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 commands/marcosd4h/deepextractruntime/xrefgit clone --depth 1 https://github.com/marcosd4h/DeepExtractRuntimeWhat 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 | $0.00000 | $0.01174 |
| Opus 5 | $0.00000 | $0.00587 |
| Sonnet 5 | $0.00000 | $0.00235 |
| Haiku 4.5 | $0.00000 | $0.00117 |
Grade A, and why
xref 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.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Reference Lookup
Overview
Quick cross-reference lookup for a function: show who calls it (inbound xrefs) and what it calls (outbound xrefs) in a compact table format. Lightweight alternative to /audit for when you just need to see a function's immediate neighborhood.
Usage:
/xref appinfo.dll AiLaunchProcess-- show callers and callees/xref AiLaunchProcess-- auto-detect module/xref appinfo.dll AiLaunchProcess --depth 2-- show 2 levels of callers/callees/xref appinfo.dll --search "Check*"-- xrefs for functions matching a pattern
IMPORTANT: Execution Model
This is an execute-immediately command. Do NOT present anything for user confirmation. Run and write the cross-reference results 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("xref", {"module": "<module>", "function": "<function>"}).
If validation fails, report the errors and stop. On success, use result.resolved["db_path"] for subsequent script calls.
1. Locate the function
Tip: All skill scripts support
--jsonfor machine-readable output. Use--jsonwhen parsing script output programmatically.
Use the function-index skill for fast lookup:
python .claude/skills/function-index/scripts/lookup_function.py <function_name> --json
Or find the module DB first:
python .claude/skills/decompiled-code-extractor/scripts/find_module_db.py <module_name>
Once located, note function_id and db_path. Use --id <function_id> in all subsequent calls.
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 · 125 lines · 0 tokens per session scan A 872200f8ce22
xref is a command published in the GitHub repository marcosd4h/DeepExtractRuntime (20 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,174 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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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.