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-local-market-adaptation-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-local-market-adaptation-strategy)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-local-market-adaptation-strategy"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-local-market-adaptation-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-local-market-adaptation-strategy"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-local-market-adaptation-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 | $0.00049 | $0.01497 |
| Opus 5 | $0.00024 | $0.00749 |
| Sonnet 5 | $0.00010 | $0.00299 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
apply-local-market-adaptation-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 9d 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 Local Market Adaptation Strategy
Adapt a business model that succeeded in one market to the specific consumer behavior, regulatory environment, and competitive dynamics of a new market — rather than directly porting the original model unchanged — because a model's success is usually tied to conditions specific to its original market that don't automatically transfer.
Why This Is Best Practice
Adopted by: Neil Shen (沈南鹏) and Sequoia Capital China (now HongShan) built much of their early investment thesis around backing companies that adapted successful foreign business models (particularly from the U.S. consumer internet market) to local Chinese market conditions, rather than companies that attempted to directly replicate the original model unchanged. This approach is documented across Forbes and Fortune coverage of Sequoia China's investment philosophy during the early Chinese consumer internet era, and is broadly recognized in the venture capital industry as a defining feature of the "copy but adapt" wave of company-building that produced several of China's largest consumer internet companies. Impact: Companies that directly ported foreign models into the Chinese market without adaptation — assuming that a model's success in one market would automatically translate — frequently underperformed relative to competitors that adapted the model to local payment behavior, mobile usage patterns, regulatory requirements, and competitive dynamics specific to the local market. The companies Sequoia China backed that succeeded most durably were generally the ones that treated the original model as a starting reference point requiring substantial local adaptation, not a template to be copied unchanged. Why best: A business model's success is rarely a function of the abstract idea alone — it depends on the specific fit between the model and the market conditions (consumer purchasing behavior, payment infrastructure, regulatory environment, existing competitive landscape) it was built for. Assuming this fit transfers automatically to a structurally different market ignores the actual mechanism behind the original model's success. Adapting the model deliberately, based on genuine understanding of what's different about the new market, preserves the core mechanism of value creation while adjusting for the specific conditions that actually determine whether it works in the new context.
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.
- 9d ago First seen · 68 lines · 49 tokens per session scan A 83058b75e68b
apply-local-market-adaptation-strategy is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 24d ago), licensed MIT. It adds 49 tokens to every session and 1,497 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 2026-09-03.
Other skills, from other repositories
semantic-model-disambiguation
Analyze Power BI semantic models for column-level overlaps that confuse Copilot and Fabric data agents. Detect ambiguity, review with domain expert, apply fixes via MCP.
prompt-2-data
Generate comprehensive synthetic relational data for any specified subject with multiple normalized CSV files maintaining referential integrity.
infrastructure-as-code
Domain: DevOps & Cloud Engineering.
academic-paper-drafting
End-to-end academic paper drafting for CHI, HBR, journals, and conferences with venue-specific templates, drafting workflows, and revision strategies.
research-first-development
Build knowledge bases that build software — research before code, teach before execute.
agent-governance
Patterns for adding safety, trust, and policy enforcement to AI agent systems -- control which tools agents can call, what content they process, and maintain accountability through audit trails.