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Agent Expenditure

Community
athola
agent-expenditure

Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.

Overview

Publisherathola
Repositoryclaude-night-market
Skill nameagent-expenditure
Stars
343
Forks
37
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by athola on GitHub. Read the source before you install it.

Installation

Install the Agent Expenditure AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/athola/claude-night-market/tree/master/plugins/conserve/skills/agent-expenditure
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/athola/claude-night-market.git /tmp/claude-night-market
mkdir -p .claude/skills
cp -r /tmp/claude-night-market/plugins/conserve/skills/agent-expenditure .claude/skills/agent-expenditure
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Expenditure in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Expenditure on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Expenditure is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 CountExpected OverheadGuidance
1-3NegligibleDispatch freely
4-510-15%Acceptable; plan first
6-820-30%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

  • Dispatching 4 or more agents needs an agreed plan first: the roster, each agent's scope, and the output contract it returns. 1-3 agents may dispatch directly.
  • conserve:token-conservation for session-level token budgeting
  • conjure:agent-teams for dispatch coordination

Exit Criteria

  • All 4 post-dispatch review questions answered with explicit yes/no per agent (unique findings, proportional expenditure, no duplication, fewer agents sufficient)
  • Waste signals from modules/waste-signals.md checked against the completed run; any triggered signal named with the category
  • A recommendation is produced: either "reduce agent count to N" or "dispatch was efficient" with coordination overhead percentage
  • If 2+ review questions answer no, a concrete agent-count reduction is stated for future dispatches of the same type

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Expenditure AI skill do?

Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.

Why use Agent Expenditure on TypingMind?

Because you install it once and use it with any model. Agent Expenditure is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Expenditure in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/athola/claude-night-market/tree/master/plugins/conserve/skills/agent-expenditure. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Expenditure?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Expenditure?

As many as you like. As long as a model supports skills, you can use Agent Expenditure with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Expenditure AI skill free?

Yes. It is published on GitHub by athola under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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