Mmcp.market

context-engineering skill

by a5c-ai·a5c-ai/babysitter·1.8k stars·MIT

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

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Is the context-engineering skill safe?

Clean: nothing in its files matched our rules. We read 2 files in the folder on 2026-09-28.

No findings.

Install the context-engineering skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/a5c-ai/babysitter.git /tmp/babysitter
mkdir -p ~/.claude/skills
cp -r /tmp/babysitter/library/methodologies/everything-claude-code/skills/context-engineering ~/.claude/skills/context-engineering
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

  • 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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