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.
74
68%
Does it follow best practices?
Impact
Pending
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 names and abbreviations, and provides explicit 'Use when' guidance. The description is concise yet comprehensive, occupying a very distinct niche that minimizes conflict risk with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: parsing K8s audit logs, detecting exec-into-pod, secret access, RBAC modifications, privileged pod creation, anonymous API access, and building threat detection rules from audit event patterns. | 3 / 3 |
Completeness | Clearly answers both 'what' (parses K8s audit logs to detect specific threat patterns and builds detection rules) and 'when' (investigating Kubernetes cluster compromise or building k8s-specific SIEM detection rules) with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Kubernetes', 'audit logs', 'exec-into-pod', 'secret access', 'RBAC', 'privileged pod', 'cluster compromise', 'SIEM detection rules', 'k8s'. Good coverage of both full names and abbreviations. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche targeting Kubernetes API server audit logs specifically, with very specific triggers around K8s security events and SIEM rule building. Unlikely to conflict with general log analysis or other security 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 K8s audit logs, but only covers 2 of 5 listed detection categories with actual code. The workflow lacks sequencing, validation, and triage steps critical for a security investigation skill. Boilerplate sections like Prerequisites and When to Use add little value for Claude.
Suggestions
Add executable code examples for all 5 detection categories (clusterrolebindings creation, privileged pod creation, anonymous access) instead of just listing them.
Add a clear investigation workflow: 1) validate log format, 2) run detections, 3) correlate findings, 4) output structured results—with explicit validation checkpoints.
Remove the 'When to Use' and 'Prerequisites' sections entirely—Claude doesn't need to be told when to use the skill or that Python 3.8+ is required.
Add an example of structured output format (e.g., JSON alert schema) so Claude knows what detection results should look like when reporting findings.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 'When to Use' and 'Prerequisites' sections contain filler that Claude already knows (e.g., 'Familiarity with container security concepts', 'Python 3.8+ with required dependencies installed'). The core instructions are reasonably lean but the surrounding boilerplate wastes tokens. | 2 / 3 |
Actionability | The initial code snippet is executable and concrete for detecting pods/exec, and the secret enumeration example is useful. However, only 2 of the 5 listed detection categories have actual code—clusterrolebindings creation, privileged pod creation, and anonymous access detection are listed but lack any concrete implementation, leaving significant gaps. | 2 / 3 |
Workflow Clarity | There is no clear multi-step workflow for analyzing audit logs end-to-end. The skill lists detection categories but doesn't sequence them into an investigation process, provide validation steps (e.g., verifying log format, confirming findings), or describe what to do with detected events. For a security investigation skill, the absence of any verification or triage workflow is a significant gap. | 1 / 3 |
Progressive Disclosure | The content is organized into sections (When to Use, Prerequisites, Instructions, Examples) which provides some structure. However, the detection rules for all 5 categories could benefit from being expanded inline or split into a reference file. There are no references to supplementary materials for advanced detection patterns or SIEM rule output formats. | 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
888bbe4
- 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.