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 borhen68/SkillEngine --skill context-engineeringgit clone --depth 1 https://github.com/borhen68/SkillEngineWrote 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/borhen68/skillengine/context-engineering)<a href="https://agentmods.dev/skills/borhen68/skillengine/context-engineering"><img src="https://agentmods.dev/badge/skills/borhen68/skillengine/context-engineering/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/borhen68/skillengine/context-engineering"><img src="https://agentmods.dev/badge/skills/borhen68/skillengine/context-engineering.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 | $0.00043 | $0.02638 |
| Opus 5.5 | $0.00017 | $0.01055 |
| Sonnet 5.5 | $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 1mo 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 — 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.
- 1mo ago 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 4mo ago), licensed MIT. It adds 43 tokens to every session and 2,638 once invoked, about $0.0002 per session on Opus 5.5. 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
constraint-driven-development
A development method for writing a project's quality standards as a lasting, checkable contract. It can cover areas such as test coverage, security, performance, accessibility, and architecture.
code-review-and-quality
A code-review guide for checking changes across correctness, readability, architecture, security, and performance before they are merged.
doubt-driven-development
A development practice that puts an independent, fresh-context challenge in front of important coding decisions.
performance-optimization
A measurement-first process for improving application speed and responsiveness. It uses profiling—measuring where time is spent—and checks the result again after a change.
test-driven-development
Instructions for test-driven development, or TDD: writing a failing test first, then the smallest code that makes it pass, followed by cleanup.
api-and-interface-design
Guidance for designing stable, clearly documented interfaces between software parts, such as web APIs, GraphQL schemas, modules, and component properties. It covers defining the contract first, limiting accidental dependencies, and planning safe changes.