# Context Engineering

Dynamic context injection, mode switching (dev/review/research), selective loading, and...

- Repository: https://github.com/a5c-ai/babysitter (1887 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add a5c-ai/babysitter --skill context-engineering -g -a claude-code -y`
Codex: `npx skills add a5c-ai/babysitter --skill context-engineering -g -a codex -y`
Cursor: `npx skills add a5c-ai/babysitter --skill context-engineering -g -a cursor -y`
Gemini CLI: `npx skills add a5c-ai/babysitter --skill context-engineering -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add a5c-ai/babysitter`

## Description

Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

- 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