context-engineering

A method for deciding which project information a coding agent should see and when. It organizes rules, design documents, source files, errors, and test results so the agent can follow the project’s conventions.

In plain words
What is it for?
Use it when starting a coding session, switching tasks, setting up AI-assisted development, or improving an agent’s results in an existing project.
Why use it?
It reduces incorrect assumptions, made-up interfaces, and missed project rules caused by giving the agent too little or unfocused information.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/chankov/agent-fleet/context-engineering
Any agent
npx skills add chankov/agent-fleet --skill context-engineering
Clone the repo
git clone --depth 1 https://github.com/chankov/agent-fleet

Made for: Claude Code, Codex.

Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,395 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 92% 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 $0.00043 $0.02395
Opus 5 $0.00022 $0.01197
Sonnet 5 $0.00009 $0.00479
Haiku 4.5 $0.00004 $0.00239

Measured 2d ago against content hash 6df9b56caa57, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 2d 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

92% identical to context-engineering — 2 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.

.versions/0.0.1/skills/context-engineering/SKILL.md · 292 lines

How it starts

The opening of the file, as written. The whole thing — 292 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 (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.

When phrasing rules, agent prompts, or any instruction an agent will read, consult references/prompting-patterns.md before writing — it captures Anthropic and OpenAI guidance on positive instructions, explaining why, eliminating contradictions, and avoiding aggressive emphasis.

CLAUDE.md (for Claude Code):

# 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 · 292 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. 2d ago First seen · 292 lines · 43 tokens per session scan A 6df9b56caa57

Subscribe to this mod's changes

context-engineering is a skill published in the GitHub repository chankov/agent-fleet (10 stars, last pushed 6d ago), licensed MIT. It adds 43 tokens to every session and 2,395 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to context-engineering, differing in 2 lines, and is treated as a copy.

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