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Investor targeting

Skill oncesylvia/fundraising-skills/skills/investor-targeting

Free, open-source Claude Code skills for founder-led fundraising — find stage/sector-fit investors (researched live, no hallucinated lists), then run cold email, warm intros & cold calls without spam. Bilingual EN/中文 (US + China). Built by a founder raising for Copay (agentic stablecoin payments).

Install
npx -y skills add oncesylvia/fundraising-skills --skill investor-targeting

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Build a tiered, stage- and sector-fit list of investors (VCs, angels, micro-funds, accelerators) for a founder's raise, using free public sources and live web search. Use when a founder asks "who should I raise from", "find investors for my startup", "which funds invest in <sector> at <stage>", or wants to build or qualify a fundraising target list. Never invents firms or partners — every name is researched live and carries a source link.

SKILL.md

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Investor targeting

Help a founder build a tiered target list of investors that actually fit their stage, sector, geography, and check size — grounded in live research, not memory. A wrong or invented name wastes the founder's scarcest resource (warm intros and credibility), so the prime directive is: research, cite, flag — never fabricate.

Read shared/references/outreach-ethics.md before producing output.

The hard rule on hallucination

You do not have a reliable investor database in your weights. Fund theses, partner moves, check sizes, and "are they actively deploying" change constantly. Therefore:

  • Do not name a specific firm, partner, or angel from memory as a recommendation. Every name in the final list must come from a live WebSearch / WebFetch performed in this session.
  • Each entry carries a source link and a confidence flag (verified / likely / unverified — check).
  • If research is thin for a niche, say so. A short honest list beats a long fabricated one. It is correct to return "I found 6 strong fits; here are 4 search angles to find more" rather than padding to 30.

Step 1 — Profile the raise (ask before searching)

Collect these from the founder. If they're missing, ask; don't assume.

  1. One-liner: what you do, for whom, the wedge.
  2. Stage & round: pre-seed / seed / Series A; how much you're raising; how much is committed. (See references/stage-map.md for what each stage means for targeting.)
  3. Sector / category + business model (B2B SaaS, consumer, deep tech, fintech, hardware, marketplace, AI infra, etc.). See references/sector-taxonomy.md.
  4. Geography: where you're based, where you can take money from (some funds only invest in their region/jurisdiction).
  5. Traction: the 1–2 metrics that make you fundable right now (revenue, growth, users, LOIs, a notable design partner). This drives who is a fit — a fund's check size must match your stage.
  6. Any constraints: strategic investors to court or avoid, conflicts (competing portfolio cos), values requirements.

Step 2 — Search across complementary angles

Don't rely on one query. Use several angles so you don't miss whole pockets. See references/free-data-sources.md for the full source list. Core moves:

  • Thesis search: "<sector> <stage> investors", "funds investing in <category> 2025", "<sub-niche> seed funds".
  • Portfolio-analogy search: find a few non-competing companies one notch ahead of you in the same category, then search who funded their seed/A. Their investors have proven appetite for your space. ("who invested in <company> seed round").
  • Operator-angel search: search for angels who built or led in your category ("<category> angel investors", notable operators who now angel invest). Angels move faster and are often the first checks.
  • Accelerator search: if pre-seed/idea stage, search accelerators/studios that focus on your sector or geography.
  • Directory search: OpenVC, NFX Signal, the fund's own "submit a deal" page, recent SEC Form D filings, AngelList/Wellfound syndicates, relevant newsletters and "VC thesis" posts.

For each promising hit, WebFetch the firm/angel's own site to confirm: stage focus, check size, sector, geography, and how they want to be contacted (many list a submit form or a partner's preferred path).

Step 3 — Qualify and tier

Score each candidate on fit, then sort into tiers:

  • Stage fit — does their typical check match your round?
  • Sector fit — explicit thesis or portfolio evidence in your space?
  • Geo/jurisdiction fit — can they legally/practically invest in you?
  • No conflict — not already backing a direct competitor.
  • Reachability — is there a warm path or a clear public contact route?

Tier the output:

  • Tier A (high conviction, lead with these) — strong on all five, clear contact path. Aim for a focused set you can deeply personalize.
  • Tier B (good fit, second wave) — solid fit, weaker on one axis or no warm path yet.
  • Tier C (worth watching / opportunistic) — plausible but unverified, or fit is partial.

Step 3.5 — Sequence the outreach (don't burn your best leads first)

Recommend running outreach in waves: start with a few Tier B firms to pressure- test the pitch and gather objections, refine, then approach Tier A with the sharpened version and your best warm intros. Hand the Tier A targets to the warm-intro and cold-email skills.

Step 4 — Deliver

Output a table the founder can paste into a tracker (the pipeline-tracker format if present), with columns:

Firm / AngelTierWhy they fitStage & checkContact pathSourceConfidence

Then add:

  • Coverage note: which angles you searched and which you didn't, so the founder knows what's not covered.
  • Next searches: 3–5 concrete queries to extend the list themselves.
  • Verify-before-send reminder: re-check each firm's current stage focus and contact preference right before reaching out — these go stale.

Bilingual / China market (中文)

If the founder is raising in the China market, read shared/references/china-market-playbook.md first. The free data sources differ — use IT桔子 / 企查查 / 天眼查 / 36氪 / 投资界 instead of Crunchbase to verify a fund's deals and whether it's an RMB or state-backed fund (人民币/国资, which changes its mandate and process). Same anti-hallucination rule: research live, cite the source, never invent a 机构 name.

Anti-patterns to refuse

  • Producing a long list "from knowledge" without searching.
  • Guessing partner email addresses.
  • Recommending a fund whose stage clearly doesn't match (e.g., a growth fund for a pre-seed idea) just to lengthen the list.
  • Dropping source links "to keep it clean." The links are the point.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.