- name
- utility
- description
- |
- version
- 1.8.2
- triggers
- metadata
- {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/leyline", "emoji": "\�\�"}}
- source
- claude-night-market
- source_plugin
- leyline
Night Market Skill — ported from claude-night-market/leyline. For the full experience with agents, hooks, and commands, install the Claude Code plugin.
Utility Skill
Overview
A decision framework for agent orchestration based on Liu et al., "Utility-Guided Agent Orchestration for Efficient LLM Tool Use" (arXiv:2603.19896). Each candidate action is scored by subtracting weighted costs from expected gain, producing a single utility value that guides action selection. The framework prevents over-calling tools and premature stopping by making both errors costly. Utility range is [-2.3, 1.0].
When To Use
- Deciding whether to dispatch another agent or tool call
- Gating expensive tool calls (search, code execution, delegation)
- Selecting the right model tier for a sub-task
- Continuation decisions after receiving partial results
- Verification gating before writing or committing output
When NOT to Use
- Single-step operations with one obvious action
- Trivial tasks where cost of scoring exceeds benefit
- Already-committed actions that cannot be undone
Action Space
A = {respond, retrieve, tool_call, verify, delegate, stop}
| Action | Description |
|---|---|
| respond | Emit a final answer from current context |
| retrieve | Fetch additional information (search, read, lookup) |
| tool_call | Execute a tool (code runner, API, file write) |
| verify | Check a prior result for correctness or completeness |
| delegate | Spawn a sub-agent or hand off to a specialist |
| stop | Terminate the loop and return current state |
Utility Function
U(a | s_t) = Gain(a | s_t)
- λ₁ · StepCost(a | s_t)
- λ₂ · Uncertainty(a | s_t)
- λ₃ · Redundancy(a | s_t)| Parameter | Default | Rationale |
|---|---|---|
| λ₁ | 1.0 | Cost baseline; all other weights relative to this |
| λ₂ | 0.5 | Weak empirical correlation with outcome (r=0.0131) |
| λ₃ | 0.8 | Redundancy pruning yields ~10% token savings |
Utility range: [-2.3, 1.0]. Positive values indicate the action is worth taking. Values below the floor (-0.5 default) indicate the action should be skipped.
Termination Conditions
Stop the loop when any of the following is true:
- (a) Selected action is
stop - (b) Step budget exhausted (default: 10 steps)
- (c) All non-
stopactions score below the floor (default: -0.5)
High-gain override: If Gain >= 0.7 for any action, condition (c) may be overridden. Document the override and the gain value in your reasoning trace.
Quick Start
Minimal 4-step advisory pattern:
- Construct state -- gather task context per
modules/state-builder.md
- Score candidates -- evaluate each action in
Aper
modules/action-selector.md
- Prefer highest utility -- select the action with the
maximum U(a | s_t), subject to termination conditions
- Log score and decision -- record the winning action,
its utility value, and step count before executing
Detailed Resources
- State Builder:
modules/state-builder.md-- how to
populate s_t from task context
- Gain:
modules/gain.md-- estimating expected information
or progress gain
- Step Cost:
modules/step-cost.md-- token, latency, and
monetary cost tables
- Uncertainty:
modules/uncertainty.md-- confidence
estimation and calibration
- Redundancy:
modules/redundancy.md-- detecting duplicate
or low-delta actions
- Action Selector:
modules/action-selector.md-- scoring
loop and tie-breaking rules
- Integration:
modules/integration.md-- wiring utility
scoring into existing orchestration loops
Exit Criteria
- [ ] State constructed with task goal and prior steps
- [ ] All six actions scored before selecting one
- [ ] Termination condition checked after each step
- [ ] Score and decision logged for each step taken
- [ ] High-gain overrides documented with gain value