apply-double-down-portfolio-strategy

apply-double-down-portfolio-strategy is a skill for Claude Code from jeffreytse/grimoire-core. It costs 48 tokens per session (1,636 once invoked), scanned A, original, MIT.

An investment-allocation approach for early-stage investment portfolios: keep money available to invest more in the companies that show the clearest promise.

In plain words
What is it for?
Use it to decide which portfolio companies should receive additional investment as their performance becomes clearer.
Why use it?
It addresses the risk of spreading follow-on money evenly when a small number of investments may produce most of the returns.

Skill for Claude Code

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

Part of the grimoire-business plugin — 145 skills shipped together

Good fit Use it to decide which portfolio companies should receive additional investment as their performance becomes clearer.

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Install with agentmods
npx agentmods add skills/jeffreytse/grimoire-core/apply-double-down-portfolio-strategy
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 jeffreytse/grimoire-core --skill apply-double-down-portfolio-strategy
Clone the repo
git clone --depth 1 https://github.com/jeffreytse/grimoire-core

Made for: Claude Code.

Or install grimoire-business, the plugin that ships this one along with the rest of its 145 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 apply-double-down-portfolio-strategy

README.md
[![agentmods](https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-double-down-portfolio-strategy/github.svg)](https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-double-down-portfolio-strategy)
Your own site
<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-double-down-portfolio-strategy"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-double-down-portfolio-strategy/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 apply-double-down-portfolio-strategy

Your own site · 80×15
<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-double-down-portfolio-strategy"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-double-down-portfolio-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,636 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 $0.00048 $0.01636
Opus 5 $0.00024 $0.00818
Sonnet 5 $0.00010 $0.00327
Haiku 4.5 $0.00005 $0.00164

Measured 12d ago against content hash 273eea6998b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

apply-double-down-portfolio-strategy 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/business/entrepreneurship/skills/apply-double-down-portfolio-strategy/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Apply Double-Down Portfolio Strategy

Reserve significant capital specifically for follow-on investment in a portfolio's clearest-performing companies, rather than spreading initial capital evenly or allocating follow-on capital proportionally across the whole portfolio — because venture returns are dominated by a small number of outsized winners, and capturing that asymmetry requires concentrating more capital into demonstrated winners as they emerge, not just at initial entry.

Why This Is Best Practice

Adopted by: Neil Shen (沈南鹏) and Sequoia Capital China (now HongShan) have documented this reserve-capital, follow-on-concentration approach as a core part of their portfolio construction discipline, reflecting Sequoia's broader global practice of deliberately reserving a substantial portion of fund capital specifically for follow-on rounds in the portfolio's demonstrated top performers, rather than treating each portfolio company's ongoing capital allocation as fixed at the initial check size. Impact: Venture capital return data, documented across the industry, consistently shows returns dominated by a small number of investments that vastly outperform the rest of a given portfolio — the majority of early-stage investments return little or nothing, while a small handful drive the large majority of a fund's overall returns (the venture-capital analog to the public-equity "ten-bagger" pattern — see apply-ten-bagger-strategy). A fund that commits all its capital at initial entry, without reserving substantial capital for follow-on investment in the winners that emerge, cannot capture as much of this asymmetry as one that actively concentrates additional capital into demonstrated winners as they're identified. Why best: The alternative — spreading a fixed amount of capital evenly across a portfolio at initial investment, with no reserve for follow-on concentration — treats every portfolio company as equally worth continued investment, when in reality the evidence about which companies are actually succeeding only becomes available after initial investment, as they develop a track record. Proactively reserving capital to double down on the emerging winners captures more of the return concentration that venture investing is structurally built around, rather than leaving that additional capital allocation to chance or to whichever companies happen to raise the largest follow-on rounds regardless of actual performance signal.

Read the full file on GitHub · 68 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. 12d ago First seen · 68 lines · 48 tokens per session scan A 273eea6998b9

Subscribe to this mod's changes

apply-double-down-portfolio-strategy is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 23d ago), licensed MIT. It adds 48 tokens to every session and 1,636 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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