analyzing-docker-container-forensics
Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.
50
55%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/analyzing-docker-container-forensics/SKILL.mdQuality
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description is strong in specificity and distinctiveness, clearly carving out a niche around Docker container forensics with concrete artifact types listed. However, it lacks an explicit 'Use when...' clause that would help Claude know exactly when to select this skill, and could benefit from additional natural trigger terms that users might employ when seeking this capability.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user suspects a Docker container has been compromised, needs container forensics, or asks about investigating suspicious container activity.'
Include additional natural trigger terms like 'container forensics', 'docker security investigation', 'incident response', 'suspicious container', or 'container breach' to improve keyword coverage.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: analyzing images, layers, volumes, logs, and runtime artifacts, with the clear goal of identifying malicious activity and evidence. These are concrete, domain-specific capabilities. | 3 / 3 |
Completeness | Clearly answers 'what' (investigate compromised Docker containers by analyzing various artifacts), but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric. | 2 / 3 |
Trigger Term Quality | Includes good terms like 'Docker containers', 'compromised', 'images', 'layers', 'volumes', 'logs', and 'malicious activity', but misses common user variations like 'container forensics', 'incident response', 'docker security', 'container breach', or 'suspicious container'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of 'compromised Docker containers' with forensic analysis of specific Docker artifacts (images, layers, volumes, runtime artifacts) creates a very clear niche that is unlikely to conflict with general Docker skills or general security skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill excels at actionability with complete, executable code examples and a logical multi-step workflow for Docker container forensics. However, it is excessively verbose, explaining concepts Claude already understands and including large reference tables inline. The lack of progressive disclosure (no bundle files, no external references) and missing validation checkpoints in a workflow involving evidence preservation are notable weaknesses.
Suggestions
Remove the Key Concepts table, Tools & Systems table, and Common Scenarios section — these explain things Claude already knows or provide vague guidance that doesn't add actionable value.
Extract the Python analysis scripts and tool reference tables into separate bundle files (e.g., TOOLS.md, scripts/analyze_config.py) and reference them from the main skill.
Add explicit validation checkpoints after critical steps: verify evidence hashes after export, confirm tar extraction succeeded, validate that docker inspect output is non-empty before proceeding.
Trim the Prerequisites section to only list non-obvious tool requirements (dive, container-diff, trivy) rather than listing knowledge Claude already possesses.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~250+ lines with significant redundancy. The Key Concepts table explains things Claude already knows (what overlay2 is, what volume mounts are), the Tools & Systems table restates obvious tool purposes, and the Common Scenarios section is vague prose that adds little actionable value. The prerequisite section lists knowledge requirements Claude doesn't need to be told about. | 1 / 3 |
Actionability | The skill provides fully executable bash commands and Python scripts throughout all workflow steps. Commands are copy-paste ready with specific flags, output paths, and tool invocations. The Python analysis scripts are complete and functional, not pseudocode. | 3 / 3 |
Workflow Clarity | The 5-step workflow is clearly sequenced and logically ordered (preserve → analyze layers → host artifacts → filesystem changes → scan/report). However, there are no explicit validation checkpoints or feedback loops — for instance, no verification that evidence hashing succeeded, no check that container export completed properly, and no error recovery guidance for failed exports or scans. | 2 / 3 |
Progressive Disclosure | The content is a monolithic wall of text with no references to external files and no bundle structure. Everything is inlined — the Key Concepts table, Tools table, Common Scenarios, and Output Format could all be separate reference files. For a skill this long and complex, the lack of any content splitting is a significant organizational weakness. | 1 / 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
9a588e6
- Reviewed
Table of Contents
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