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-deep-industry-immersion-researchgit 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-deep-industry-immersion-research)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-deep-industry-immersion-research"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-deep-industry-immersion-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/jeffreytse/grimoire-core/apply-deep-industry-immersion-research"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-deep-industry-immersion-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 | $0.00050 | $0.01521 |
| Opus 5 | $0.00025 | $0.00760 |
| Sonnet 5 | $0.00010 | $0.00304 |
| Haiku 4.5 | $0.00005 | $0.00152 |
Grade A, and why
apply-deep-industry-immersion-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 11d 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 Deep Industry Immersion Research
Commit to extended, multi-year immersion in an emerging technology trend's practitioners, technology, and market structure before evaluating any specific deal within it — rather than evaluating opportunities opportunistically as they happen to arrive, without a pre-existing depth of understanding to evaluate them against.
Why This Is Best Practice
Adopted by: Liu Qin (刘芹), founding partner of Wuyuan Capital (五源资本, formerly known as 晨兴资本/Morningside Venture Capital China), is documented across Chinese and English technology press accounts as having spent years directly studying the emerging mobile internet and smartphone hardware/software trend before making Wuyuan Capital's early investment in Xiaomi — one of the earliest institutional investments in what became one of China's largest technology companies. This extended immersion, rather than opportunistic deal evaluation, is specifically credited in these accounts as the basis for the conviction and speed with which the investment was made once the opportunity appeared.
Impact: Documented accounts of the Xiaomi investment describe Liu Qin arriving at the specific investment decision with unusually high conviction and speed relative to typical early-stage due diligence timelines, attributed directly to years of prior groundwork understanding the mobile internet trend's technology and market dynamics — rather than starting the analytical process from a blank slate at the moment the specific opportunity appeared. This groundwork allowed rapid, well-grounded evaluation of a genuinely non-consensus opportunity (see apply-non-consensus-category-conviction) that a more opportunistic evaluation process, starting analysis from zero, likely could not have matched in speed or depth.
Why best: Opportunistic deal evaluation — assessing each opportunity as it arrives without pre-existing depth in the relevant trend — forces the investor to build foundational understanding of the industry at the same time as evaluating the specific deal, under whatever time pressure the deal process imposes. Extended prior immersion decouples the two: foundational understanding is built well before any specific opportunity appears, so that when a strong opportunity does appear, evaluation can proceed rapidly and with genuine depth, rather than being constrained by how much can be learned under deal-timeline pressure.
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.
- 11d ago First seen · 68 lines · 50 tokens per session scan A 118a98fea10d
apply-deep-industry-immersion-research is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 22d ago), licensed MIT. It adds 50 tokens to every session and 1,521 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 2026-08-31.
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