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/explaingit 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 | $0.02198 |
| Opus 5 | $0.00000 | $0.01099 |
| Sonnet 5 | $0.00000 | $0.00440 |
| Haiku 4.5 | $0.00000 | $0.00220 |
Grade A, and why
explain 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explain Function
Overview
Get a quick, structured explanation of what a decompiled function does -- its purpose, parameters, key API calls, data flow, and call context -- without running a full audit or trace pipeline.
The text after /explain specifies the function name and optionally the module:
/explain AiLaunchProcess-- searches all modules/explain appinfo.dll AiLaunchProcess-- targets specific module/explain appinfo.dll AiLaunchProcess --depth 2-- include callee code 2 levels deep/explain appinfo.dll --search LaunchProcess-- pattern search
If no function is specified, ask the user.
Default callee depth is 1 (direct callees only). Override with --depth N (0 = no callees, 2 = callees of callees, etc.). The script performs true BFS recursive traversal through the call chain, filtering boilerplate (WIL/CRT/ETW thunks) automatically.
IMPORTANT: Execution Model
This is an execute-immediately command. Do NOT present anything for user confirmation. Run and write the final explanation 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("explain", {"module": "<module>", "function": "<function>"}).
If validation fails, report the errors and stop. On success, use result.resolved["db_path"] for subsequent script calls.
- Locate the function
Quick lookup: Use
python .claude/skills/function-index/scripts/lookup_function.py <function_name>to locate the function across all modules instantly. Cross-dimensional search: When the search term might match a string, API call, or class name, usepython .claude/helpers/unified_search.py <db_path> --query <term>to search all dimensions at once. Otherwise, use the decompiled-code-extractor skill (find_module_db.pythenlist_functions.py --search) to resolve the module DB and exact function name.
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 · 142 lines · 0 tokens per session scan A 952f70b39701
explain 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 2,198 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.