What is investor-targeting?

A research guide for building a ranked list of investors who fit a startup's stage, industry, location, and fundraising needs. It requires current public research and links for each named investor.

It is for finding and qualifying venture funds, angel investors, micro-funds, and accelerators for a specific startup raise.

It reduces wasted outreach by filtering out investors who are unlikely to fit and by avoiding guesses based on outdated information.

investor-targeting — Skill for Claude Code by oncesylvia

Install

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.

Any agent

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

Clone the repo

git clone --depth 1 https://github.com/oncesylvia/fundraising-skills

Made for: Claude Code.

Or install fundraising-skills, the plugin that ships this one along with the rest of its 10 skills.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/oncesylvia/fundraising-skills/investor-targeting/github.svg)](https://agentmods.dev/skills/oncesylvia/fundraising-skills/investor-targeting)

Your own site

<a href="https://agentmods.dev/skills/oncesylvia/fundraising-skills/investor-targeting"><img src="https://agentmods.dev/badge/skills/oncesylvia/fundraising-skills/investor-targeting/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/oncesylvia/fundraising-skills/investor-targeting"><img src="https://agentmods.dev/badge/skills/oncesylvia/fundraising-skills/investor-targeting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 106 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,581 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00106	$0.01581
Opus 5	$0.00053	$0.00790
Sonnet 5	$0.00021	$0.00316
Haiku 4.5	$0.00011	$0.00158

Measured 12d ago against content hash 62985568a9f3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

investor-targeting 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 12d 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.

skills/investor-targeting/SKILL.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

One-liner: what you do, for whom, the wedge.
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.)
Sector / category + business model (B2B SaaS, consumer, deep tech, fintech, hardware, marketplace, AI infra, etc.). See references/sector-taxonomy.md.
Geography: where you're based, where you can take money from (some funds only invest in their region/jurisdiction).
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.
Any constraints: strategic investors to court or avoid, conflicts (competing portfolio cos), values requirements.

Read the full file on GitHub · 131 lines

Files

What ships with it

3 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.

Changes

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.

12d ago First seen · 131 lines · 106 tokens per session scan A 62985568a9f3

Subscribe to this mod's changes

investor-targeting is a skill published in the GitHub repository oncesylvia/fundraising-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 106 tokens to every session and 1,581 once invoked, about $0.0005 per session on Opus 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 2026-08-31.

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