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 oyi77/1ai-skills --skill detecting-fileless-attacks-on-endpointsgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/detecting-fileless-attacks-on-endpoints)<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.
<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>- 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 | $0.00087 | $0.01046 |
| Opus 5 | $0.00044 | $0.00523 |
| Sonnet 5 | $0.00017 | $0.00209 |
| Haiku 4.5 | $0.00009 | $0.00105 |
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.
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()}
- Define Detection Scope — Identify the specific fileless attacks on endpoints techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
- Collect Baseline Data — Gather historical logs and establish normal behavior patterns for fileless attacks on endpoints.
- Build Detection Queries — Write detection rules, Sigma rules, or SIEM queries targeting fileless attacks on endpoints indicators.
- Execute Hunts — Run queries against the collected data, starting with broad filters and narrowing down.
- Triage Results — Investigate alerts, filter false positives, and validate findings against known-good behavior.
- Document Findings — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.
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.
- 7d ago First seen · 119 lines · 87 tokens per session scan A 2067196491ac
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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