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 shawnpang/startup-founder-skills --skill investor-researchgit clone --depth 1 https://github.com/shawnpang/startup-founder-skillsWrote 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/shawnpang/startup-founder-skills/investor-research)<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/investor-research"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/investor-research/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/shawnpang/startup-founder-skills/investor-research"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/investor-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00053 | $0.01690 |
| Opus 5 | $0.00026 | $0.00845 |
| Sonnet 5 | $0.00011 | $0.00338 |
| Haiku 4.5 | $0.00005 | $0.00169 |
Grade A, and why
investor-research 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- investor-research — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investor Research
When to Use
- The founder is preparing to fundraise and needs a target investor list.
- The founder has a list of investors and wants to qualify or prioritize them.
- The founder asks which VCs or angels are a good fit for their stage, sector, or geography.
- The founder wants to understand a specific fund's thesis, portfolio, or decision-making process.
Context Required
From startup-context: stage, sector/category, location, current round target (amount), business model, and any existing investor relationships or warm connections.
From the user: geographic preferences (if any), whether they want VC-only, angel-only, or both, any investors already in conversation, and any firms they want to explicitly avoid (e.g., portfolio conflicts they know about).
Workflow
- Read startup context — Pull stage, sector, geography, round size, and existing investors from
.agents/startup-context.md. - Define investor criteria — Based on context, establish the filtering parameters: stage match, sector focus, typical check size range, geographic relevance, and portfolio conflict exclusions.
- Build the raw list — Research investors matching the criteria. For each investor, capture: firm name, partner name, fund stage focus, sector focus, typical check size, recent fund size/vintage, portfolio companies, geographic preference, and a source URL.
- Check for conflicts — Flag any firm that has a portfolio company directly competing with the founder's startup. These go on a "conflicts" list, not the target list.
- Score and tier — Assign each investor to Tier 1 (strong fit, prioritize), Tier 2 (good fit, pursue), or Tier 3 (acceptable fit, use as backfill) using the scoring framework below.
- Identify warm paths — For each Tier 1 investor, suggest how the founder might get a warm intro: mutual connections, portfolio founder intros, accelerator networks, or conference overlap.
- Deliver the target list — Output a structured, sortable list with tiers and recommended outreach order.
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 · 105 lines · 53 tokens per session scan A fd64e67dc9ba
investor-research is a skill published in the GitHub repository shawnpang/startup-founder-skills (321 stars, last pushed 5mo ago), licensed MIT. It adds 53 tokens to every session and 1,690 once invoked, about $0.0003 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 2026-08-30.
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