SKILLS/analyzing-indicators-of-compromise/SKILL.md
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 multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.
npx skillsauth add pinkpixel-dev/skills-collection-1 analyzing-indicators-of-compromiseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when:
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).
requests and vt-py libraries, or SOAR platform with pre-built connectorsBefore enriching, classify each IOC:
evil[.]com), extract registered domain via tldextractDefang IOCs in documentation (replace . with [.] and :// with [://]) to prevent accidental clicks.
VirusTotal (file hash, URL, IP, domain):
import vt
client = vt.Client("YOUR_VT_API_KEY")
# File hash lookup
file_obj = client.get_object(f"/files/{sha256_hash}")
detections = file_obj.last_analysis_stats
print(f"Malicious: {detections['malicious']}/{sum(detections.values())}")
# Domain analysis
domain_obj = client.get_object(f"/domains/{domain}")
print(domain_obj.last_analysis_stats)
print(domain_obj.reputation)
client.close()
AbuseIPDB (IP addresses):
import requests
response = requests.get(
"https://api.abuseipdb.com/api/v2/check",
headers={"Key": "YOUR_KEY", "Accept": "application/json"},
params={"ipAddress": "1.2.3.4", "maxAgeInDays": 90}
)
data = response.json()["data"]
print(f"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}")
MalwareBazaar (file hashes):
response = requests.post(
"https://mb-api.abuse.ch/api/v1/",
data={"query": "get_info", "hash": sha256_hash}
)
result = response.json()
if result["query_status"] == "ok":
print(result["data"][0]["tags"], result["data"][0]["signature"])
Query MISP for existing events matching the IOC:
from pymisp import PyMISP
misp = PyMISP("https://misp.example.com", "API_KEY")
results = misp.search(value="evil-domain.com", type_attribute="domain")
for event in results:
print(event["Event"]["info"], event["Event"]["threat_level_id"])
Check Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).
Apply a tiered decision framework:
Record findings in TIP/MISP with:
Export to STIX indicator object with confidence field set appropriately.
| Term | Definition |
|------|-----------|
| IOC | Indicator of Compromise — observable network or host artifact indicating potential compromise |
| Enrichment | Process of adding contextual data to a raw IOC from multiple intelligence sources |
| Defanging | Modifying IOCs (replacing . with [.]) to prevent accidental activation in documentation |
| False Positive Rate | Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds |
| Sinkhole | DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely |
| TTL | Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days |
testing
When the user wants a full ASO health audit, review their App Store listing quality, or diagnose why their app isn't ranking. Also use when the user mentions "ASO audit", "ASO score", "why am I not ranking", "listing review", or "optimize my app store page". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
testing
Clarify requirements before implementing. Use when serious doubts arise.
tools
Complete reference and build guide for ASI:One (ASI1) — the AI platform by Fetch.ai built for agentic, Web3-native applications. Use this skill IMMEDIATELY and ALWAYS when the user mentions ASI1, ASI:One, Fetch.ai AI API, building with ASI1, integrating ASI:One, asking about ASI1 models, tool calling with ASI1, ASI1 image generation, ASI1 agentic LLM, Agentverse, uagents, Agent Chat Protocol, structured output with ASI1, or OpenAI-compatible wrappers for ASI1. Also trigger when the user says things like "use ASI1 instead of OpenAI", "build an app with ASI:One", "ASI1 API", or references docs.asi1.ai. This skill covers everything needed to build production apps - setup, all models, all API features, tool calling, image gen, agentic orchestration, structured data, session management, streaming, LangChain integration, uagents / Agent Chat Protocol, and TypeScript/Node.js patterns.
data-ai
When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.