sast-bandit

A Python security scanner that examines source code with Bandit. It finds risky coding patterns and connects findings to CWE and OWASP categories.

In plain words
What is it for?
Scanning Python code for hardcoded secrets, SQL injection, command injection, insecure APIs, and other vulnerabilities, then producing classified security reports.
Why use it?
It helps catch common security mistakes in Python before the code runs in production. Reports with severity levels make the findings easier to review.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/agentsecops/secopsagentkit/sast-bandit
Any agent
npx skills add AgentSecOps/SecOpsAgentKit --skill sast-bandit
Clone the repo
git clone --depth 1 https://github.com/AgentSecOps/SecOpsAgentKit

Made for: Claude Code, Codex.

Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,536 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00097 $0.02536
Opus 5 $0.00048 $0.01268
Sonnet 5 $0.00019 $0.00507
Haiku 4.5 $0.00010 $0.00254

Measured 3d ago against content hash 2136f3e3934b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sast-bandit 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 3d 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.

skills/appsec/sast-bandit/SKILL.md · 306 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

5 files 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.

Changes

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.

  1. 3d ago First seen · 306 lines · 97 tokens per session scan A 2136f3e3934b

Subscribe to this mod's changes

sast-bandit is a skill published in the GitHub repository AgentSecOps/SecOpsAgentKit (202 stars, last pushed 4mo ago), with no licence file. It adds 97 tokens to every session and 2,536 once invoked, about $0.0005 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 2026-08-30.

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