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 GalaxyRuler/Galactic-skills --skill angel-investinggit clone --depth 1 https://github.com/GalaxyRuler/Galactic-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/galaxyruler/galactic-skills/angel-investing)<a href="https://agentmods.dev/skills/galaxyruler/galactic-skills/angel-investing"><img src="https://agentmods.dev/badge/skills/galaxyruler/galactic-skills/angel-investing/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/galaxyruler/galactic-skills/angel-investing"><img src="https://agentmods.dev/badge/skills/galaxyruler/galactic-skills/angel-investing.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 | $0.00136 | $0.01750 |
| Opus 5 | $0.00068 | $0.00875 |
| Sonnet 5 | $0.00027 | $0.00350 |
| Haiku 4.5 | $0.00014 | $0.00175 |
Grade A, and why
angel-investing 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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Angel Investing
Investor-side evaluation OS for early-stage deals. The job is to replace the pitch narrative with validated evidence and decide where your time and capital go — not to reward a polished deck or a charismatic founder. Decision support, not investment advice.
Opposite chair from the
startup-consultingskill. That one builds the company (founder-side); this one evaluates inbound deals (investor-side). Same disciplines — TAM, unit economics, SAFEs — read from the buyer's seat. Founder-side request → usestartup-consulting.
Core principles
- Screen for rejection first — a 5-minute pass answers "does this deserve more time?", never "should I invest?".
- Thesis-fit ≠ investable-for-you — a strong company can still be wrong for your stage, check size, geography, or edge.
- Pick deal-specific CSFs — identify the 3-7 critical success factors that actually drive this deal; don't apply a generic rubric blindly.
- Superpowers and fatal flaws don't add — a critical flaw in a high-importance CSF kills the deal regardless of a high average.
- Separate evidence types — facts, founder claims, assumptions, external data, and analyst judgment stay structurally distinct.
- Validate, don't trust — the founder interview produces hypotheses; diligence tests them.
- Don't false-precision early valuation — use ranges, comps, scorecards; label assumptions; never a single misleading point figure.
- Model ownership, dilution, terms — not just price — liquidation preference, anti-dilution, and the cap table can outweigh the headline valuation.
- Portfolio construction underwrites outcomes — returns are power-law; size every check inside a diversification and reserve plan.
- Escalate professional issues — legal, tax, regulatory, IP, security, and audits require qualified experts.
The three-tier funnel (reject ~90% per tier)
| Tier | Time | Purpose | Output |
|---|---|---|---|
| 1 — Assess | 5 min | Screen for rejection: thesis fit, team/product/market sanity, evidence of pain | Reject / clarify / interview |
| 2 — Evaluate | 45-60 min | Founder interview: score CSF skill, red/green flags, how they think | Pass / request data room / deeper diligence |
| 3 — Validate | 3+ hr | Diligence: validate decision-drivers first, then standard, then lower-risk | Memo + recommendation |
What ships with it
8 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.
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 · 86 lines · 136 tokens per session scan A 87c7d9b2fa15
angel-investing is a skill published in the GitHub repository GalaxyRuler/Galactic-skills (5 stars, last pushed 7d ago), licensed MIT. It adds 136 tokens to every session and 1,750 once invoked, about $0.0007 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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