Offer Definer
Othmane-Khadri/YALC-the-GTM-operating-system
Transform product descriptions and feature lists into compelling, outcome-focused offers for cold outreach.
Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals.
Install Customer Finder
$ npx skills add buildfastwithai/gen-ai-experiments --skill customer-finder -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments customer-finder --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/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/customer-finder-skill/customer-finder .claude/skills/customer-finder && 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-finder" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finder into .claude/skills/customer-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-finder", 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/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finderType 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 buildfastwithai/gen-ai-experiments --skill customer-finder -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments customer-finder --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/customer-finder-skill/customer-finder .agents/skills/customer-finder && 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-finder" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finder into .agents/skills/customer-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-finder", 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 buildfastwithai/gen-ai-experiments --skill customer-finder -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments customer-finder --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/customer-finder-skill/customer-finder .cursor/skills/customer-finder && 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-finder" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finder into .cursor/skills/customer-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-finder", 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/buildfastwithai/gen-ai-experiments.git --path skills/customer-finder-skill/customer-finder--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 buildfastwithai/gen-ai-experiments --skill customer-finder -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments customer-finder --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/customer-finder-skill/customer-finder .gemini/skills/customer-finder && 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-finder" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finder into .gemini/skills/customer-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-finder", 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 buildfastwithai/gen-ai-experiments customer-finderInstalls 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 buildfastwithai/gen-ai-experiments --skill customer-finder -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/customer-finder-skill/customer-finder .github/skills/customer-finder && 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-finder" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finder into .github/skills/customer-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-finder", 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 buildfastwithai/gen-ai-experiments --skill customer-finder -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install buildfastwithai/gen-ai-experiments customer-finder --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/buildfastwithai/gen-ai-experiments.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/customer-finder-skill/customer-finder .opencode/skills/customer-finder && 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-finder" agent skill from https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finder into .opencode/skills/customer-finder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "customer-finder", 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-finder- GitHub stars
- 785
- Token cost
- ~1.4k tokens
- SKILL.md length
- 671 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 13
- Repo updated
- First seen
- Licence
- MIT
At a glance
Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals.
- Works in 6 steps: Understand the product → Build a public-signal search plan → Research safely → …
- Codex needs to analyze a product URL
- SKILL.md covers Workflow, Modes and Quality bar
- Runs Python scripts from its folder
What it does
Customer Finder is an agent skill from buildfastwithai/gen-ai-experiments. Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals. Use when Codex needs to analyze a product URL or idea, define an ideal customer profile, research public discussions and business pages, identify first-user prospects, rank lead fit and timing, prepare source-based outreach drafts, or create a shareable early-customer prospecting report without sending messages automatically.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/report-artifact.md` and `references/research-framework.md`).
It sits in Sales & Support, covering Positioning and messaging and Cold outreach. The repository describes itself as: Collection of Jupyter notebooks is designed to provide you with a comprehensive guide to various AI tools and technologies. The licence is MIT.
When your agent uses it
- Codex needs to analyze a product URL
- Define an ideal customer profile
- Research public discussions and business pages
- Identify first-user prospects
Example prompts
- “/customer-finder”
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 7b62043. 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 1 file in scripts/ (Python), which the agent can run.
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
Customer Finder loads about 1.4k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 127 tokens; SKILL.md has 671 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 buildfastwithai/gen-ai-experiments at commit 7b62043, republished under its MIT licence (© buildfastwithai). 671 words, ~1,398 tokens.
Download SKILL.mdSave it as
.claude/skills/customer-finder/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- customer-finder
- description
- Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals. Use when Codex needs to analyze a product URL or idea, define an ideal customer profile, research public discussions and business pages, identify first-user prospects, rank lead fit and timing, prepare source-based outreach drafts, or create a shareable early-customer prospecting report without sending messages automatically.
Customer Finder
Turn a startup URL or product description into a short, evidence-backed list of plausible first customers. Use public signals, preserve privacy, and distinguish a prospect from a confirmed buyer.
Read references/research-framework.md before researching or scoring prospects. Read references/report-artifact.md before creating the final report.
Workflow
1. Understand the product
- Inspect the supplied URL, repository, landing-page copy, or product description.
- Identify the product, outcome, buyer, user, price or buying motion, geography, and strongest use case.
- Define one primary ICP, one adjacent ICP, pain triggers, positive signals, and disqualifiers.
- Infer missing context when safe and label the inference. Ask one concise question only when ambiguity would materially change the search.
2. Build a public-signal search plan
Search current public sources for:
- explicit tool or alternative requests
- first-person descriptions of the target problem
- manual workflows and repeated workaround complaints
- migration, churn, or competitor-frustration signals
- public company changes that create timing, such as hiring, launching, expanding, or adopting a relevant workflow
Use multiple query angles and source types. Prefer original pages over search snippets. Record the source URL, source type, publication date when visible, and the exact evidence supporting qualification.
3. Research safely
- Use public, intentionally shared professional or business information only.
- Do not bypass login walls, paywalls, access controls, rate limits, or robots restrictions.
- Do not use data brokers, leaked datasets, private groups, personal email discovery, phone enrichment, or sensitive personal information.
- Do not infer protected traits or target people using health, financial hardship, political belief, sexuality, religion, or other sensitive attributes.
- Prefer companies, public professional profiles, public requests, and community posts relevant to the product.
- Quote minimally and paraphrase by default. Link every material pain or timing signal.
4. Qualify and deduplicate
Score each prospect using the bundled framework:
- pain strength
- product fit
- timing
- public reachability
- evidence quality
Remove duplicates and weak matches. A prospect without a cited pain, need, or timing signal is only a speculative fit and must not appear in the primary shortlist.
Never claim that a prospect is interested, has consented, or will buy. Label the output “potential customer based on public signals.”
5. Draft outreach, never send it
- Recommend the most natural public or professional channel already associated with the source.
- Write one short opener grounded only in the cited public context.
- Avoid pretending to know the person, overstating familiarity, or mentioning unrelated personal details.
- Do not send messages, submit forms, connect, follow, comment, or create CRM records unless the user separately requests and authorizes that action.
Show full SKILL.md (260 more words)Show less
6. Produce the report
Lead with the most actionable evidence. Use this order:
- Verdict — whether the startup has reachable early-customer signals.
- ICP — buyer, job, trigger, and disqualifiers.
- Top prospect — strongest evidence-backed candidate and why now.
- Prospect shortlist — source, pain signal, fit score, stage, why now, channel, and opener.
- Repeated patterns — pains and triggers appearing across prospects.
- Seven-day outreach plan — a manual, low-volume validation sequence.
- Limits — missing evidence and what must be confirmed through real conversations.
Create a standalone HTML report unless the user explicitly requests chat-only output:
- Write structured JSON using
references/report-artifact.md. - Run
scripts/generate_report.py <analysis.json> <report.html>. - Save the report in the workspace
outputs/directory. - Verify prospect cards, source links, scores, patterns, outreach plan, and limitations.
- Return a clickable absolute file link in the final response so it opens from Codex.
Modes
- quick: Find and qualify up to five strong prospects.
- standard: Find up to ten prospects across several public source types.
- deep: Research up to twenty prospects and map repeated pain patterns.
- design-partners: Prioritize users willing to test and give feedback over immediate buyers.
- b2b: Prioritize companies, public business triggers, and relevant decision roles.
- community: Prioritize public discussion and explicit request signals.
Use standard by default.
Quality bar
- Link every prospect to at least one meaningful public signal.
- Prefer ten strong matches over a long generic lead list.
- Make uncertainty and stale evidence visible.
- Personalize from the source, not from invented assumptions.
- Keep outreach manual and respectful.
- Treat the shortlist as a research hypothesis, not a customer database.
© buildfastwithai, 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, references) in skills/customer-finder-skill/customer-finder of buildfastwithai/gen-ai-experiments.
- SKILL.md
- agents/openai.yaml
- references/report-artifact.md
- references/research-framework.md
- scripts/generate_report.py
Open the folder on GitHubat commit 7b62043
Compare with similar skills
Customer Finder 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 Finder this skillbuildfastwithai/gen-ai-experiments | 785 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Offer DefinerOthmane-Khadri/YALC-the-GTM-operating-system | 318 | — | ~1k | Automated safety check: Pass | MIT | |
| Atomic Messageextruct-ai/gtm-skills | 109 | — | ~952 | Automated safety check: Pass | None | |
| Icp Onboardinggrowthenginenowoslawski/coldoutboundskills | 753 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Messaging Ab Testergooseworks-ai/goose-skills | 1.2k | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Customer Segment Slicinghashgraph-online/awesome-codex-plugins | 1.3k | — | ~807 | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Customer Finder
What does Customer Finder do?
Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals. Customer Finder is an agent skill from buildfastwithai/gen-ai-experiments. Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals.
When should I use Customer Finder?
Customer Finder fits situations like: Codex needs to analyze a product URL; define an ideal customer profile; research public discussions and business pages; identify first-user prospects.
How do I install Customer Finder in Claude Code?
Run `npx skills add buildfastwithai/gen-ai-experiments --skill customer-finder -a claude-code`. Or copy the skill folder (skills/customer-finder-skill/customer-finder in buildfastwithai/gen-ai-experiments) into .claude/skills/customer-finder in your project. Claude Code loads it when a task matches its description.
How do I install Customer Finder in Codex?
Run `npx skills add buildfastwithai/gen-ai-experiments --skill customer-finder -a codex`. Or copy the skill folder (skills/customer-finder-skill/customer-finder in buildfastwithai/gen-ai-experiments) into .agents/skills/customer-finder in your project. Codex loads it when a task matches its description.
Can I use Customer Finder 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 buildfastwithai/gen-ai-experiments --skill customer-finder -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-finder, .gemini/skills/customer-finder, .github/skills/customer-finder and .opencode/skills/customer-finder in your project.
What does Customer Finder need to run?
Going by SKILL.md and its folder, Customer Finder needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Customer Finder 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 Finder 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 Finder use?
Customer Finder 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 Finder use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.
What are the alternatives to Customer Finder?
Skills that share tags, products or a category with Customer Finder: Offer Definer (Othmane-Khadri/YALC-the-GTM-operating-system, 318 stars), Atomic Message (extruct-ai/gtm-skills, 109 stars), Icp Onboarding (growthenginenowoslawski/coldoutboundskills, 753 stars) and Messaging Ab Tester (gooseworks-ai/goose-skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Customer Finder?
buildfastwithai (a GitHub organization) maintains it in buildfastwithai/gen-ai-experiments, which has 785 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on September 22, 2026.
Source: buildfastwithai/gen-ai-experiments on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.