Skill: athola/nm-conserve-agent-expenditure (version 1.0.2)

--- name: agent-expenditure description: | >- Track per-agent token usage and flag waste patterns in parallel dispatch workflows. Consult after running parallel agents to evaluate whether expenditure was proportional to value. Cross-references the plan-before-large-dispatch rule version: 1.9.5 triggers: - token-waste - agent-coordination - brooks-law - parallel-dispatch metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/conserve", "emoji": "\ud83e\udd9e", "requires": {"config": ["night-market.modules/waste-signals.md"]}}} source: claude-night-market source_plugin: conserve --- > **Night Market Skill** — ported from [claude-night-market/conserve](https://github.com/athola/claude-night-market/tree/master/plugins/conserve). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # Agent Token Waste Monitoring ## When To Use - After parallel agent dispatch completes - When evaluating whether to increase agent count - During retrospectives on agent-heavy workflows - When plan-before-large-dispatch rule triggers ## When NOT To Use - Single-agent workflows (no coordination overhead) - During active agent execution (post-hoc analysis) - For token budgeting (use token-conservation instead) ## Brooks's Law for Agents Dispatching more agents does not always help. Coordination overhead grows with agent count: | Agent Count | Expected Overhead | Guidance | |-------------|-------------------|----------| | 1-3 | Negligible | Dispatch freely | | 4-5 | 10-15% | Acceptable; plan first | | 6-8 | 20-30% | Monitor closely | | 9+ | 30%+ | Likely counterproductive | Coordination overhead is measured as shared-file conflicts: concurrent Read/Write operations on the same file by different agents, as a percentage of total agent runtime. ## Post-Dispatch Review Checklist After parallel agent runs, evaluate: 1. Did each agent produce unique findings? 2. Was total token expenditure proportional to value? 3. Did any agent duplicate another's work? 4. Would fewer agents have produced the same result? If 2+ questions answer no, reduce agent count in future dispatches of the same type. ## Waste Signals See `modules/waste-signals.md` for the 5 waste signal categories and detection criteria. ## Cross-References - `.claude/rules/plan-before-large-dispatch.md` for the 4+ agent planning requirement - `conserve:token-conservation` for session-level token budgeting - `conjure:agent-teams` for dispatch coordination

path: modules/waste-signals.md
content: ---
name: waste-signals
description: Definitions and detection criteria for 5 categories of
  agent token waste in parallel dispatch workflows.
parent_skill: conserve:agent-expenditure
category: resource-optimization
estimated_tokens: 250
---

# Waste Signal Definitions

## 1. Ghost Agent

An agent that consumes tokens without producing actionable output.

**Detection criteria** (ALL must be true):
- Token expenditure >1.5x median for task type
- Findings count <30% of median for agent type
- Findings lack evidence citations (no code refs, no line numbers, no
  file paths)

**Exception**: Zero-finding results from low-risk scans
(e.g., security audit of already-linted code) are valid, not waste.

## 2. Redundant Reader

An agent that re-reads files already loaded by another agent in the
same dispatch.

**Detection**: Compare file access logs across agents. If agent B
reads the same files as agent A and produces overlapping findings,
agent B's reads were redundant.

**Mitigation**: Assign distinct file scopes to each agent before
dispatch.

## 3. Duplicate Worker

An agent whose findings overlap >50% with another agent's output.

**Detection**: Compare finding descriptions across agents. Semantic
overlap (same issue described differently) counts as duplication.

**Mitigation**: Assign distinct review dimensions (e.g., one agent
reviews security, another reviews performance).

## 4. Token Hog

An agent that exceeds 3x the median token count for its task type
without proportional output.

**Detection**: Compare agent token usage to historical median for the
same task type. If output quality or quantity does not justify the
excess, flag as waste.

**Mitigation**: Set token budgets per agent. Use haiku for
well-scoped tasks (see conjure cheapest-capable model selection).

## 5. Coordination Overhead

When N > 5 agents and shared-file conflicts exceed 20% of total agent
runtime.

**Detection**: Count concurrent Read/Write operations on the same file
by different agents, as percentage of total runtime.

**Mitigation**: Reduce agent count, use git worktrees for isolation,
or assign non-overlapping file scopes.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)