agent-expenditure

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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.

AI & Automation 310 stars 27 forks Updated today MIT

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Skill Content

# 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...

Details

Author
athola
Repository
athola/claude-night-market
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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