LaunchKit · 2026
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smart-context

AI & LLMsv1.0.0

Token-efficient agent behavior — response sizing, context pruning, tool efficiency, and delegation.

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by @joe3112

Setup & Installation

openclaw skills install @joe3112/smart-context

Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:

npx clawhub install smart-context

What This Skill Does

Instructs agents to match response length to question complexity, batch tool calls, and avoid loading files unnecessarily. Covers response sizing rules, context pruning, vision call costs, and delegating bulk work to cheaper sub-agent models.

Instead of applying uniform verbosity to every task, it uses a cost-aware decision tree that trims tokens on simple work while preserving depth where complexity demands it.

When to use it

  • Cutting API costs on high-volume agent pipelines
  • Batching parallel tool calls to reduce round-trips
  • Skipping memory lookups for simple greetings or status checks
  • Delegating bulk file formatting to a lighter model
  • Getting direct answers without preamble or restatements

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: Is the config file present?

AGENT
  1. 1Classifies question as yes/no type
  2. 2Determines a single targeted check is sufficient — no full file read needed
  3. 3Makes one tool call with a specific path lookup
  4. 4Skips confirmation read after the check
OUTPUT

Yes, config.json exists in the project root.