kill-chain-analysis

kill-chain-analysis is a skill for Claude Code from MingyiSecLab/Mingyi-Atlas. It costs 27 tokens per session (1,404 once invoked), scanned A, a copy of kill-chain-analysis, Apache-2.0.

A decision procedure for analyzing security findings and choosing the next attack path during a red-team engagement. It uses the operational plan, previous findings, risk, and operational security impact to prioritize actions.

In plain words
What is it for?
Use it to interpret signs such as vulnerable web apps, directory services, exposed credentials, cloud mistakes, VPNs, or employee data, then select and prioritize the relevant testing path.
Why use it?
It helps turn reconnaissance results into an ordered set of possible next steps instead of choosing attack methods ad hoc.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents.

Good fit Use it to interpret signs such as vulnerable web apps, directory services, exposed credentials, cloud mistakes, VPNs, or employee data, then select and prioritize the relevant testing path.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mingyiseclab/mingyi-atlas/kill-chain-analysis
Install

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.

Any agent
npx skills add MingyiSecLab/Mingyi-Atlas --skill kill-chain-analysis
Clone the repo
git clone --depth 1 https://github.com/MingyiSecLab/Mingyi-Atlas

Made for: Claude Code.

Wrote 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.

agentmods badge for kill-chain-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/kill-chain-analysis/github.svg)](https://agentmods.dev/skills/mingyiseclab/mingyi-atlas/kill-chain-analysis)
Your own site
<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.

agentmods 80×15 button for kill-chain-analysis

Your own site · 80×15
<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>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,404 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash 52e4ab2be4cd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

Origin

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.

src/skills/standard/atlas/kill-chain-analysis/SKILL.md · 133 lines

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:

  1. What does the OPPLAN say? — Prioritized objectives drive decisions
  2. What do findings tell us? — Previous phase results constrain options
  3. What's the risk/reward? — Lower noise approaches first
  4. What's the OPSEC impact? — Consult opsec skill 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

Read the full file on GitHub · 133 lines

Changes

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.

  1. 10d ago First seen · 133 lines · 27 tokens per session scan A 52e4ab2be4cd

Subscribe to this mod's changes

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.