building-automated-malware-submission-pipeline

building-automated-malware-submission-pipeline is a skill for Claude Code, Codex from RobotFlow-Labs/skills-repo. It costs 72 tokens per session (4,227 once invoked), scanned A, original, no licence file.

An automated pipeline for sending suspicious files from endpoints and email gateways to malware sandboxes and multiple scanning engines. It produces malware verdicts and indicators of compromise for SIEM systems, which collect and correlate security events.

In plain words
What is it for?
Use it to submit files for malware analysis, collect verdicts and indicators of compromise, and integrate results with a SIEM.
Why use it?
It reduces the manual work involved in checking large numbers of suspicious files. This helps security operations teams analyze threats at higher volume and pass findings into their monitoring tools.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to submit files for malware analysis, collect verdicts and indicators of compromise, and integrate results with a SIEM.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/robotflow-labs/skills-repo/building-automated-malware-submission-pipeline
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.

Any agent
npx skills add RobotFlow-Labs/skills-repo --skill building-automated-malware-submission-pipeline
Clone the repo
git clone --depth 1 https://github.com/RobotFlow-Labs/skills-repo

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for building-automated-malware-submission-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/robotflow-labs/skills-repo/building-automated-malware-submission-pipeline/github.svg)](https://agentmods.dev/skills/robotflow-labs/skills-repo/building-automated-malware-submission-pipeline)
Your own site
<a href="https://agentmods.dev/skills/robotflow-labs/skills-repo/building-automated-malware-submission-pipeline"><img src="https://agentmods.dev/badge/skills/robotflow-labs/skills-repo/building-automated-malware-submission-pipeline/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.

agentmods 80×15 button for building-automated-malware-submission-pipeline

Your own site · 80×15
<a href="https://agentmods.dev/skills/robotflow-labs/skills-repo/building-automated-malware-submission-pipeline"><img src="https://agentmods.dev/badge/skills/robotflow-labs/skills-repo/building-automated-malware-submission-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,227 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00072 $0.04227
Opus 5 $0.00036 $0.02114
Sonnet 5 $0.00014 $0.00845
Haiku 4.5 $0.00007 $0.00423

Measured 8d ago against content hash 1ecfff55f70b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

building-automated-malware-submission-pipeline 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 8d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

response = requests.get(
skills/building-automated-malware-submission-pipeline/SKILL.md · 487 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

3 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. 8d ago First seen · 487 lines · 72 tokens per session scan A 1ecfff55f70b

Subscribe to this mod's changes

building-automated-malware-submission-pipeline is a skill published in the GitHub repository RobotFlow-Labs/skills-repo (2 stars, last pushed 5mo ago), with no licence file. It adds 72 tokens to every session and 4,227 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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