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Find first customers

Skill Abdullah4AI/customer-signal-scout/skill/find-first-customers

Find and qualify a startup's first customers from current public pain, intent, hiring, migration, and tool-switching signals. Use when Codex is asked to discover early adopters, build a prospect shortlist, research evidence-backed sales leads, score customer fit and timing, or create personalized founder outreach from a startup URL or product description.From its SKILL.md

Install
npx -y skills add Abdullah4AI/customer-signal-scout --skill find-first-customers

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.5 KB, 640 tokens by cl100k_base, as published. Nobody here has run it

Find First Customers

Turn a startup URL or product description into an evidence-backed prospect report. Never invent prospects, quotes, contact details, or source URLs.

Inputs

Collect or infer:

  • startup URL or product description
  • target geography, if constrained
  • B2B, B2C, developer, or local-business motion
  • maximum prospect count, default 20
  • freshness window, default 90 days

Ask only when a missing constraint would materially change the search. Otherwise state the assumption and proceed.

Workflow

  1. Inspect the product and derive an Ideal Customer Profile with pains, triggers, exclusions, likely roles, and search vocabulary.
  2. Read references/research-playbook.md. Build queries across at least three relevant public-source families.
  3. Search current public discussions. Capture the exact source URL, date, author or organization, a short evidence excerpt, and the signal type.
  4. Read references/evidence-schema.md. Store candidates in its JSON shape. Use null for unknown fields; never guess an email or identity.
  5. Read references/scoring.md. Score fit, intent, authority, reachability, and freshness independently. Record a one-sentence rationale for every component.
  6. Deduplicate candidates by canonical source, public handle, organization, and underlying event. Merge corroborating evidence rather than creating duplicate cards.
  7. Resolve this skill folder as SKILL_DIR, then run the deterministic report builder from any working directory:
node "$SKILL_DIR/scripts/build-report.mjs" --input prospects.json --out customer-signal-report
  1. Inspect summary.json and report.html. Recheck the top five sources before presenting results.

Quality gates

  • Require a working original source URL for every prospect.
  • Separate direct buying intent from weak topical interest.
  • Prefer recent first-person pain, active migration, budget, hiring, or evaluation signals.
  • Mark stale evidence instead of silently treating it as current.
  • Use only public professional channels explicitly exposed by the person or organization.
  • Keep outreach openers specific to the evidence, low-pressure, and under 70 words.
  • Do not automate contact or post/send messages without explicit authorization.

Outputs

The report builder creates:

  • report.html — searchable, filterable evidence dashboard
  • prospects.json — normalized, deduplicated machine-readable data
  • prospects.csv — CRM-friendly export
  • outreach.md — personalized opener variants
  • summary.json — run metrics and signal/source coverage

Gotchas

  • Search-result snippets are discovery aids, not evidence. Open the original page.
  • A complaint about a category is not automatically purchase intent. Score the timing separately.
  • A company name in a profile does not prove employment. Use the public source's own context and qualify uncertainty.
  • Do not infer private emails from names or domains.
  • Avoid broad scraping or login-gated sources. Respect platform terms, robots rules, and rate limits.
  • Do not include sensitive personal data, protected-class inferences, or non-professional contact details.

What ships with it: 6 files

32.0 KB alongside SKILL.md, 1 of them executable

assets/

references/

scripts/

Gives 0 of the 12 instructions most sales audience skills give in 640 tokens

Counted across 401 of the 401 authors here whose files we hold, read 2026-08-07

  • Read product marketing context before asking questionsin 21 of 401, across 11 files
  • Acknowledge competitor strengths honestlyin 18 of 401, across 7 files
  • Start every page with a summaryin 15 of 401, across 4 files
  • Use a single, low-friction call to actionin 15 of 401, across 7 files
  • Create a single source of truth for each competitorin 14 of 401, across 3 files
  • Make each follow-up email add new valuein 11 of 401, across 5 files
  • Cut any sentence that does not drive a replyin 10 of 401, across 4 files
  • Tie personalization directly to the problemin 10 of 401, across 4 files
  • Write paragraph comparisons for each dimensionin 9 of 401, across 3 files
  • Link between related competitor pagesin 9 of 401, across 3 files
  • Keep subject lines short and lowercasein 9 of 401, across 3 files
  • Define ideal customer profile from top customersin 9 of 401, across 3 files

Said here and by no other author read

  • derive ideal customer profile from the product
  • build queries across three source families
  • capture exact source url date author and excerpt
  • score fit intent authority reachability and freshness
  • deduplicate candidates by source handle and organization
  • require a working source url per prospect

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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