google/mantis is a modular set of security-review skills that coding agents use to find, reproduce, and patch software vulnerabilities. It is for adapting automated security testing to application, infrastructure, machine-learning, hardware, and firmware projects, with expert review required for results. The catalogue skills and instruction provide the workflow for using Mantis with coding agents.
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 skills add google/mantis --skill mantis-advisegit clone --depth 1 https://github.com/google/mantisWrote 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/skills/google/mantis/mantis-advise)<a href="https://agentmods.dev/skills/google/mantis/mantis-advise"><img src="https://agentmods.dev/badge/skills/google/mantis/mantis-advise/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/skills/google/mantis/mantis-advise"><img src="https://agentmods.dev/badge/skills/google/mantis/mantis-advise.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
- Socket pass
- Snyk pass
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 | $0.00070 | $0.01977 |
| Opus 5.5 | $0.00028 | $0.00791 |
| Sonnet 5.5 | $0.00014 | $0.00395 |
| Haiku 4.5 | $0.00007 | $0.00198 |
Grade A, and why
mantis-advise scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
`subprocess.run(["cmd", arg])` without `shell=True`. How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Advisor & Remediation Engine (/mantis-advise)
System Goal
Proactive Secure Development & Architectural Remediation Engine. Functions as a security guardrail, advisory assistant, and architectural remediator for developers and coding agents. Queries Mantis threat models, historical vulnerability lineages, verified remediation patterns, triaged false positives, and learned trajectory invariants to ensure that new code and refactors are implemented securely from the start, and to synthesize robust architectural remediations for confirmed vulnerabilities.
Command Definition
- Command:
/mantis-advise - Description: Queries security knowledge for a given target file or module, evaluates proposed changes against known threat boundaries, and provides verified secure implementation guidance or architectural remediation plans.
- Execution Command:
python3 "${MANTIS_HOME:-/path/to/mantis}/reference/scripts/advise.py" --file <target_file> [--db knowledge.db] - Arguments (optional):
--file/-f(or--target/-t): Target source file or component path (e.g.src/auth.pyorapi/app.py). Defaults to repo-wide scope if omitted.--remediate/-r: Finding ID, lineage UUID, or file to generate an architectural remediation dossier and verification plan for.--db/-d: Path to Mantis SQLite database (default: auto-discoversknowledge.dborworkspace/knowledge.db).--lineage/-l: Query lifecycle and recurrence for a specific lineage UUID.--signature/-s: Query lifecycle for a specific content signature hash.--json: Emit structured JSON output instead of formatted markdown.--full: Emit unabridged OKF markdown bodies and un-truncated diffs.
How to Fetch Guidance
All Mantis knowledge (threat models, historical findings, verified patches,
triaged false positives, and learned invariants) lives in the SQLite database
(knowledge.db). Do not look for flat files on disk (like learnings.jsonl or
workspace/findings/*.json). Use one of the two execution doors below:
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.
- 1mo ago Changed · +45 lines · +3 tokens per session 54346a95c066
- 1mo ago First seen · 180 lines · 67 tokens per session scan A 026e82fe6573
mantis-advise is a skill published in the GitHub repository google/mantis (2,525 stars, last pushed today), licensed Apache-2.0. It adds 70 tokens to every session and 1,977 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
ai-agent-evaluation-benchmarking
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
ai-agent-tool-routing
Use when designing or reviewing an AI agent tool registry or MCP router: document parameter schemas, caller authorization, execution limits, replay-safe retries and verification evidence.
jobs
Manage agy staffer background jobs - collect results, check status, cancel, follow-up conversation, and setup. Use when an agy job needs collecting, when the user asks "is the agy job done", "show agy's result", "cancel the agy job", "continue the agy conversation", or "set up agy". This is the orchestrator's skill…
agy-lead
Orchestrate an ongoing task with AGY while the current agent owns key decisions, review, and delivery. Use when the user invokes /agy-lead or asks you to coordinate a task using AGY.
researcher
Delegate a deep research or survey task to Google's Antigravity CLI (agy staffer, fast Gemini). Use when the user says /agy:researcher, "ask agy to research", "have the agy staffer survey X", or wants a second, independent deep-dive on a topic or codebase without spending the host model's quota.