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 MingyiSecLab/Mingyi-Atlas --skill kill-chain-analysisgit clone --depth 1 https://github.com/MingyiSecLab/Mingyi-AtlasWrote 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/mingyiseclab/mingyi-atlas/kill-chain-analysis)<a href="https://agentmods.dev/skills/mingyiseclab/mingyi-atlas/kill-chain-analysis"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/kill-chain-analysis/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/mingyiseclab/mingyi-atlas/kill-chain-analysis"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/kill-chain-analysis.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 | $0.00027 | $0.01404 |
| Opus 5 | $0.00014 | $0.00702 |
| Sonnet 5 | $0.00005 | $0.00281 |
| Haiku 4.5 | $0.00003 | $0.00140 |
Grade A, and why
kill-chain-analysis 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 10d 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
91% identical to kill-chain-analysis — 3 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kill Chain Analysis & Attack Path Decision-Making
Decision Framework
When selecting the next action, evaluate in order:
- What does the OPPLAN say? — Prioritized objectives drive decisions
- What do findings tell us? — Previous phase results constrain options
- What's the risk/reward? — Lower noise approaches first
- What's the OPSEC impact? — Consult
opsecskill before noisy actions
Findings Analysis
After Recon Phase — Selecting Attack Vectors
Read recon/ outputs and categorize:
| Finding Type | Indicates | Next Action |
|---|---|---|
| Web apps with known CVEs | Web exploitation path | exploit → web techniques |
| AD services (88/389/636) | AD attack surface | exploit → AD techniques (after initial access) |
| Exposed credentials (OSINT) | Credential-based access | exploit → credential stuffing/spray |
| Cloud misconfigs (S3/blob) | Cloud attack path | exploit → cloud-specific techniques |
| VPN/remote access services | Network perimeter entry | exploit → VPN/RDP exploitation |
| Employee emails + breach data | Social engineering path | exploit → phishing (if in scope) |
Attack Vector Prioritization
Rank available vectors by:
Score = (Success Probability × Impact) / Detection Risk
1. Valid credentials from OSINT → High prob, High impact, Low noise
2. Known web CVE (public exploit) → High prob, Med impact, Med noise
3. AD misconfiguration (no patch) → Med prob, High impact, Med noise
4. Password spray against O365 → Med prob, High impact, High noise
5. Zero-day or custom exploit → Low prob, High impact, Low noise
Always prefer: credentials > misconfigurations > known CVEs > brute force
After Exploitation — Deciding Post-Exploit Strategy
Once a foothold is established, analyze:
| Context | Decision |
|---|---|
| Low-privilege user on workstation | Prioritize: privesc → cred dump → lateral to server |
| Service account on server | Prioritize: cred dump (may have cached admin creds) → lateral |
| Domain user credentials | Prioritize: AD enumeration → Kerberoasting → DCSync path |
| Local admin on single host | Prioritize: cred dump → check for cached domain creds → lateral |
| Already domain admin | Prioritize: objective completion → evidence collection → reporting |
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.
- 10d ago First seen · 133 lines · 27 tokens per session scan A 52e4ab2be4cd
kill-chain-analysis is a skill published in the GitHub repository MingyiSecLab/Mingyi-Atlas (11 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 1,404 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to kill-chain-analysis, differing in 3 lines, and is treated as a copy.
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