Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Adds a machine-readable intent layer to OpenClaw agents by installing an INTENT.md priority manifest and wiring it into every subagent spawn. Each subagent receives a compact summary of your optimization priorities before executing its task. Addresses the pattern where agents optimize measurable proxies instead of actual goals.
Rather than relying on prompt-level reminders that get lost across subagent spawns, this installs a persistent context layer that propagates priorities automatically to every agent in the chain.
When to use it
- Preventing subagents from optimizing speed at the expense of output quality
- Setting explicit cost vs depth tradeoffs for different task types
- Ensuring research agents prioritize depth over quick answers
- Configuring which model tier handles architecture decisions vs routine tasks
- Establishing delegation rules so agents know when to escalate vs proceed
Example Workflow
Here's how your AI assistant might use this skill in practice.
User sets up a new OpenClaw workspace and wants subagents to prioritize user value over cost
- 1Copies intent-template.md to workspace root as INTENT.md
- 2Edits optimization_priority, tradeoffs, and model_tier_intent fields to reflect actual goals
- 3Installs agent-context-loader.js into lib/ directory
- 4Wraps existing subagent spawn calls to prepend intent context using prepareAgentContext()
- 5Spawns a test subagent with a cost-vs-quality tradeoff task to verify priorities are reflected in output
Subagents receive an intent header on every spawn and make tradeoff decisions aligned with the configured priorities