detecting-fileless-attacks-on-endpoints

detecting-fileless-attacks-on-endpoints is a skill for Claude Code from oyi77/1ai-skills. It costs 87 tokens per session (1,046 once invoked), scanned A, original, MIT.

A security-analysis guide for finding malware that runs in a computer's memory instead of leaving files on disk. It covers PowerShell attacks, code injected into other processes, WMI abuse, and malware stored in the Windows registry.

In plain words
What is it for?
Use it to create endpoint detections, investigate suspicious in-memory activity, and configure Sysmon, AMSI, and PowerShell logging.
Why use it?
It helps detect attacks that traditional antivirus may miss because there is no ordinary malicious file to scan.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the 1ai-skills plugin — 209 skills, 4 commands shipped together

Good fit Use it to create endpoint detections, investigate suspicious in-memory activity, and configure Sysmon, AMSI, and PowerShell logging.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/oyi77/1ai-skills/detecting-fileless-attacks-on-endpoints
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 oyi77/1ai-skills --skill detecting-fileless-attacks-on-endpoints
Clone the repo
git clone --depth 1 https://github.com/oyi77/1ai-skills

Made for: Claude Code.

Or install 1ai-skills, the plugin that ships this one along with the rest of its 209 skills, 4 commands.

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 detecting-fileless-attacks-on-endpoints

README.md
[![agentmods](https://agentmods.dev/badge/skills/oyi77/1ai-skills/detecting-fileless-attacks-on-endpoints/github.svg)](https://agentmods.dev/skills/oyi77/1ai-skills/detecting-fileless-attacks-on-endpoints)
Your own site
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/detecting-fileless-attacks-on-endpoints"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/detecting-fileless-attacks-on-endpoints/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 detecting-fileless-attacks-on-endpoints

Your own site · 80×15
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/detecting-fileless-attacks-on-endpoints"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/detecting-fileless-attacks-on-endpoints.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,046 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original 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 $0.00087 $0.01046
Opus 5 $0.00044 $0.00523
Sonnet 5 $0.00017 $0.00209
Haiku 4.5 $0.00009 $0.00105

Measured 7d ago against content hash 2067196491ac, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

detecting-fileless-attacks-on-endpoints 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 7d 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.

cybersecurity/_deprecated/detecting-fileless-attacks-on-endpoints/SKILL.md · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Detecting Fileless Attacks On Endpoints

Overview

Cybersecurity skill for detecting fileless attacks on endpoints. Follows industry best practices and security standards.

When to Use

Trigger phrases:

  • "detecting fileless attacks on endpoints"
  • "Building detection rules for fileless malware that operates entirely in memory"
  • "Hunting for PowerShell-based attacks, reflective DLL injection, and WMI abuse"
  • "Configuring endpoint telemetry (Sysmon, AMSI, PowerShell logging) to capture fil"

Use this skill when:

  • Building detection rules for fileless malware that operates entirely in memory
  • Hunting for PowerShell-based attacks, reflective DLL injection, and WMI abuse
  • Configuring endpoint telemetry (Sysmon, AMSI, PowerShell logging) to capture fileless indicators
  • Investigating incidents where traditional AV found no malicious files

Do not use for detecting file-based malware or for malware reverse engineering.

When NOT to Use

  • When you lack proper authorization for testing
  • For production systems without change management
  • When the task requires legal or compliance expertise beyond technical scope

Prerequisites

  • Sysmon with process creation and WMI event logging enabled
  • PowerShell Script Block Logging and Module Logging enabled
  • AMSI (Antimalware Scan Interface) enabled for script content inspection
  • EDR with behavioral detection capabilities (MDE, CrowdStrike, SentinelOne)

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
  1. Define Detection Scope — Identify the specific fileless attacks on endpoints techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
  2. Collect Baseline Data — Gather historical logs and establish normal behavior patterns for fileless attacks on endpoints.
  3. Build Detection Queries — Write detection rules, Sigma rules, or SIEM queries targeting fileless attacks on endpoints indicators.
  4. Execute Hunts — Run queries against the collected data, starting with broad filters and narrowing down.
  5. Triage Results — Investigate alerts, filter false positives, and validate findings against known-good behavior.
  6. Document Findings — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

Read the full file on GitHub · 119 lines

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. 7d ago First seen · 119 lines · 87 tokens per session scan A 2067196491ac

Subscribe to this mod's changes

detecting-fileless-attacks-on-endpoints is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 87 tokens to every session and 1,046 once invoked, about $0.0004 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 2026-09-04.

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