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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/google/mantisnpx agentmods add skills/google/mantis/mantis-calibrateWrote 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-calibrate)<a href="https://agentmods.dev/skills/google/mantis/mantis-calibrate"><img src="https://agentmods.dev/badge/skills/google/mantis/mantis-calibrate/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-calibrate"><img src="https://agentmods.dev/badge/skills/google/mantis/mantis-calibrate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector 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 | $0.00052 | $0.09989 |
| Opus 5.5 | $0.00021 | $0.03996 |
| Sonnet 5.5 | $0.00010 | $0.01998 |
| Haiku 4.5 | $0.00005 | $0.00999 |
Grade A, and why
mantis-calibrate 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 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.
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 — 667 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Risk Calibrator (/mantis-calibrate)
System Goal
Risk Analysis Expert. Evaluates confirmed findings against a rigorous risk matrix, taking into account successful reproduction and production viability to produce a final risk score (1-10).
Command Definition
- Command:
/mantis-calibrate - Description: Calibrates the risk level of findings based on evidence and impact.
Input/Output Contract
- Reads:
workspace/findings/*.json(all finding files to load full pipeline state).workspace/kb/THREAT_MODEL.md(if exists, to check threat boundary overrides and asset criticality).workspace/.mantis_state.json(to track the current loop pass, and to readactive_snapshot—{snapshot_id, snapshot_pinned, root}— for finding provenance and history stamping; absent ⇒ degraded/today's behavior).
- Writes:
- Updates finding files in-place with scoring/calibration fields
(
impact_score,likelihood_score,availability_tier,inferred_exposure,attacker_position,mantis_risk_score,priority,sanity_triage_applied(may begin withSTALE_EVIDENCEwhen the STALE-EVIDENCE guard suppresses heuristics),calibration_checklist(entries may carrySTALE_EVIDENCE:reasons),outrage_commentary,executive_summary). Appends ahistoryentry withsnapshotprovenance (see history JSON template in the body). - Reusable helper script
workspace/helpers/append_calibrate.py.
- Updates finding files in-place with scoring/calibration fields
(
- Preconditions:
- Confirmed or raw findings must exist in
workspace/findings/.
- Confirmed or raw findings must exist in
- Idempotency Guarantee:
- Updates findings in place by overwriting existing keys with the calculated score. Running multiple times on the same inputs yields identical outputs, with no duplicated entries.
Instructions
Convert the raw security findings and their empirical results (repro/patch) into a prioritized, actionable risk report.
Execute the calibration as follows:
Locator resolution (findings-only). Calibrate never needs to read target source to compute a score, but some heuristics below may re-inspect code; resolve the code root the same way every stage does. Block A is inlined below:
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 667 lines · 52 tokens per session scan A c4eb14c114e3
mantis-calibrate is a skill published in the GitHub repository google/mantis (2,525 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 9,989 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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