vc-finder

vc-finder is a skill for Claude Code from Varnan-Tech/opendirectory. It costs 107 tokens per session (11,667 once invoked), scanned A, original, MIT.

A research workflow for finding investors for a startup. It studies the startup's industry and funding stage, finds comparable funded companies, and identifies venture capital firms connected to that space.

In plain words
What is it for?
Use it to research a startup from its product URL or description, identify five comparable companies, find their investors, and produce a ranked list of relevant venture capital firms with supporting details.
Why use it?
It helps replace broad, unsupported investor lists with candidates tied to search evidence. It also separates investors found through comparable companies from those that publish relevant investment ideas.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Gemini CLI.

Part of the opendirectory plugin — 58 skills shipped together

Good fit Use it to research a startup from its product URL or description, identify five comparable companies, find their investors, and produce a ranked list of relevant venture capital firms with supporting details.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/varnan-tech/opendirectory/vc-finder
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 Varnan-Tech/opendirectory --skill vc-finder
Clone the repo
git clone --depth 1 https://github.com/Varnan-Tech/opendirectory

Made for: Claude Code.

Or install opendirectory, the plugin that ships this one along with the rest of its 58 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.

agentmods badge for vc-finder

README.md
[![agentmods](https://agentmods.dev/badge/skills/varnan-tech/opendirectory/vc-finder/github.svg)](https://agentmods.dev/skills/varnan-tech/opendirectory/vc-finder)
Your own site
<a href="https://agentmods.dev/skills/varnan-tech/opendirectory/vc-finder"><img src="https://agentmods.dev/badge/skills/varnan-tech/opendirectory/vc-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.

agentmods 80×15 button for vc-finder

Your own site · 80×15
<a href="https://agentmods.dev/skills/varnan-tech/opendirectory/vc-finder"><img src="https://agentmods.dev/badge/skills/varnan-tech/opendirectory/vc-finder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 11,667 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 11 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 43
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • high Supply Chain · line 78
    Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.
    Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
  • high Supply Chain · line 94
    Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.
    Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
  • high Tool Misuse · line 913
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • medium Data Exfiltration · line 78
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 78
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 94
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 94
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 439
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 538
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 607
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00107 $0.11667
Opus 5 $0.00053 $0.05833
Sonnet 5 $0.00021 $0.02333
Haiku 4.5 $0.00011 $0.01167

Measured 9d ago against content hash 16d9de05adb7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

vc-finder scanned grade A with 1 finding 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 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/match_funds.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

from urllib.parse import urlparse
skills/vc-finder/SKILL.md · 917 lines

How it starts

The opening of the file, as written. The whole thing — 917 lines — stays where its author put it; the contents beside it link to each section on GitHub.

VC Finder

Take a product URL or description. Detect industry and stage. Find 5 comparable funded companies. Run two research tracks: who invested in those comparables (Track A), and which VCs publish theses about this space (Track B). Return a sourced, ranked investor list with outreach hooks.


Zero-hallucination policy: Every fact in the output must be traceable to a specific Tavily search result or the fetched product page. This applies to:

  • Comparable company names: must appear in Tavily search results, not AI training knowledge
  • VC fund names: must appear verbatim in Tavily search results
  • Check sizes, stage focus, portfolio companies: must come from search snippets, not AI knowledge
  • Fund overviews and thesis summaries: extracted from search snippets only. If a detail is not in the search data, write "not found in search data" -- do not fill from training knowledge.

Common Mistakes

The agent will want to... Why that's wrong
Add a16z or Sequoia because they are famous A famous VC without evidence is noise. Only include VCs that appear in Tavily search results for this specific product. Name-dropping wastes the founder's time.
Generate comparable companies from training knowledge Comparables must come from Tavily search results (Step 6). AI knowledge of companies is not evidence -- a company suggested from memory may have wrong funding status or may not be a true comparable.
Continue when all 5 Track A searches return 0 results Zero Track A results means the comparables were wrong or too obscure. Stop, re-run Step 6 with broader search queries, and retry.
Include a Track B VC without citing the article or post Thesis without a source is indistinguishable from hallucination. The founder cannot verify it and the list loses all credibility.
Fill in fund overview from training knowledge Fund overviews must come from Tavily snippet text only. If the snippets don't describe the fund, write "not found in search data".
Detect stage from website aesthetics Stage must come from the specific CTA signals detected in Step 4.
Write generic outreach hooks Every outreach hook must name this specific product's differentiator and a specific VC portfolio signal or thesis quote from the search data.
Skip the URL fetch when the user also provides a description Always fetch the URL. The live page often reveals stage signals that the user's description omits.

Read the full file on GitHub · 917 lines

Files

What ships with it

7 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.

  1. 9d ago First seen · 917 lines · 107 tokens per session scan A 16d9de05adb7

Subscribe to this mod's changes

vc-finder is a skill published in the GitHub repository Varnan-Tech/opendirectory (635 stars, last pushed 23d ago), licensed MIT. It adds 107 tokens to every session and 11,667 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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