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 agentmods add skills/koersliven/lore/lore-initnpx skills add koersliven/Lore --skill lore-initgit clone --depth 1 https://github.com/koersliven/LoreWrote 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/koersliven/lore/lore-init)<a href="https://agentmods.dev/skills/koersliven/lore/lore-init"><img src="https://agentmods.dev/badge/skills/koersliven/lore/lore-init.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.01280 |
| Opus 5 | $0.00007 | $0.00640 |
| Sonnet 5 | $0.00003 | $0.00256 |
| Haiku 4.5 | $0.00001 | $0.00128 |
Grade A, and why
lore-init 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 3d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/init-context — Project Context Initialization
When to Trigger
- User explicitly invokes this skill
- Agent enters a project with no
.ai-context/snapshot.md - Agent detects a project it has never seen before (no snapshot, no CLAUDE.md architecture section)
Purpose
Complete the 0→1 knowledge capture. Extract the developer's mental model of the project into structured .ai-context/ files so the agent reaches a minimum viable understanding level.
Process
Step 1: Project Discovery
First, scan the project to understand basics:
- What language/framework? (package.json, pom.xml, go.mod, etc.)
- What are the entry points? (controllers, routes, main functions)
- Module structure (directories, packages, workspaces)
- External dependencies (imports, package.json dependencies, Maven GAVs)
Report what you found, then proceed to questioning.
Step 2: Guided Questioning
Ask the user these questions in batches (3-5 at a time, conversational tone). Adapt questions based on what you already discovered.
Category A: External Dependencies
- "这个项目调用了哪些外部服务或 API?对方提供什么能力?"
- "有哪些依赖定义在外部 JAR 包里,你看不到源码的?"
Category B: Configuration
- "配置存在哪里?(Diamond / env / 数据库 / 本地文件?)"
- "有哪些关键配置项会影响行为?改了什么配置会导致线上出问题?"
Category C: Data Layer
- "数据存储在哪里?有分库分表吗?规则是什么?"
- "缓存策略是什么?什么数据被缓存?"
Category D: Business Context
- "这个项目的业务场景是什么?服务于谁?"
- "核心业务流程是什么?能描述 3-5 个关键步骤吗?"
Category E: Implicit Contracts
- "有哪些未文档化的约定?"
- "有哪些代码不能碰?为什么?"
- "踩过哪些坑?哪些设计是因为历史事故才这样的?"
Step 3: Generate Initial Files
Based on answers, create:
.ai-context/snapshot.md:
- Project overview (what it is, who it serves)
- Core flows (end-to-end process descriptions)
- Key decisions (WHY-level, with source tracing)
- Constraints (things that must not change)
- External dependencies (things AI cannot read from source)
- Config index (where to find configurations)
- Business context (scenarios, relationships)
- Glossary (terminology)
Each entry must include:
- 来源: {{user name}}, {{date}}, /init-context guided session
- 置信度: high (directly from project owner/developer)
- 验证状态: verified
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.
- 3d ago First seen · 153 lines · 14 tokens per session scan A e4a116c10e35
lore-init is a skill published in the GitHub repository koersliven/Lore (5 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 1,280 once invoked, about $0.0001 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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