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/borhen68/skillengine/context-engineeringnpx skills add borhen68/SkillEngine --skill context-engineeringgit clone --depth 1 https://github.com/borhen68/SkillEngineWhat 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 | $0.00043 | $0.02638 |
| Opus 5 | $0.00022 | $0.01319 |
| Sonnet 5 | $0.00009 | $0.00528 |
| Haiku 4.5 | $0.00004 | $0.00264 |
Grade A, and why
context-engineering 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 yesterday.
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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
Overview
An AI agent with perfect context produces code that looks like it was written by your best engineer. The same agent with poor context produces hallucinated APIs, wrong patterns, and conventions from a different project entirely. Context is not just "more information" — it's the right information, structured correctly, delivered at the right moment.
The context contract: Every project needs a rules file that captures conventions, boundaries, and patterns. Every session starts with that context loaded. Every task switch refreshes the relevant context. Context is not overhead — it's the single biggest lever for output quality.
Real-world impact: Teams with structured context files see 3x fewer "why did the agent do it that way?" moments. The 30 minutes spent writing a project rules file saves hours of correcting the agent's assumptions about your tech stack, naming conventions, and architectural patterns.
When to Use
- Starting a new coding session
- Agent output quality is declining (wrong patterns, hallucinated APIs, ignoring conventions)
- Switching between different parts of a codebase
- Setting up a new project for AI-assisted development
- The agent is not following project conventions
The Context Hierarchy
Structure context from most persistent to most transient:
┌─────────────────────────────────────┐
│ 1. Rules Files (CLAUDE.md, etc.) │ ← Always loaded, project-wide
├─────────────────────────────────────┤
│ 2. Spec / Architecture Docs │ ← Loaded per feature/session
├─────────────────────────────────────┤
│ 3. Relevant Source Files │ ← Loaded per task
├─────────────────────────────────────┤
│ 4. Error Output / Test Results │ ← Loaded per iteration
├─────────────────────────────────────┤
│ 5. Conversation History │ ← Accumulates, compacts
└─────────────────────────────────────┘
Level 1: Rules Files
Create a rules file that persists across sessions. This is the highest-leverage context you can provide.
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.
- yesterday First seen · 318 lines · 43 tokens per session scan A 1da4adf93635
context-engineering is a skill published in the GitHub repository borhen68/SkillEngine (17 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 2,638 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 2026-08-30.
Other skills, from other repositories
code-review-and-quality
执行多维度代码审查。用于合并任何变更之前；用于审查自己、其他 agent 或人类编写的代码；用于在代码进入主分支前从多个维度评估代码质量。.
code-simplification
为清晰度简化代码。用于在不改变行为的前提下重构代码以提升清晰度；用于代码能运行但比应有状态更难阅读、维护或扩展时；用于审查已累积不必要复杂度的代码时。.
doubt-driven-development
在每个非平凡决策成立前，用全新上下文进行对抗式审查。当正确性比速度更重要、处理不熟悉代码、风险较高（生产、安全敏感逻辑、不可逆操作），或任何自信输出现在验证比之后调试更便宜时使用。.
test-driven-development
用测试驱动开发。用于实现任何逻辑、修复任何 bug，或改变任何行为。用于需要证明代码能工作、收到 bug 报告，或即将修改现有功能时。.
api-and-interface-design
指导稳定的 API 和接口设计。设计 API、模块边界或任何公共接口时使用。创建 REST 或 GraphQL endpoint、定义模块之间的类型契约，或建立前后端边界时使用。.
ci-cd-and-automation
自动化 CI/CD pipeline 设置。用于设置或修改构建和部署 pipeline 时；用于需要自动化质量门禁、在 CI 中配置 test runners，或建立部署策略时。.