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 26zl/cybersec-toolkit --skill collecting-indicators-of-compromisegit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/collecting-indicators-of-compromise)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/collecting-indicators-of-compromise"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/collecting-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/26zl/cybersec-toolkit/collecting-indicators-of-compromise"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/collecting-indicators-of-compromise.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00088 | $0.02784 |
| Opus 5 | $0.00044 | $0.01392 |
| Sonnet 5 | $0.00018 | $0.00557 |
| Haiku 4.5 | $0.00009 | $0.00278 |
Grade A, and why
collecting-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 9d 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.
This is a copy
100% identical to collecting-indicators-of-compromise — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Collecting Indicators of Compromise
When to Use
- During active incident response to identify and block adversary infrastructure
- Post-incident to document all observed adversary artifacts for future detection
- When sharing threat intelligence with ISACs, sector partners, or law enforcement
- When building detection rules in SIEM, EDR, or network security tools
- When enriching IOCs with threat intelligence context for risk scoring
Do not use for behavioral TTP analysis without accompanying technical indicators; use MITRE ATT&CK mapping for behavioral characterization.
Prerequisites
- Access to incident evidence sources: SIEM logs, EDR telemetry, memory dumps, disk images, network captures
- Threat intelligence platform (MISP, OpenCTI, ThreatConnect) for IOC management and sharing
- IOC enrichment tools: VirusTotal, OTX (AlienVault Open Threat Exchange), Shodan, DomainTools
- STIX 2.1 knowledge for structured IOC representation
- Sharing agreements with relevant ISACs (FS-ISAC, H-ISAC, IT-ISAC) or sector partners
Workflow
Step 1: Identify IOC Categories
Collect indicators across all categories from incident evidence:
Network Indicators:
- IP addresses (C2 servers, staging servers, exfiltration destinations)
- Domain names (C2 domains, phishing domains, DGA domains)
- URLs (malware download, C2 check-in, exfiltration endpoints)
- JA3/JA3S hashes (TLS client/server fingerprints)
- User-Agent strings (custom or unusual HTTP headers)
- DNS query patterns (tunneling signatures, DGA patterns)
Host Indicators:
- File hashes (MD5, SHA-1, SHA-256 of malware, tools, scripts)
- File paths (known malware installation directories)
- Registry keys (persistence mechanisms, configuration storage)
- Scheduled tasks and service names (persistence)
- Mutex/event names (malware instance synchronization)
- Named pipes (C2 communication channels, e.g., Cobalt Strike)
Email Indicators:
- Sender addresses and domains (spoofed or attacker-controlled)
- Subject lines and body content patterns
- Attachment names and hashes
- Embedded URLs
- Email header anomalies (SPF/DKIM/DMARC failures)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 273 lines · 88 tokens per session scan A c6b4fefdd2bd
collecting-indicators-of-compromise is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed today), licensed MIT. It adds 88 tokens to every session and 2,784 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to collecting-indicators-of-compromise, differing in 0 lines, and is treated as a copy.
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