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.
git clone --depth 1 https://github.com/OZmasterAI/Torus-FrameworkWrote 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/commands/ozmasterai/torus-framework/graph)<a href="https://agentmods.dev/commands/ozmasterai/torus-framework/graph"><img src="https://agentmods.dev/badge/commands/ozmasterai/torus-framework/graph/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/commands/ozmasterai/torus-framework/graph"><img src="https://agentmods.dev/badge/commands/ozmasterai/torus-framework/graph.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 | $0.00000 | $0.01238 |
| Opus 5.5 | $0.00000 | $0.00495 |
| Sonnet 5.5 | $0.00000 | $0.00248 |
| Haiku 4.5 | $0.00000 | $0.00124 |
Grade A, and why
graph 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 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.
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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/graph — Query the Code Graph
Answer any codebase question using the toroidal-indexer code graph. One tool call, compact result — no grep/find/glob.
Usage
/graph <question> # natural language query
/graph what calls calculateWeeklyAIRewards # callers query
/graph blast radius of jwt-auth.ts # impact analysis
/graph path from Scheduler to AIUsageLog # shortest call chain
/graph hubs # most-connected nodes
/graph clusters # community overview
How It Works
All indexer tools route through toolshed MCP: run_tool("indexer", "<tool>", {args}).
The primary tool is code_query — it does BFS/DFS graph traversal server-side and returns a compact text summary of nodes + edges within a token budget. One call answers most questions.
Step 1 — Resolve project name
PROJECT = basename of current working directory
Step 2 — Read project context via MCP resource (optional, ~150 tokens)
Before querying, read the compact project summary to understand graph size and top hubs:
ReadMcpResourceTool("indexer://project/PROJECT/context")
For cluster overview (~300 tokens): ReadMcpResourceTool("indexer://project/PROJECT/clusters")
For hub details (~200 tokens): ReadMcpResourceTool("indexer://project/PROJECT/hubs")
These replace reading the full GRAPH_REPORT.md (~2000 tokens). Only read resources when you need orientation — skip for targeted queries where you already know what to look for.
Step 3 — Call code_query (default for most questions)
run_tool("indexer", "code_query", {
"project": "PROJECT",
"question": "the user's question or key terms",
"depth": 2,
"budget": 2000
})
Returns pre-formatted text: scored seed nodes → BFS traversal → NODES list with file:line → EDGES list with relationship types. All in ~1500-2000 tokens.
Seed selection uses hybrid BM25 + vector similarity search. Semantic queries like "authentication flow" will find JWT/login symbols even without exact substring matches.
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 · 121 lines · 0 tokens per session scan A 7c4bb7a4f47f
graph is a command published in the GitHub repository OZmasterAI/Torus-Framework (5 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,238 tokens. 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.
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