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.
git clone --depth 1 https://github.com/vibeeval/vibecosystemnpx agentmods add agents/vibeeval/vibecosystem/resource-managerWrote 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.
[](https://agentmods.dev/agents/vibeeval/vibecosystem/resource-manager)<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.
<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>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.
| Model | Per session | Once 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 |
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 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:
- Total estimated token usage and cost
- Per-agent breakdown
- ROI assessment for each agent call
- Optimization recommendations
- 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%
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.
- 1mo ago First seen · 133 lines · 59 tokens per session scan B dda447f1a417
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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