mantis-summarize

name	mantis_summarize
description	Pre-processes the repository by generating security-focused summaries (mantis_summary.md) for each directory to make planning and research more efficient. Use when starting a review campaign to map the codebase before threat modeling and planning. Don't use for executing code reviews, writing test scripts, or patching code.

Summarizer (/mantis_summarize)

System Goal

Repository Mapper. Automates the generation of security-focused, deterministic summaries of directory contents to reduce token overhead for downstream planning and research stages.

Command Definition

• Command: /mantis_summarize
• Description: Pre-processes the repository by generating security-focused summaries (mantis_summary.md) for each directory to make planning and research more efficient.

Instructions

Your task is to write and execute a script that will traverse the repository directory tree and create a mantis_summary.md file in each directory containing source code.

This is an optional pre-processing phase designed to drastically reduce the context window size required for the strategist (/mantis_plan), and provide a quick reference map for researchers (/mantis_researcher) and threat model generator (/mantis_threat_model).

Execute the summarize stage as follows:

1. Write the Traversal Script (Bottom-Up Hierarchical): Write a script (e.g., Python or bash) in your workspace that walks the repository directory tree using a bottom-up (post-order) traversal.

   • The script must ignore non-source-code directories such as node_modules, vendor, .git, build outputs, and tests/.
   • By traversing bottom-up, the script ensures that subdirectories are summarized before their parent directories.
   • When analyzing a directory, the script should pass the LLM the local source files in that directory PLUS the mantis_summary.md files of its immediate subdirectories. Do not pass the raw source files of subdirectories to the parent.
   • When analyzing very large directories, context window size might become a problem. Instead of passing files and directory summaries in bulk, generate per-file summaries or operate in more efficient chunks to avoid passing too many tokens for the LLM to handle.

2. Generate the Security Summary (Map-Reduce): The script should read historical_learnings.jsonl (if it exists) to check for past vulnerabilities and security fixes associated with files in the current directory, and pass them in context. The script should instruct the LLM or agent tool to generate a concise, security-focused summary of the directory. To keep token lengths reasonable at higher levels of the directory tree, the LLM should abstract away lower-level details, focusing on the rolled-up architecture. The prompt used by your script should ask for:

   • Core Components: What are the primary files and subdirectories, and what do they do?
   • API Endpoints & Exports: What functions or classes are exposed to other modules?
   • Trust Boundaries & External Inputs: Does this directory handle untrusted data, network requests, or user input?
   • Sensitive Operations: Are there parsers, cryptographic functions, or memory management operations?
   • Historical Vulnerabilities & Fixes: What files or components in this directory have historical vulnerabilities or security-related fixes recorded in historical_learnings.jsonl? Summarize the past fixes, components affected, and vulnerability classes to highlight past regressions or recurring weaknesses.

   The summary must be a reasonable size to incorporate into work on larger problems, so aim for several thousand words or fewer.

3. Output to mantis_summary.md: The script must write the resulting summary into a file named mantis_summary.md inside the corresponding directory. Overwrite it if it already exists.

4. Execute the Script: Run the script you just wrote to generate all the summaries across the repository. Wait for it to finish successfully.

When complete, notify the user.