agents/skills/lookalike-accounts && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "lookalike-accounts" agent skill from https://github.com/explorium-ai/gtm-skills/tree/main/skills/lookalike-accounts into .agents/skills/lookalike-accounts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lookalike-accounts", 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 explorium-ai/gtm-skills --skill lookalike-accounts -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install explorium-ai/gtm-skills lookalike-accounts --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/explorium-ai/gtm-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/lookalike-accounts .cursor/skills/lookalike-accounts && 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 "lookalike-accounts" agent skill from https://github.com/explorium-ai/gtm-skills/tree/main/skills/lookalike-accounts into .cursor/skills/lookalike-accounts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lookalike-accounts", 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/explorium-ai/gtm-skills.git --path skills/lookalike-accounts--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 explorium-ai/gtm-skills --skill lookalike-accounts -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install explorium-ai/gtm-skills lookalike-accounts --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/explorium-ai/gtm-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/lookalike-accounts .gemini/skills/lookalike-accounts && 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 "lookalike-accounts" agent skill from https://github.com/explorium-ai/gtm-skills/tree/main/skills/lookalike-accounts into .gemini/skills/lookalike-accounts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lookalike-accounts", 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 explorium-ai/gtm-skills lookalike-accountsInstalls 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 explorium-ai/gtm-skills --skill lookalike-accounts -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/explorium-ai/gtm-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/lookalike-accounts .github/skills/lookalike-accounts && 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 "lookalike-accounts" agent skill from https://github.com/explorium-ai/gtm-skills/tree/main/skills/lookalike-accounts into .github/skills/lookalike-accounts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lookalike-accounts", 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 explorium-ai/gtm-skills --skill lookalike-accounts -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install explorium-ai/gtm-skills lookalike-accounts --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/explorium-ai/gtm-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/lookalike-accounts .opencode/skills/lookalike-accounts && 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 "lookalike-accounts" agent skill from https://github.com/explorium-ai/gtm-skills/tree/main/skills/lookalike-accounts into .opencode/skills/lookalike-accounts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lookalike-accounts", 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
lookalike-accounts- GitHub stars
- 185
- Token cost
- ~1.5k tokens
- SKILL.md length
- 777 words
- Files
- 1
- Skills in repo
- 17
- Repo updated
- First seen
- Licence
- MIT
At a glance
Lookalike company API skill for Claude Code and Codex: find companies that resemble a seed account by reconstructing its firmographic, technographic, and industry profile, then surfacing similar…
- Works in 7 steps: Resolve the seed. Match the supplied… → Domain-variant sanity check. After… → Reconstruct the seed profile. Enrich… → …
- Territory expansion
- SKILL.md covers Input, Workflow, Output Format and Limitations
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Lookalike Accounts is an agent skill from explorium-ai/gtm-skills. Lookalike company API skill for Claude Code and Codex: find companies that resemble a seed account by reconstructing its firmographic, technographic, and industry profile, then surfacing similar companies from Explorium's 150M+ business dataset. Use for territory expansion, TAM analysis, competitive mapping, account list extension, and finding audience twins. Triggers on 'find companies like', 'lookalike accounts', 'similar companies to', 'expand my territory', 'audience twins'. Works in Claude Code, Codex, and…
Its SKILL.md is about 1.5k 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 Sales & Support. The repository describes itself as: GTM Skills for Claude & Codex. The licence is MIT.
When your agent uses it
- Territory expansion
- Competitive mapping
- Account list extension
- Finding audience twins
Example prompts
- “find companies like”
- “lookalike accounts”
- “similar companies to”
- “/lookalike-accounts”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Resolve the seed. Match the supplied name or domain. If multiple candidates return, pick the highest-confidence match whose domain aligns…
- Domain-variant sanity check. After firmographics land, if a major-brand input resolves to a row with headcount 1-50 and NAICS 551114…
- Reconstruct the seed profile. Enrich firmographics and technographics on the seed. Extract
- Discover canonical values for the reconstructed industry and each tech token. Drop any value that does not resolve cleanly. Bucket enums…
- Apply user overrides. If the user specified country, size, revenue, public-only, or extra tech filters, replace or extend the…
- Size the candidate pool. Get a total count for the assembled filters. If below the requested return size, relax the most restrictive…
- Fetch lookalikes. Sample the validated filter set to surface candidates, then export the user-requested count via the paid export path for…
What it can do on your machine
Read from SKILL.md and the folder at commit f0efa6b. 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
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.
Network
No URLs in SKILL.md.
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
Lookalike Accounts loads about 1.5k tokens when it runs. Until then it costs about 137 tokens; SKILL.md has 777 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from explorium-ai/gtm-skills at commit f0efa6b, republished under its MIT licence (© explorium-ai). 777 words, ~1,498 tokens.
Download SKILL.mdSave it as
.claude/skills/lookalike-accounts/SKILL.md (or your agent's skills folder).- name
- lookalike-accounts
- description
- Lookalike company API skill for Claude Code and Codex: find companies that resemble a seed account by reconstructing its firmographic, technographic, and industry profile, then surfacing similar companies from Explorium's 150M+ business dataset. Use for territory expansion, TAM analysis, competitive mapping, account list extension, and finding audience twins. Triggers on 'find companies like', 'lookalike accounts', 'similar companies to', 'expand my territory', 'audience twins'. Works in Claude Code, Codex, and Hermes-Agent.
Lookalike Accounts
Reconstruct the profile of one seed company, then surface other companies that share its industry, size, region, and tech stack.
Input
The user supplies via $ARGUMENTS:
- A seed company name or domain (required).
- Optional: how many lookalikes to return (default 25, ceiling 100).
- Optional overrides: country focus (country codes or grouped regions, pick one), size or revenue bucket, public-only flag, additional named tech stack components.
If the user gives only a person or asks for similar contacts, redirect them to a prospecting flow. This skill returns accounts.
Workflow
Resolve the seed. Match the supplied name or domain. If multiple candidates return, pick the highest-confidence match whose domain aligns with the user input and confirm the chosen company back in the final output. If nothing resolves, stop and ask for a clearer identifier (domain preferred).
Domain-variant sanity check. After firmographics land, if a major-brand input resolves to a row with headcount 1-50 and NAICS
551114(Corporate Managing Offices) or SICHotels and motels, the match likely routed to a registered-agent shell. Re-try with the alternate domain or with the company name string. A broken seed produces garbage lookalikes.Reconstruct the seed profile. Enrich firmographics and technographics on the seed. Extract:
- Industry category (LinkedIn or NAICS, whichever is populated; the two are mutually exclusive as filters).
- Headcount bucket and revenue bucket.
- Country code (or regional grouping if the seed is multinational and the user asked for regional scope).
- Top 3-5 tech stack values, biased toward category-defining tools (CRM, MAP, data warehouse, primary cloud) over ubiquitous infrastructure (Analytics, jQuery). Skip any field that comes back empty: never fabricate a value.
Discover canonical values for the reconstructed industry and each tech token. Drop any value that does not resolve cleanly. Bucket enums and country codes do not need discovery.
Apply user overrides. If the user specified country, size, revenue, public-only, or extra tech filters, replace or extend the reconstructed values with the overrides. Honor the mutual exclusivity rule between LinkedIn and NAICS industry, and between country code and grouped region.
Size the candidate pool. Get a total count for the assembled filters. If below the requested return size, relax the most restrictive filter first: drop the 5th tech, then 4th, then 3rd, then collapse the headcount bucket to an adjacent one. Re-size after each step. If the count exceeds 5,000, tighten by adding back a tech filter or narrowing region. Report the final candidate count in the output.
Fetch lookalikes. Sample the validated filter set to surface candidates, then export the user-requested count via the paid export path for the full materialized list. Exclude the seed before display.
Output Format
Header
Lookalikes for [Seed Company Name] ([seed domain]) Reconstructed profile: [industry] | [headcount bucket] | [revenue bucket] | [country or region] | Tech: [comma-separated tech list]. Candidate pool: [N] companies matching this profile. Returning top [requested count].
Show full SKILL.md (299 more words)Show less
Results Table
| Rank | Company | Domain | Industry | Employees | Revenue | Country |
|---|
Show the user-requested count (default 25, cap 100). Always exclude the seed.
Pattern Notes
2-4 bullets covering dominant geography, size concentration, tech-overlap density (tech filters are not fully populated on every row), and any adjacency the filter set introduced (e.g. "industry widened because LinkedIn category was empty on seed").
Suggested Next Steps
Pull contacts at any of these accounts via the list-builder flow scoped by the businesses table; deepen one row with the enrich-company flow; watch the list for growth events on the reconstructed cohort.
Limitations
- No native similarity model. There is no lookalike API or ML similarity score. This skill approximates similarity by reconstructing the seed's firmographics, technographics, and industry, then doing a filtered company fetch on those reconstructed attributes. The native flow is match-seed, enrich attributes, then re-fetch with attributes. Results are ranked by default fetch order, NOT by a true similarity score.
- No similarity ranking column. No per-row similarity score is computed: rank reflects fetch order.
- Industry classification gaps. If neither LinkedIn nor NAICS category is populated, the skill falls back to size + region + tech, producing a looser approximation. Flag this in the output.
- Tech stack coverage is partial. Tech tokens are not exhaustively populated, so filtering on multiple values can collapse the candidate pool faster than expected. The relaxation logic in step 6 handles this.
- Bucketed size and revenue. A seed at the high edge of one bucket and a candidate at the low edge of the next can look further apart than they are.
- Region taxonomy is country-level. No metro or sub-country scope.
- High-profile execs at these accounts often have suppressed contact data: if the user pivots to "who runs these", recommend LinkedIn outreach for top execs.
- No contact-level lookalikes. This skill returns accounts only.
© explorium-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
Just SKILL.md in skills/lookalike-accounts of explorium-ai/gtm-skills.
Open the folder on GitHubat commit f0efa6b
Compare with similar skills
Lookalike Accounts 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 |
|---|---|---|---|---|---|---|
| Lookalike Accounts this skillexplorium-ai/gtm-skills | 185 | — | ~1.5k | Automated safety check: Pass | MIT | |
| GEO Service Proposal Generatorzubair-trabzada/geo-seo-claude | 11k | — | ~3k | Automated safety check: Notes | MIT | |
| SEO EcommerceAgriciDaniel/codex-seo | 799 | 2 repos | ~3k | Automated safety check: Pass | MIT | |
| GEO Prospect Trackerzubair-trabzada/geo-seo-claude | 11k | — | ~1.7k | Automated safety check: Notes | MIT | |
| Editing Site ContentComfy-Org/workflow_templates | 1.3k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Icp Onboardinggrowthenginenowoslawski/coldoutboundskills | 753 | — | ~1.9k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Lookalike Accounts
What does Lookalike Accounts do?
Lookalike company API skill for Claude Code and Codex: find companies that resemble a seed account by reconstructing its firmographic, technographic, and industry profile, then surfacing similar…. Lookalike Accounts is an agent skill from explorium-ai/gtm-skills. Lookalike company API skill for Claude Code and Codex: find companies that resemble a seed account by reconstructing its firmographic, technographic, and industry profile, then surfacing similar companies from Explorium's 150M+ business dataset.
When should I use Lookalike Accounts?
Lookalike Accounts fits situations like: territory expansion; competitive mapping; account list extension; finding audience twins.
How do I install Lookalike Accounts in Claude Code?
Run `npx skills add explorium-ai/gtm-skills --skill lookalike-accounts -a claude-code`. Or copy the skill folder (skills/lookalike-accounts in explorium-ai/gtm-skills) into .claude/skills/lookalike-accounts in your project. Claude Code loads it when a task matches its description.
How do I install Lookalike Accounts in Codex?
Run `npx skills add explorium-ai/gtm-skills --skill lookalike-accounts -a codex`. Or copy the skill folder (skills/lookalike-accounts in explorium-ai/gtm-skills) into .agents/skills/lookalike-accounts in your project. Codex loads it when a task matches its description.
Can I use Lookalike Accounts 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 explorium-ai/gtm-skills --skill lookalike-accounts -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lookalike-accounts, .gemini/skills/lookalike-accounts, .github/skills/lookalike-accounts and .opencode/skills/lookalike-accounts in your project.
What does Lookalike Accounts need to run?
SKILL.md names no scripts, command-line tools or credentials: Lookalike Accounts is instructions for the agent only.
Does Lookalike Accounts 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 Lookalike Accounts 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. Review the folder before installing.
What licence does Lookalike Accounts use?
Lookalike Accounts 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 Lookalike Accounts use?
About 1.5k tokens (SKILL.md is roughly 6k 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 Lookalike Accounts?
Skills that share tags, products or a category with Lookalike Accounts: GEO Service Proposal Generator (zubair-trabzada/geo-seo-claude, 11k stars), SEO Ecommerce (AgriciDaniel/codex-seo, 799 stars), GEO Prospect Tracker (zubair-trabzada/geo-seo-claude, 11k stars) and Editing Site Content (Comfy-Org/workflow_templates, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Lookalike Accounts?
explorium-ai (a GitHub organization) maintains it in explorium-ai/gtm-skills, which has 185 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 8, 2026.
Source: explorium-ai/gtm-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.