context-engineering

A method for preparing information and instructions for AI agents so the most relevant details arrive at the right stage. It emphasizes useful signal over unnecessary context.

In plain words
What is it for?
Use it when designing prompts, delegating complex work, or coordinating several agents that need different project details.
Why use it?
It reduces wasted context, repetition, and mistakes caused by giving an agent too much or poorly organized 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/drvoss/everything-copilot-cli/context-engineering
Any agent
npx skills add drvoss/everything-copilot-cli --skill context-engineering
Clone the repo
git clone --depth 1 https://github.com/drvoss/everything-copilot-cli

Made for: Claude Code, Codex.

Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,933 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00024 $0.01933
Opus 5 $0.00012 $0.00966
Sonnet 5 $0.00005 $0.00387
Haiku 4.5 $0.00002 $0.00193

Measured 2d ago against content hash c250c758e5a9, 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.

skills/development/context-engineering/SKILL.md · 263 lines

How it starts

The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Context Engineering

When to Use

  • Delegating a complex task to an AI agent
  • An agent keeps repeating the wrong approach
  • Agent quality drops as the context grows longer
  • Designing a pipeline where multiple agents collaborate

Difference from context-prime (Copilot-specific):

  • context-prime: loads live project context at session start
  • context-engineering: structures the best possible information for a specific task

Prerequisites

  • The delegated task has a clear goal and scope
  • You know the relevant files or domain area

Workflow

1. Analyze signal vs. noise

Classify the information you plan to give the agent:

Information type Include? Why
Directly relevant code files ✅ Yes The agent must edit or reason about them
Interface/type definitions ✅ Yes Essential for understanding contracts
Unrelated files ❌ No Waste tokens and reduce focus
Entire README ❌ No (summarize instead) Low information density for the size
Information the agent already has ❌ No Duplicate token cost

2. Progressive Disclosure

Do not provide everything at once. Reveal only what each phase needs:

Phase 1: Task definition + interface contract
Phase 2: Implementation starts -> add relevant files
Phase 3: Testing -> add test patterns and references

3. Use a structured context template

Use this shape when instructing an agent:

## Task
[one clear objective]

## Given (what is already known)
- [file path]: [role]
- [interface contract]

## Constraints (what must not happen)
- [prohibited action]
- [files that must not be changed]

## Done When
- [ ] [specific, testable criterion]

4. Manage the context-window budget

Use context size intentionally. For exact model choice, see multi-model-strategy:

Task complexity Context size Example
Short task (fast response first) 2-3 files, clear goal small bug fix, type addition
Medium task (balanced) 5-10 files, interface contract new API endpoint, component addition
Long task (deep reasoning first) 10-20 files, module-level context architecture refactor, complex bug

Read the full file on GitHub · 263 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 · 263 lines · 24 tokens per session scan A c250c758e5a9

Subscribe to this mod's changes

context-engineering is a skill published in the GitHub repository drvoss/everything-copilot-cli (45 stars, last pushed 5d ago), licensed MIT. It adds 24 tokens to every session and 1,933 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 2026-08-30.

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