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/marcosd4h/DeepExtractRuntimenpx agentmods add commands/marcosd4h/deepextractruntime/callgraphWrote 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/marcosd4h/deepextractruntime/callgraph)<a href="https://agentmods.dev/commands/marcosd4h/deepextractruntime/callgraph"><img src="https://agentmods.dev/badge/commands/marcosd4h/deepextractruntime/callgraph/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/marcosd4h/deepextractruntime/callgraph"><img src="https://agentmods.dev/badge/commands/marcosd4h/deepextractruntime/callgraph.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 | $0.01393 |
| Opus 5.5 | $0.00000 | $0.00557 |
| Sonnet 5.5 | $0.00000 | $0.00279 |
| Haiku 4.5 | $0.00000 | $0.00139 |
Grade A, and why
callgraph 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Call Graph
Overview
Build, query, and visualize call graphs for a module or function. Shows topology statistics, strongly connected components, hub functions, root/leaf nodes, and generates Mermaid diagrams.
Usage:
/callgraph appinfo.dll-- module-wide stats (node/edge counts, hubs, connectivity)/callgraph appinfo.dll --scc-- strongly connected components (recursive call clusters)/callgraph appinfo.dll --roots-- functions with no callers (entry points by graph structure)/callgraph appinfo.dll --leaves-- functions that call nothing (leaf/utility functions)/callgraph appinfo.dll AiLaunchProcess-- neighborhood graph for a specific function/callgraph appinfo.dll AiLaunchProcess --diagram-- Mermaid diagram of the function's call neighborhood/callgraph appinfo.dll --path FuncA FuncB-- find call path between two functions/callgraph appinfo.dll --reachable FuncA-- all functions reachable from FuncA
IMPORTANT: Execution Model
This is an execute-immediately command. Do NOT present anything for user confirmation. Run and write the call graph analysis straight to the chat as your response. The user expects to see the completed output.
Execution Context
IMPORTANT: Any inline Python that imports
helpers.*must run withcd <workspace>/.claude(so the.claude/directory is onsys.path), not from the workspace root. Script invocations likepython .claude/skills/.../script.pycan be run from the workspace root because those scripts manage their own path setup.
Steps
Step 0: Preflight Validation
Validate arguments using helpers.command_validation.validate_command_args("callgraph", {"module": "<module>"}).
If validation fails, report the errors and stop. On success, use result.resolved["db_path"] for subsequent script calls.
1. Find the module DB
Use the decompiled-code-extractor skill (find_module_db.py) to resolve the module name to its DB path.
Tip: All skill scripts support
--jsonfor machine-readable output. Use--jsonwhen parsing script output programmatically.
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 · 141 lines · 0 tokens per session scan A 5ebcbcf8601c
callgraph is a command published in the GitHub repository marcosd4h/DeepExtractRuntime (21 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,393 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 2026-08-30.
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