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 onfire7777/universal-ai-skills-library --skill analyzing-cyber-kill-chaingit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote 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/onfire7777/universal-ai-skills-library/analyzing-cyber-kill-chain)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/analyzing-cyber-kill-chain"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/analyzing-cyber-kill-chain/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/onfire7777/universal-ai-skills-library/analyzing-cyber-kill-chain"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/analyzing-cyber-kill-chain.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 | $0.00099 | $0.01552 |
| Opus 5 | $0.00049 | $0.00776 |
| Sonnet 5 | $0.00020 | $0.00310 |
| Haiku 4.5 | $0.00010 | $0.00155 |
Grade A, and why
analyzing-cyber-kill-chain 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.
This is a copy
95% identical to analyzing-cyber-kill-chain — 44 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Cyber Kill Chain
When to Use
Use this skill when:
- Conducting post-incident analysis to determine how far an adversary progressed through an attack sequence
- Designing layered defensive controls with the goal of interrupting attacks at the earliest possible phase
- Producing threat intelligence reports that communicate attack progression to non-technical stakeholders
Do not use this skill as a standalone framework — combine with MITRE ATT&CK for technique-level granularity beyond what the 7-phase kill chain provides.
Prerequisites
- Complete incident timeline with forensic artifacts mapped to specific adversary actions
- MITRE ATT&CK Enterprise matrix for technique-level mapping within each kill chain phase
- Access to threat intelligence on the suspected adversary group's typical kill chain progression
- Post-incident report or IR timeline from responding team
Workflow
Step 1: Map Observed Actions to Kill Chain Phases
The Lockheed Martin Cyber Kill Chain consists of seven phases. Map all observed adversary actions:
Phase 1 - Reconnaissance: Adversary gathers target information before attack.
- Indicators: DNS queries from adversary IP, LinkedIn scraping, job posting analysis, Shodan scans of organization infrastructure
Phase 2 - Weaponization: Adversary creates attack tool (malware + exploit).
- Indicators: Malware compilation timestamps, exploit document metadata, builder artifacts in malware samples
Phase 3 - Delivery: Adversary transmits weapon to target.
- Indicators: Phishing emails, malicious attachments, drive-by downloads, USB drops, supply chain compromise
Phase 4 - Exploitation: Adversary exploits vulnerability to execute code.
- Indicators: CVE exploitation events in application/OS logs, memory corruption artifacts, shellcode execution
Phase 5 - Installation: Adversary establishes persistence on target.
- Indicators: New scheduled tasks, registry run keys, service installation, web shells, bootkits
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.
- 8d ago First seen · 131 lines · 99 tokens per session scan A 8c141078daea
analyzing-cyber-kill-chain is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 99 tokens to every session and 1,552 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to analyzing-cyber-kill-chain, differing in 44 lines, and is treated as a copy.
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