analyzing-indicators-of-compromise

analyzing-indicators-of-compromise is a skill for Claude Code from oyi77/1ai-skills. It costs 107 tokens per session (1,055 once invoked), scanned A, original, MIT.

A guide for assessing indicators of compromise, such as suspicious IP addresses, domains, URLs, file hashes, and email artifacts.

In plain words
What is it for?
It helps triage phishing and security alerts, enrich threat data, assign confidence, and support incident investigations.
Why use it?
It helps security teams turn raw alert data into an informed estimate of whether activity is malicious and how urgently it should be blocked.

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 It helps triage phishing and security alerts, enrich threat data, assign confidence, and support incident investigations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/oyi77/1ai-skills/analyzing-indicators-of-compromise
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 analyzing-indicators-of-compromise
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 analyzing-indicators-of-compromise

README.md
[![agentmods](https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-indicators-of-compromise/github.svg)](https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-indicators-of-compromise)
Your own site
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-indicators-of-compromise"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-indicators-of-compromise/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 analyzing-indicators-of-compromise

Your own site · 80×15
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-indicators-of-compromise"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-indicators-of-compromise.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,055 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 $0.00107 $0.01055
Opus 5 $0.00053 $0.00528
Sonnet 5 $0.00021 $0.00211
Haiku 4.5 $0.00011 $0.00105

Measured 8d ago against content hash 94d771acaf17, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

analyzing-indicators-of-compromise 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 8d 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/analyzing-indicators-of-compromise/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyzing Indicators Of Compromise

Overview

Cybersecurity skill for analyzing indicators of compromise. Follows industry best practices and security standards.

When to Use

Trigger phrases:

  • "analyzing indicators of compromise"
  • "A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requi"
  • "Automated feeds deliver bulk IOCs that need confidence scoring before ingestion"
  • "An incident investigation requires contextual enrichment of observed network art"

Use this skill when:

  • A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage
  • Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls
  • An incident investigation requires contextual enrichment of observed network artifacts

Do not use this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).

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

  • VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup
  • AbuseIPDB API key for IP reputation checks
  • MISP instance or TIP for cross-referencing against known campaigns
  • Python with requests and vt-py libraries, or SOAR platform with pre-built connectors

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. Scope the Analysis — Define what indicators of compromise artifacts or data sources to examine and the investigation timeline.
  2. Preserve Evidence — Create forensic copies of relevant data. Maintain chain of custody documentation.
  3. Extract Key Indicators — Parse and extract relevant indicators of compromise data points from collected artifacts.
  4. Correlate Findings — Cross-reference extracted data with other sources (threat intel, logs, timelines).
  5. Build Timeline — Construct a chronological sequence of events related to indicators of compromise.
  6. Document Analysis — Write findings report with evidence, conclusions, and recommendations.

Read the full file on GitHub · 123 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. 8d ago First seen · 123 lines · 107 tokens per session scan A 94d771acaf17

Subscribe to this mod's changes

analyzing-indicators-of-compromise is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 107 tokens to every session and 1,055 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 2026-09-04.

Related

Other skills, from other repositories

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

mukul975/Anthropic-Cybersecurity-Skills · 106 tokens

analyzing-indicators-of-compromise

A guide for investigating indicators of compromise, or signs such as suspicious IP addresses, domains, URLs, file fingerprints, and email addresses. It combines information from several security-intelligence services.

killvxk/cybersecurity-skills-zh · 111 tokens

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

26zl/cybersec-toolkit · 106 tokens

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

plurigrid/asi · 106 tokens

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

balsm-health/Balsm-AI · 106 tokens

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

pinkpixel-dev/skills-collection-1 · 106 tokens