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 instructions/atriumn/tokencost-dev/claude-mdgit clone --depth 1 https://github.com/atriumn/tokencost-devWhat 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 | $0.00288 | $0.00288 |
| Opus 5 | $0.00144 | $0.00144 |
| Sonnet 5 | $0.00058 | $0.00058 |
| Haiku 4.5 | $0.00029 | $0.00029 |
Grade A, and why
tokencost-dev CLAUDE.md 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 2d 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.
What it actually says
tokencost — LLM Pricing Oracle MCP Server
Overview
Lightweight MCP server that pulls real LLM pricing data from LiteLLM's community-maintained registry and exposes it as tools any MCP-compatible client can query.
Architecture
- TypeScript, ESM (
"type": "module") @modelcontextprotocol/sdkwith stdio transport- Data source: LiteLLM
model_prices_and_context_window.json - Fuzzy model name matching via
fuse.js
File Structure
src/index.ts— Server setup, stdio transport, handler wiringsrc/tools.ts— Tool definitions (inputSchema) + executeTool dispatchsrc/pricing.ts— Fetch, cache, normalize LiteLLM datasrc/search.ts— Fuzzy model name matching
Tools Exposed
get_model_details— Look up pricing/capabilities for a modelcalculate_estimate— Estimate cost for a given token countcompare_models— Filter and compare models by provider/context/moderefresh_prices— Force re-fetch pricing data
Build & Run
npm run build
npm start
Cache
- In-memory with 24h TTL
- Disk fallback at
.cache/prices.json - Background refresh on startup
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.
- 2d ago First seen · 34 lines · 288 tokens per session scan A a955f42a8965
tokencost-dev CLAUDE.md is an instructions file published in the GitHub repository atriumn/tokencost-dev (1 stars, last pushed 7d ago), licensed MIT. It adds 288 tokens to every session, about $0.0014 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 2026-08-31.
Other instructions, from other repositories
Assistant CLAUDE.md
Instructions for bearlike/Assistant, covering agents guide — mewbo, mandatory: hydrate before touching files, what mewbo is, monorepo layering — read before adding a module and engineering principles.
Assistant AGENTS.md
Instructions for bearlike/Assistant: This is a shim file for external agents.
ai AGENTS.md
AGENTS.md instructions for vercel/ai, covering agents.md, project overview, repository structure, key directories and core package dependencies.
GPT-RAG release.instructions.md
Instructions for Azure/GPT-RAG, a project described as: Sharing the learning along the way we been gathering to enable Azure OpenAI at enterprise scale in a secure manner. GPT-RAG core is a Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large…
llm-context.py CLAUDE.md
Instructions for cyberchitta/llm-context.py, covering claude.md, working notes (gitignored) and draining the field notes.
seekstone CLAUDE.md
Instructions for shaqmughal/seekstone, covering claude.md, what this repo is, commands, the harness itself (run after npm install) and architecture.