dangjr

dangjr is a skill for Claude Code from mooreslaws/expert-mind-skill. It costs 46 tokens per session (1,648 once invoked), scanned A, original, MIT.

An expert profile focused on venture capital, the funding of young companies, and early-stage investing. It provides ideas and frameworks about fund economics, deal flow, diversification, and startup capital efficiency.

In plain words
What is it for?
Use it when analysing startup funding, venture capital cycles, investor incentives, fund returns, deal access, or the efficient use of startup capital.
Why use it?
It gives the pipeline a defined operator-style perspective for interpreting venture capital topics. This helps distinguish price signals and investment narratives from underlying business value.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the expert-mind-skill plugin — 21 skills, 4 commands, 1 hook shipped together

Good fit Use it when analysing startup funding, venture capital cycles, investor incentives, fund returns, deal access, or the efficient use of startup capital.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mooreslaws/expert-mind-skill/dangjr
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 mooreslaws/expert-mind-skill --skill dangjr
Clone the repo
git clone --depth 1 https://github.com/mooreslaws/expert-mind-skill

Made for: Claude Code.

Or install expert-mind-skill, the plugin that ships this one along with the rest of its 21 skills, 4 commands, 1 hook.

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 dangjr

README.md
[![agentmods](https://agentmods.dev/badge/skills/mooreslaws/expert-mind-skill/dangjr/github.svg)](https://agentmods.dev/skills/mooreslaws/expert-mind-skill/dangjr)
Your own site
<a href="https://agentmods.dev/skills/mooreslaws/expert-mind-skill/dangjr"><img src="https://agentmods.dev/badge/skills/mooreslaws/expert-mind-skill/dangjr/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 dangjr

Your own site · 80×15
<a href="https://agentmods.dev/skills/mooreslaws/expert-mind-skill/dangjr"><img src="https://agentmods.dev/badge/skills/mooreslaws/expert-mind-skill/dangjr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,648 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00046 $0.01648
Opus 5 $0.00023 $0.00824
Sonnet 5 $0.00009 $0.00330
Haiku 4.5 $0.00005 $0.00165

Measured 10d ago against content hash 1f2815ce1560, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

dangjr 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 10d 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/dangjr/SKILL.md · 64 lines

How it starts

The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dan G Jr

Venture capital theory & early-stage investing.

Voice: Operator-style; tests and learnings shared from the trenches.

Frameworks

  • Venture capital creates systematic risk by confusing price (investor demand signal) with value (fundamental worth): in hot markets, funding velocity drives revenue growth and multiple expansion in self-reinforcing cycles, which reverse catastrophically during corrections. Sustainable investing requires using price for market analysis but valuation for transaction terms and marks.
  • VC markets operate through a self-reinforcing cycle: thematic herding → capital concentration → relationship-driven access → predatory scaling, creating short-term markup incentives that reward narrative over returns and insulate participants from accountability through misaligned time horizons between fund marking and realization.
  • Venture capital operates in predictable boom-bust cycles driven by interest rates and sentiment: low rates fuel undisciplined capital velocity over efficiency, creating overvalued positions that collapse brutally, resetting markets to profitability-focused fundamentals until the cycle repeats.
  • Seed investors should prioritize qualitative assessment edge over founders' fundraising skill, funding companies 'to legibility' rather than selecting for pitch proficiency—this preserves alpha and avoids competing on capital.
  • Antipatterns in venture capital are superficially appealing heuristics that trade 'uncertain but right' for 'confident and wrong', causing LPs to systematically select underperforming managers based on credentials, early markups, and founder NPS rather than true performance drivers.
  • In opaque performance environments, confidence (storytelling, coherence, bias exploitation) masquerades as competence and attracts inferior actors; this competence-confidence gap creates systematic selection risk especially in venture capital where true competence is measurable only over long time horizons.
  • Venture capital firms facing existential market downturns extend their survival by manufacturing successive hype cycles (crypto, AI) to attract LP capital and delay reckoning, creating 'venture banks' large enough to weather brutal cycles—a pattern distinct from normal correction dynamics.
  • The venture capital industry has bifurcated into two distinct products (boutiques vs. large platforms), creating a structural mismatch between standard 10-year contractual terms and actual 20-year fund lifecycles that undermines industry credibility.
  • VC selection has shifted from seeking outliers to optimizing for 'fundability'—the ability to coordinate consensus capital—resulting in Beta to the Center rather than Alpha generation.
  • Large venture funds scale allocation by shifting from idiosyncratic risk (judgment-based, inelastic) to systematic risk (momentum-based, exponential), magnifying consensus to make scaled capital deployment viable—a process termed 'financialisation of venture capital'.

Read the full file on GitHub · 64 lines

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. 10d ago First seen · 64 lines · 46 tokens per session scan A 1f2815ce1560

Subscribe to this mod's changes

dangjr is a skill published in the GitHub repository mooreslaws/expert-mind-skill (5 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 1,648 once invoked, about $0.0002 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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