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 jeffreytse/grimoire-core --skill apply-double-down-portfolio-strategygit clone --depth 1 https://github.com/jeffreytse/grimoire-coreWrote 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/jeffreytse/grimoire-core/apply-double-down-portfolio-strategy)<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.
<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>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 | $0.00048 | $0.01636 |
| Opus 5 | $0.00024 | $0.00818 |
| Sonnet 5 | $0.00010 | $0.00327 |
| Haiku 4.5 | $0.00005 | $0.00164 |
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.
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.
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 · 68 lines · 48 tokens per session scan A 273eea6998b9
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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