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Mantis Summarize

OrganizationPopular
google
mantis-summarize

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.

Overview

Publishergoogle
Repositorymantis
Skill namemantis-summarize
Stars
1.6K
Forks
154
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

Install the Mantis Summarize AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google/mantis.git /tmp/mantis
mkdir -p .claude/skills
cp -r /tmp/mantis/mantis-summarize .claude/skills/mantis-summarize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mantis Summarize in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mantis Summarize on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mantis Summarize is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.
  • Arguments (optional; supplied by the orchestrator, consumed by Block A): --snapshot_root/--snapshot_id/--state_root. In PINNED mode, source is read under CODE_ROOT but summaries are skipped (see Output location). All absent → MODE-OFF (in-tree summaries, as today).

Input/Output Contract

  • Reads:
    • workspace/.mantis_state.json (to track current loop pass).
    • Codebase directories and source files (excluding node_modules, vendor, .git, build outputs, and tests/).
    • Child directory summaries (mantis-summary.md files from subdirectories).
    • workspace/historical_learnings.jsonl (optional, to enrich summaries).
  • Writes:
    • Traversal script to workspace.
    • MODE-OFF: mantis-summary.md in each source directory (as today). PINNED: skipped (see Output location).
  • Preconditions:
    • Source files and directory structure must be present.
  • Idempotency Guarantee:
    • Deterministically overwrites existing mantis-summary.md files in-place with updated rollups.

Instructions

Step 0: Locator Resolution + output location (run first)

LOCATOR RESOLUTION (before reading ANY target code or artifact):
0. ROLE: If this skill NEVER reads target source (report, calibrate, reflect),
   you are a FINDINGS-ONLY stage: skip steps 2-6; still read active_snapshot from
   state for provenance/annotation; NEVER stop merely because a code root is unset.
1. Determine CODE_ROOT, in this priority order:
   a. If --target_root is passed on THIS invocation, CODE_ROOT = --target_root.
      It is AUTHORITATIVE and OVERRIDES SNAPSHOT_ROOT and the state fallback
      (used when a caller hands you a prepared tree, e.g. a patched shadow).
   b. Else if --snapshot_root (or SNAPSHOT_ROOT) is passed, use it.
   c. Else read state_root/workspace/.mantis_state.json (state_root from
      --state_root if passed, else ./workspace/... relative to the current dir)
      -> active_snapshot.root / .snapshot_id / .snapshot_pinned.
   d. Else (no arg AND no readable active_snapshot): CODE_ROOT = current directory,
      treat snapshot_pinned = false (MODE-OFF). Do NOT stop.
2. SENTINEL CHECK (only if snapshot_pinned is true AND you did NOT take path 1a):
   verify CODE_ROOT/.mantis_snapshot_id exists and equals SNAPSHOT_ID. If missing
   or different -> STOP "snapshot sentinel mismatch". (A --target_root tree (1a) is
   deliberately mutated and is sentinel-EXEMPT.)
3. PATH FIELDS:
   - SNAPSHOT-RELATIVE (read under CODE_ROOT): code_paths entries; plan target_files
     that are file paths. Strip ONLY a trailing ":<digits>". A code_paths entry
     containing "://" is a URL/endpoint, NOT a file read. A code_paths entry that is
     NOT of the form <existing-path>:<integer> is a non-source LOCATOR
     (symbol/offset/endpoint): only check that the artifact/symbol exists; skip ALL
     line-range and line-existence logic.
   - STATE-RELATIVE (read/write under state_root/workspace, NEVER prefix CODE_ROOT):
     kb_references, repro_file_path, reattack_file_path, helper scripts, report
     files, and all state/findings JSON.
4. Never WRITE under CODE_ROOT when snapshot_pinned is true. Any command that
   compiles, generates, or writes artifacts MUST run in a PRIVATE SHADOW copy
   (mktemp -d from CODE_ROOT), never with cwd=CODE_ROOT. Read-only inspection may
   cd into CODE_ROOT.
5. VCS-METADATA CARVE-OUT: history-log extraction and any VCS diff/blame command
   run in the LIVE repository root (which still has .git/.hg/.repo), NOT CODE_ROOT
   (the snapshot copy strips VCS metadata). Do NOT stop merely because CODE_ROOT
   lacks .git/.hg/.repo.
6. Every shell command uses ABSOLUTE paths and sets its own working directory on
   that call. Do NOT assume the working directory persists between calls.

Output location (MANDATORY):

  • PINNED mode (snapshot_pinned true): summaries are skipped this pass. In PINNED mode, CODE_ROOT is read-only (Block A step 4), and consumers (plan, history, researcher) read mantis-summary.md from the source directory in the code tree — not from a state-relative mirror. Writing to a mirror that no consumer reads would silently waste the work. Do NOT write any mantis-summary.md files in PINNED mode. (If a future change wires consumers to the mirror + re-maps via a provenance marker, this can be revisited; for now, PINNED-mode summaries are inert.)
  • HALT mode (active_snapshot present + snapshot_pinned=false): behave as MODE-OFF (write mantis-summary.md into each source directory). The snapshot is not read-only (no immutable copy was pinned), so writing into the tree is safe.
  • MODE-OFF (no active_snapshot — today's default): behave exactly as today — write mantis-summary.md into each source directory.
  • In all modes except PINNED, mantis-summary.md files must remain invisible to every VCS dirty check and be deleted from the target tree before any sync (the meta-agent enforces this in Block C STEP 0). Never let a summary make the tree look dirty.

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).

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 workspace/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 workspace/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: In MODE-OFF (or HALT), write mantis-summary.md into the corresponding source directory (overwrite if present). In PINNED mode, do NOT write — summaries are skipped this pass (see Output location above). Never write into the read-only snapshot.

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

  5. Complete: Summaries are now generated. Notify the user.

When complete, notify the user.

Frequently asked questions

What does the Mantis Summarize AI skill do?

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.

Why use Mantis Summarize on TypingMind?

Because you install it once and use it with any model. Mantis Summarize is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mantis Summarize in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/mantis/tree/main/mantis-summarize. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mantis Summarize?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mantis Summarize?

As many as you like. As long as a model supports skills, you can use Mantis Summarize with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mantis Summarize AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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