analyzing-heap-spray-exploitation
Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.
55
45%
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-heap-spray-exploitation/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 a well-defined niche of memory forensics. The main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The technical terminology is appropriate for the target audience and provides excellent distinctiveness.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about heap spray detection, memory forensics with Volatility, analyzing suspicious memory allocations, or investigating exploit attempts in memory dumps.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: detect heap spray attacks, analyze memory dumps, identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space. These are highly specific technical capabilities. | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific actions and tools, 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 user in this domain would use: 'heap spray', 'memory dumps', 'Volatility3', 'NOP sled', 'shellcode', 'landing zones', 'large allocations', 'virtual address space'. These are the exact terms a forensic analyst would use. | 3 / 3 |
Distinctiveness Conflict Risk | Extremely niche and specific to heap spray attack analysis using Volatility3. The combination of memory forensics, heap spray, NOP sleds, and shellcode makes this highly unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
7%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads like a high-level outline or table of contents rather than actionable guidance. It explains concepts Claude already knows (what heap spraying is, what NOP sleds are) while failing to provide the concrete commands, code, thresholds, and examples that would make it useful. Every step needs actual Volatility3 command-line invocations, Python code for pattern scanning, and concrete examples of output interpretation.
Suggestions
Replace each step's description with actual executable Volatility3 commands (e.g., `python3 vol.py -f memory.dmp windows.malfind --pid <PID>`) and Python code for pattern scanning.
Remove the explanation of what heap spraying is and the generic 'When to Use' section—Claude knows these concepts. Use the saved tokens for concrete examples.
Add a concrete expected output example showing the JSON schema with sample values (process names, memory addresses, region sizes, shellcode hashes).
Add validation checkpoints: e.g., verify the memory dump format first, check malfind output count before proceeding, define thresholds for 'suspicious' allocation sizes (e.g., >1MB contiguous RWX regions).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The overview explains what heap spraying is, which Claude already knows. The 'When to Use' section is generic boilerplate that adds no actionable value. The prerequisites list basic concepts Claude understands. Significant token waste throughout. | 1 / 3 |
Actionability | No executable commands, no code examples, no concrete Volatility3 plugin invocations. Each step is a vague description ('Use Volatility3 windows.malfind to scan...') rather than actual command-line syntax or Python code. The expected output section mentions JSON but provides no schema or example. | 1 / 3 |
Workflow Clarity | Steps are listed but lack any concrete detail, validation checkpoints, or error recovery. There's no guidance on what to do if malfind returns no results, no thresholds for 'large' allocations, and no feedback loops for iterative analysis. This is a multi-step forensic process with no validation steps. | 1 / 3 |
Progressive Disclosure | The content is organized into logical sections with headers, which provides some structure. However, there are no references to external files for detailed content (e.g., shellcode pattern databases, example outputs), and the overview section is too verbose while the steps are too thin—the balance is inverted. | 2 / 3 |
Total | 5 / 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
c15f73d
- Reviewed
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