context-engineering
Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.
Best use case
context-engineering is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.
Teams using context-engineering should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/context-engineering/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How context-engineering Compares
| Feature / Agent | context-engineering | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Context Engineering ## Overview Context management methodology adapted from the Everything Claude Code project. Optimizes context window usage through dynamic injection, mode switching, selective loading, and strategic compaction. ## Context Modes ### Dev Mode - Load: architecture docs, active code files, test files, recent changes - Skip: historical discussions, completed milestones, research notes - Priority: implementation speed ### Review Mode - Load: code diff, coding standards, security rules, test coverage - Skip: architecture docs, planning notes, research - Priority: thoroughness and accuracy ### Research Mode - Load: requirements, existing patterns, external research, alternatives - Skip: implementation details, test files, CI configs - Priority: breadth of information ## Dynamic Injection - Detect project context automatically (language, framework, tools) - Load relevant skills based on detected context - Inject domain-specific patterns and conventions - Adjust tool allowlists per context mode ## Selective Loading - Load only files relevant to the current task - Use glob patterns to scope file reading - Prioritize recently modified files - Skip binary files and generated code ## Strategic Compaction - Monitor context token usage - Suggest compression for resolved/completed items - Archive to memory files (activeContext, patterns, progress) - Pre-compaction state preservation - Automated compaction triggers at token thresholds ## Cross-Platform Detection - Package manager: npm (package-lock.json), pnpm (pnpm-lock.yaml), yarn (yarn.lock), bun (bun.lockb) - Language: TypeScript (tsconfig.json), Go (go.mod), Python (pyproject.toml), Java (pom.xml) - Test runner: vitest, jest, pytest, go test - CI/CD: GitHub Actions, Dockerfile, docker-compose ## When to Use - Session initialization (detect context) - Before each phase (inject relevant context) - Token budget warnings (strategic compaction) - Mode transitions (dev to review to research) ## Agents Used - Used by all agents indirectly through context detection - `context-engineering` agent for explicit compaction analysis
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