analyzing-kubernetes-audit-logs
Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat detection rules from audit event patterns. Use when investigating Kubernetes cluster compromise or building k8s-specific SIEM detection rules.
59
68%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/analyzing-kubernetes-audit-logs/SKILL.mdQuality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that clearly articulates specific capabilities (parsing K8s audit logs for five distinct threat patterns), includes rich natural trigger terms spanning both full and abbreviated forms, and provides explicit 'Use when' guidance. It occupies a well-defined niche that minimizes conflict risk with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: parses Kubernetes API server audit logs, detects exec-into-pod, secret access, RBAC modifications, privileged pod creation, anonymous API access, and builds threat detection rules from audit event patterns. | 3 / 3 |
Completeness | Clearly answers both what (parses K8s audit logs to detect specific threat patterns and build detection rules) and when ('Use when investigating Kubernetes cluster compromise or building k8s-specific SIEM detection rules') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Kubernetes', 'audit logs', 'exec-into-pod', 'secret access', 'RBAC', 'privileged pod', 'k8s', 'SIEM detection rules', 'cluster compromise'. Good coverage of both full names and abbreviations (Kubernetes/k8s). | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche targeting Kubernetes API server audit logs specifically, with domain-specific triggers like 'k8s', 'audit logs', 'RBAC', 'SIEM detection rules'. Very unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
37%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides a reasonable starting point with executable Python for parsing Kubernetes audit logs, but it only covers 1-2 of the 5 promised detection categories with actual code. The lack of a structured investigation workflow, validation steps, and completeness across detection types significantly limits its utility. Boilerplate prerequisites and 'When to Use' sections add little value for Claude.
Suggestions
Add executable code snippets for all 5 detection categories (RBAC escalation, privileged pod creation, anonymous access) instead of just listing them.
Define a clear multi-step investigation workflow with validation checkpoints, e.g.: 1) Load and validate log format, 2) Run detection passes, 3) Correlate findings, 4) Output structured report.
Remove the generic 'Prerequisites' and 'When to Use' sections or condense them to 1-2 lines—Claude doesn't need to be told about 'familiarity with container security concepts.'
Add a concrete output format example (e.g., JSON alert schema) so Claude knows what the final detection output should look like.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 'When to Use' and 'Prerequisites' sections contain generic filler (e.g., 'Familiarity with container security concepts,' 'Access to a test or lab environment') that Claude doesn't need. The core detection logic is reasonably lean, but the surrounding boilerplate wastes tokens. | 2 / 3 |
Actionability | The main code snippet is executable and concrete for parsing audit logs and detecting pod exec events. However, only 1 of the 5 listed detection categories has actual code; the others (RBAC escalation, privileged pod creation, anonymous access) are listed but lack concrete implementation, leaving significant gaps. | 2 / 3 |
Workflow Clarity | There is no defined multi-step workflow for investigating an incident or building detection rules. The skill lists detection categories and provides isolated code snippets but lacks sequencing, validation checkpoints, or any feedback loop for verifying findings or handling malformed log entries. | 1 / 3 |
Progressive Disclosure | The content is organized into sections (When to Use, Prerequisites, Instructions, Examples), which provides some structure. However, there are no references to external files for advanced topics like RBAC detection rules or privileged pod patterns, and the examples section is thin—content that could benefit from a separate reference file is simply absent rather than split. | 2 / 3 |
Total | 7 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- mukul975/Anthropic-Cybersecurity-Skills
- Commit
0f429d0
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.