context-engineering

context-engineering is a skill for Claude Code from aAAaqwq/AGI-Super-Team. It costs 43 tokens per session (2,341 once invoked), scanned A, a copy of context-engineering, MIT.

A method for deciding which project information an AI coding agent should see and when. It organizes instructions, design documents, relevant source files, and test results by how persistent or task-specific they are.

In plain words
What is it for?
Use it when starting a coding session, switching tasks, improving weak agent output, setting up an AI-assisted project, or making sure work follows the project’s rules.
Why use it?
It reduces mistakes caused by missing project conventions or by giving the agent too much unrelated information.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions CLAUDE.md; mentions Claude Code; mentions AGENTS.md.

Part of the agi-super-team plugin — 208 skills, 1 agent, 1 plugin shipped together

Good fit Use it when starting a coding session, switching tasks, improving weak agent output, setting up an AI-assisted project, or making sure work follows the project’s rules.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aaaaqwq/agi-super-team/context-engineering
Install

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.

Any agent
npx skills add aAAaqwq/AGI-Super-Team --skill context-engineering
Clone the repo
git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team

Made for: Claude Code.

Or install agi-super-team, the plugin that ships this one along with the rest of its 208 skills, 1 agent, 1 plugin.

Wrote 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.

agentmods badge for context-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/context-engineering/github.svg)](https://agentmods.dev/skills/aaaaqwq/agi-super-team/context-engineering)
Your own site
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/context-engineering"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/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.

agentmods 80×15 button for context-engineering

Your own site · 80×15
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/context-engineering"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/context-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,341 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 94% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.02341
Opus 5.5 $0.00017 $0.00936
Sonnet 5.5 $0.00009 $0.00468
Haiku 4.5 $0.00004 $0.00234

Measured 1mo ago against content hash d8b40aa8e398, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

Origin

This is a copy

94% identical to context-engineering — 72 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/agi-super-team-codex/skills/context-engineering/SKILL.md · 290 lines

How it starts

The opening of the file, as written. The whole thing — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Context Engineering

Overview

Feed agents the right information at the right time. Context is the single biggest lever for agent output quality — too little and the agent hallucinates, too much and it loses focus. Context engineering is the practice of deliberately curating what the agent sees, when it sees it, and how it's structured.

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 (AGENTS.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.

AGENTS.md (for OpenAI Codex):

# Project: [Name]

## Tech Stack
- React 18, TypeScript 5, Vite, Tailwind CSS 4
- Node.js 22, Express, PostgreSQL, Prisma

## Commands
- Build: `npm run build`
- Test: `npm test`
- Lint: `npm run lint --fix`
- Dev: `npm run dev`
- Type check: `npx tsc --noEmit`

## Code Conventions
- Functional components with hooks (no class components)
- Named exports (no default exports)
- colocate tests next to source: `Button.tsx` → `Button.test.tsx`
- Use `cn()` utility for conditional classNames
- Error boundaries at route level

## Boundaries
- Never commit .env files or secrets
- Never add dependencies without checking bundle size impact
- Ask before modifying database schema
- Always run tests before committing

## Patterns
[One short example of a well-written component in your style]

Read the full file on GitHub · 290 lines

Changes

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.

  1. 1mo ago First seen · 290 lines · 43 tokens per session scan A d8b40aa8e398

Subscribe to this mod's changes

context-engineering is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (110 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 2,341 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to context-engineering, differing in 72 lines, and is treated as a copy.

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