Borrowing it
Nothing to install: this file belongs to Micro-Evaluation-Group/pwndbg-lldb-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Micro-Evaluation-Group/pwndbg-lldb-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/Micro-Evaluation-Group/pwndbg-lldb-mcpWrote 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/instructions/micro-evaluation-group/pwndbg-lldb-mcp/claude-md)<a href="https://agentmods.dev/instructions/micro-evaluation-group/pwndbg-lldb-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/micro-evaluation-group/pwndbg-lldb-mcp/claude-md/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/instructions/micro-evaluation-group/pwndbg-lldb-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/micro-evaluation-group/pwndbg-lldb-mcp/claude-md.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 | $0.01172 | $0.01172 |
| Opus 5 | $0.00586 | $0.00586 |
| Sonnet 5 | $0.00234 | $0.00234 |
| Haiku 4.5 | $0.00117 | $0.00117 |
Grade A, and why
pwndbg-lldb-mcp 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 9d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Layout
This is an unusual codebase: nearly all server logic lives in a single file, pwndbg_lldb_mcp.py (~5400 lines). It exposes 146 @mcp.tool() functions plus three @mcp.resource() endpoints. There is no src/ package — adding a tool means adding another decorated function in this file.
pwndbg_lldb_mcp.py— entire MCP servertest_callbacks.py— unit tests (no LLDB required; uses pipes to mock the PTY)test_improvements.py— integration tests (requireslldbon PATH; uses/usr/bin/trueas a target)docs/— Sphinx sources;docs/api.rstautogenerates from docstrings viaautomodule.github/workflows/docs.yml— builds and publishes docs to GitHub Pages on every push tomain
Common Commands
uv sync # install deps into .venv/
.venv/bin/python pwndbg_lldb_mcp.py # run the MCP server (stdio transport)
.venv/bin/python pwndbg_lldb_mcp.py --debug # with debug logging to stdout
.venv/bin/python test_callbacks.py # run unit tests (no LLDB needed)
.venv/bin/python test_improvements.py # run integration tests (needs lldb on PATH)
make -C docs html # build docs locally (requires `pip install -e ".[docs]"`)
There is no test runner config (no pytest/unittest discovery). Each test file is a standalone script with its own main() and asyncio.run(...). Run a single test by editing the test list in main() or by importing and calling the test function directly.
Architecture
PTY-based communication. The server does not use the LLDB Python API. Instead, each PwndbgSession spawns lldb as a subprocess attached to a pty.openpty() pair, writes commands to the master fd, and reads until one of PROMPT_PATTERNS ((pwndbg-lldb), pwndbg>, pwndbg-lldb>, (lldb)) appears in the buffer. This means:
- Output parsing is text-scraping, not structured. Tool implementations typically return raw debugger output verbatim.
- All I/O is non-blocking with a 30-second
COMMAND_TIMEOUT. Commands that can take longer (e.g.run) need atimeout_overridepassed toexecute_command_with_progress. - pwndbg is loaded via
command script import "<pwndbg_path>"if a path was provided to the session; otherwise the session runs plain LLDB.
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.
- 9d ago First seen · 53 lines · 1,172 tokens per session scan A 448f085c0dfd
pwndbg-lldb-mcp CLAUDE.md is an instructions file published in the GitHub repository Micro-Evaluation-Group/pwndbg-lldb-mcp (1 stars, last pushed 25d ago), licensed MIT. It adds 1,172 tokens to every session, about $0.0059 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.
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