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 analyzing-indicators-of-compromisegit 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/analyzing-indicators-of-compromise)<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.
<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>- 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 | $0.00107 | $0.01055 |
| Opus 5 | $0.00053 | $0.00528 |
| Sonnet 5 | $0.00021 | $0.00211 |
| Haiku 4.5 | $0.00011 | $0.00105 |
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.
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
requestsandvt-pylibraries, 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()}
- Scope the Analysis — Define what indicators of compromise artifacts or data sources to examine and the investigation timeline.
- Preserve Evidence — Create forensic copies of relevant data. Maintain chain of custody documentation.
- Extract Key Indicators — Parse and extract relevant indicators of compromise data points from collected artifacts.
- Correlate Findings — Cross-reference extracted data with other sources (threat intel, logs, timelines).
- Build Timeline — Construct a chronological sequence of events related to indicators of compromise.
- Document Analysis — Write findings report with evidence, conclusions, and recommendations.
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.
- 8d ago First seen · 123 lines · 107 tokens per session scan A 94d771acaf17
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.
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…
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.
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…
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…
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…
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…