dvcrn Intent Engineering
The Problem
Without an intent layer, agents optimize for what's measurable (fast response, no errors) rather than what matters (your actual priorities). The Klarna failure: AI saved $60M and destroyed customer loyalty because it optimized resolution time, not relationships.
What This Skill Installs
INTENT.md— YAML priority manifest at workspace rootlib/agent-context-loader.js— prepends intent summary to every subagent spawn- Routing integration — intent propagation flag in all routing decisions
Installation
Step 1 — Create INTENT.md
Copy references/intent-template.md to your workspace root as INTENT.md and edit:
cp $(dirname $0)/references/intent-template.md $OPENCLAW_WORKSPACE/INTENT.md
Or create it manually — see references/intent-template.md for the annotated schema.
Step 2 — Install agent-context-loader
Copy the reference implementation to lib/:
cp $(dirname $0)/references/agent-context-loader-template.js $OPENCLAW_WORKSPACE/lib/agent-context-loader.js
Verify it runs:
node $OPENCLAW_WORKSPACE/lib/agent-context-loader.js $OPENCLAW_WORKSPACE
Step 3 — Wire into spawn calls
In any subagent spawn, prepend the intent context to the task description:
const { prepareAgentContext } = require('./lib/agent-context-loader');
const { context } = prepareAgentContext(taskType, workspaceRoot);
const fullTask = context ? context + '\n\n---\n\n' + originalTask : originalTask;
// Use fullTask as your subagent task description
taskType is a string describing the work (e.g. "code_review", "research", "writing"). The loader extracts relevant context from INTENT.md and recent memory automatically.
Step 4 — Verify
Spawn a test subagent with a task that would normally trigger a tradeoff (cost vs quality, speed vs depth). Confirm the subagent's output reflects your priorities from INTENT.md.
INTENT.md Structure
| Field | Purpose |
|---|---|
optimization_priority |
Ordered priorities: primary → never_sacrifice |
tradeoffs |
Explicit rules when objectives conflict |
model_tier_intent |
Which task types get which model tier |
delegation_intent |
When to delegate vs handle inline |
quality_intent |
Per-domain quality standards |
See references/intent-template.md for the full annotated template.
How It Works
prepareAgentContext() reads INTENT.md fresh on every call, extracts a compact summary (≤200 chars), and prepends it to the spawn task. Subagents receive:
## Intent
Optimize for: user_value > honesty > cost. Never sacrifice: honesty, safety.
Delegate when blocking chat. Prefer depth for architecture/writing.
If INTENT.md is missing, a safe default is injected. The loader never throws — it degrades silently.
References
references/intent-template.md— Full annotated INTENT.md template with all fieldsreferences/agent-context-loader-template.js— Complete agent-context-loader.js implementation