analyzing-ransomware-network-indicators
Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via Zeek conn.log and NetFlow analysis
66
58%
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-ransomware-network-indicators/SKILL.mdQuality
Discovery
82%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 a strong, highly specific description that clearly communicates concrete capabilities in ransomware network analysis with excellent domain-specific trigger terms. Its main weakness is the absence of an explicit 'Use when...' clause, which caps the completeness score. Adding trigger guidance would make this description excellent.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when investigating ransomware incidents, analyzing suspicious network traffic, or reviewing Zeek conn.log/NetFlow data for indicators of compromise.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: identifying C2 beaconing patterns, TOR exit node connections, data exfiltration flows, encryption key exchange, and specifies the tools/data sources (Zeek conn.log and NetFlow analysis). | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The when is only implied by the nature of the actions described. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords a security analyst would use: 'ransomware', 'C2 beaconing', 'TOR exit node', 'data exfiltration', 'encryption key exchange', 'Zeek conn.log', 'NetFlow'. These are highly specific and natural terms in the cybersecurity domain. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a very clear niche — ransomware network indicator analysis using specific tools (Zeek conn.log, NetFlow). Unlikely to conflict with other skills due to the narrow, specialized domain. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads more like a high-level procedure outline than an actionable skill file. It describes what to do at each step but provides zero executable code, no concrete thresholds or detection logic, and no example input/output data. The workflow sequence is logical but lacks the validation checkpoints and concrete implementation details needed for Claude to actually perform this analysis.
Suggestions
Add executable Python code for each step — at minimum for parsing Zeek conn.log TSV, computing beaconing interval statistics (with specific CV threshold, e.g., CV < 0.2), and checking IPs against a TOR exit node list.
Include a concrete example input snippet (e.g., 5-10 lines of Zeek conn.log) and the expected JSON output structure with actual field names and sample values.
Add validation checkpoints: e.g., after parsing verify record count, after beaconing detection verify results against known-good baseline, and define specific numeric thresholds for flagging (byte ratio > X, interval stddev < Y).
Either link to supplementary files for detailed implementations (e.g., 'See [BEACONING.md](BEACONING.md) for detection algorithm details') or inline the critical code — the current level of abstraction is not actionable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably structured but includes some unnecessary filler (e.g., the 'When to Use' section with generic bullet points that don't add value for Claude, and the overview paragraph restates what the steps already cover). Could be tightened. | 2 / 3 |
Actionability | The skill provides only abstract descriptions of steps with no executable code, no concrete commands, no example data formats, and no actual implementation. 'Parse Connection Logs' and 'Detect Beaconing Patterns' are described rather than instructed with specific code or tool invocations. | 1 / 3 |
Workflow Clarity | Steps are listed in a logical sequence, but there are no validation checkpoints, no error handling guidance, no feedback loops for when detection thresholds need tuning, and no concrete criteria for what constitutes a 'beaconing pattern' or 'unusually high outbound byte ratio'. | 2 / 3 |
Progressive Disclosure | The content has some structural organization with sections, but everything is inline with no references to supplementary files for detailed implementations, IOC lists, or example data. For a skill of this complexity, the lack of any linked resources or code files is a missed opportunity. | 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
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