Codex First Customer Finder Skill is a Codex skill that researches public signals to identify likely early customers for a startup or product. It defines customer profiles, evaluates prospect fit and timing, cites supporting sources, and produces a report with manual outreach suggestions. Its catalogue add-ons provide the skill’s workflow for coding agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Kappaemme-git/codex-first-customer-finder-skill --skill first-customer-findergit clone --depth 1 https://github.com/Kappaemme-git/codex-first-customer-finder-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kappaemme-git/codex-first-customer-finder-skill/first-customer-finder)<a href="https://agentmods.dev/skills/kappaemme-git/codex-first-customer-finder-skill/first-customer-finder"><img src="https://agentmods.dev/badge/skills/kappaemme-git/codex-first-customer-finder-skill/first-customer-finder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kappaemme-git/codex-first-customer-finder-skill/first-customer-finder"><img src="https://agentmods.dev/badge/skills/kappaemme-git/codex-first-customer-finder-skill/first-customer-finder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.01810 |
| Opus 5.5 | $0.00024 | $0.00724 |
| Sonnet 5.5 | $0.00012 | $0.00362 |
| Haiku 4.5 | $0.00006 | $0.00181 |
Grade A, and why
first-customer-finder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- customer-finder — 97% identical, 43 lines differ
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
First 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 and references/report-artifact.md before creating the final report. For saved history, feedback, or a repeat search, also read references/feedback-and-history.md.
Use English by default; follow an explicit language request. Keep the chat handoff short and put the useful detail in the report. Research requires available web search/browser tools; the bundled Python helpers only validate, export, and manage local files. If live browsing is unavailable, report that limitation instead of inventing leads.
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.
- Use one stable product-specific workspace folder. If its history exists, inspect the saved preferences and excluded prospects before searching. Never load another product's history just because it is nearby. State briefly where history will be saved; respect requests for no saved history.
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
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 295 B
- references/feedback-and-history.md 4.8 KB
- references/report-artifact.md 7.1 KB
- references/research-framework.md 5.6 KB
- scripts/generate_report.py 21 KB runs code
- scripts/prospect_data.py 14 KB runs code
- scripts/prospect_history.py 2.7 KB runs code
- scripts/report_exports.py 7.4 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +13 lines · -34 tokens per session b1624b78adc5
- 1mo ago First seen · 102 lines · 95 tokens per session scan A ae8a2fcd1368
first-customer-finder is a skill published in the GitHub repository Kappaemme-git/codex-first-customer-finder-skill (1,124 stars, last pushed 1mo ago), licensed MIT. It adds 61 tokens to every session and 1,810 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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