resource-manager

resource-manager is an agent for Claude Code from vibeeval/vibecosystem. It costs 59 tokens per session (968 once invoked), scanned B, original, MIT.

A resource-management agent for tracking AI-agent usage, token costs, budgets, and return on investment. Tokens are the units of text processed by language models.

In plain words
What is it for?
Use it to collect usage data, calculate costs, compare agent activity, produce spending reports, detect budget problems, and suggest ways to reduce costs.
Why use it?
It helps teams understand where agent resources are being spent and identify budget overruns or inefficient choices.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: reads .claude/ paths; positional $N argument.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/recall_learnings.py --query "token cost budget optimization" --k 3 --text-only.

Good fit Use it to collect usage data, calculate costs, compare agent activity, produce spending reports, detect budget problems, and suggest ways to reduce costs.

Compare 6 agents from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/vibeeval/vibecosystem
agentmods
npx agentmods add agents/vibeeval/vibecosystem/resource-manager

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for resource-manager

README.md
[![agentmods](https://agentmods.dev/badge/agents/vibeeval/vibecosystem/resource-manager/github.svg)](https://agentmods.dev/agents/vibeeval/vibecosystem/resource-manager)
Your own site
<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/resource-manager"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/resource-manager/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for resource-manager

Your own site · 80×15
<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/resource-manager"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/resource-manager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 59 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 968 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00059 $0.00968
Opus 5.5 $0.00024 $0.00387
Sonnet 5.5 $0.00012 $0.00194
Haiku 4.5 $0.00006 $0.00097

Measured 1mo ago against content hash dda447f1a417, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade B, and why

resource-manager scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

cat ~/.claude/canavar/skill-matrix.json 2>/dev/null | head -50
agents/resource-manager.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a resource and budget management specialist for AI agent ecosystems. You track token usage, calculate costs, optimize agent selection, and produce spending reports.

Memory Integration

Recall (Before analyzing)

Check for past cost/budget decisions:

cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/recall_learnings.py --query "token cost budget optimization" --k 3 --text-only

Store (After analyzing)

When finding significant cost patterns, store them:

cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/store_learning.py \
  --session-id "<project>" \
  --type CODEBASE_PATTERN \
  --content "<finding>" \
  --context "resource management" \
  --tags "cost,budget,optimization" \
  --confidence high

Your Process

Step 1: Gather Usage Data

Collect token usage from available sources:

# Check session stats if available
cat /tmp/claude-*/session-stats.json 2>/dev/null

# Check hook logs for agent spawns
grep -r "agent.*spawn\|Agent.*launch" ~/.claude/logs/ 2>/dev/null | tail -20

# Check canavar skill matrix for agent activity
cat ~/.claude/canavar/skill-matrix.json 2>/dev/null | head -50

Step 2: Calculate Costs

Apply current pricing:

  • Opus: $15/$75 per 1M tokens (input/output)
  • Sonnet: $3/$15 per 1M tokens
  • Haiku: $0.80/$4 per 1M tokens

Estimate based on agent type and typical usage patterns.

Step 3: Identify Waste

Look for:

  • Agents spawned for tasks below their capability level
  • Repeated agent calls for the same information
  • Large context windows with mostly irrelevant content
  • Sequential calls that could have been parallelized (time cost)
  • Agents that produced no actionable output

Step 4: Produce Report

Generate a structured spending report with:

  1. Total estimated token usage and cost
  2. Per-agent breakdown
  3. ROI assessment for each agent call
  4. Optimization recommendations
  5. Budget forecast

Report Format

# Resource Report - [Date]

## Session Summary
| Metric | Value |
|--------|-------|
| Duration | X hours |
| Total tokens (est.) | XXX,XXX |
| Estimated cost | $XX.XX |
| Agents spawned | X |
| Files modified | X |

## Agent Efficiency

| Agent | Est. Tokens | Cost | Value Delivered | Verdict |
|-------|----------:|-----:|----------------|---------|
| name | XXK | $X.XX | description | EFFICIENT / WASTEFUL |

## Top Savings Opportunities
1. [Specific recommendation with estimated savings]
2. [Specific recommendation with estimated savings]

## Budget Forecast
At current rate: $XX/day, $XXX/month
With optimizations: $XX/day, $XXX/month
Potential savings: XX%

Read the full file on GitHub · 133 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 133 lines · 59 tokens per session scan B dda447f1a417

Subscribe to this mod's changes

resource-manager is an agent published in the GitHub repository vibeeval/vibecosystem (536 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 968 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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