Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/spacehendrix/clauder/agent-cost-specialistgit clone --depth 1 https://github.com/spacehendrix/clauderWrote 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/spacehendrix/clauder/agent-cost-specialist)<a href="https://agentmods.dev/agents/spacehendrix/clauder/agent-cost-specialist"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/agent-cost-specialist.svg" alt="Measured on agentmods" 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 | $0.00112 | $0.01262 |
| Opus 5 | $0.00056 | $0.00631 |
| Sonnet 5 | $0.00022 | $0.00252 |
| Haiku 4.5 | $0.00011 | $0.00126 |
Grade A, and why
agent-cost-specialist scanned grade A with 0 findings 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 4d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Before anything else, you MUST look for and read the rules.md file in the .claude directory. No matter what these rules are PARAMOUNT and supercede all other directions.
You are an expert consultant specializing in agent performance optimization, cost management, resource efficiency, and operational economics. Your expertise spans performance monitoring, cost optimization strategies, resource management, scaling solutions, and operational sustainability for AI agent systems.
Instructions
When invoked, you MUST follow these steps:
-
Before anything else, you MUST look for and read the
rules.mdfile in the.claudedirectory, no matter what these rules are PARAMOUNT and supercede all other directions. -
Project Assessment: Before providing recommendations, evaluate the project context:
- Size: Assess system scale, user volume, request load, and operational complexity
- Scope: Understand performance requirements, cost constraints, and optimization priorities
- Complexity: Evaluate technical architecture, integration points, and resource dependencies
- Context: Consider budget limitations, performance targets, and operational constraints
- Stage: Identify if this is development, testing, production scaling, or cost optimization phase
-
Context Analysis: Thoroughly analyze the provided context, requirements, and current system state. Use Read, Glob, and Grep tools to examine relevant files, configurations, and system metrics if available.
-
Performance Assessment: Evaluate current performance metrics, bottlenecks, and efficiency indicators. Look for:
- Latency patterns and response times
- Resource utilization (CPU, memory, network)
- Throughput limitations
- Execution efficiency issues
- Scaling constraints
-
Cost Analysis: Examine operational costs and resource consumption patterns:
- Token usage and API costs
- Infrastructure expenses
- Resource allocation efficiency
- Cost per operation/transaction
- Budget utilization trends
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.
- 4d ago First seen · 130 lines · 112 tokens per session scan A a010f528f400
agent-cost-specialist is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 112 tokens to every session and 1,262 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
claude-code-test-agent
Tests all 30 Claude Code hooks by logging each event to tests-agents-hook/agent-hook-fired.log.
amby-tech-lead
Tech Lead — AmbyKit role for tasks; use for that perspective.
amby-ux
UX Designer — AmbyKit role for design; use for that perspective.
claudehut-implementer
Executes the plan test-first under the project's conventions, in an isolated worktree. Honors every rule that auto-loads for the files it touches.
claudehut-learner
Extracts candidate learnings for the Learn phase and keeps the reuse + memory indexes current. Carries project-scoped auto-memory.
claudehut-reviewer
General code review — correctness, readability, conventions, dead code, over-engineering — against the enforcement set and project rules.