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.
60
51%
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 trigger terms.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about heap spray detection, memory dump analysis for exploitation artifacts, or investigating suspicious memory allocations with Volatility3.'
| 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 distinctive and unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
20%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a high-level outline rather than actionable guidance. It lacks any executable commands, code examples, or concrete patterns that would enable Claude to actually perform heap spray analysis. The content wastes tokens explaining concepts Claude already knows while omitting the specific technical details (exact CLI commands, Python scripts, byte pattern matching code, output schemas) that would make it useful.
Suggestions
Add concrete Volatility3 CLI commands for each step, e.g., `vol -f dump.raw windows.malfind --pid <PID>` with expected output snippets.
Include executable Python code for NOP sled pattern scanning, such as regex or byte-matching against extracted memory regions with specific signatures (0x90*N, 0x0c0c0c0c).
Remove the overview paragraph explaining what heap spraying is and the generic 'When to Use' bullets—replace with a one-line purpose statement.
Add a concrete JSON output schema or example showing the expected report structure, and include validation criteria for distinguishing true heap spray from benign large allocations.
| 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. Prerequisites list basic knowledge Claude possesses. Significant token waste throughout. | 1 / 3 |
Actionability | No executable code, no concrete commands, no specific Volatility3 CLI invocations, no code examples for pattern scanning. Every step is a vague description ('Use Volatility3 windows.malfind to scan...') rather than an actual command or script. The expected output mentions JSON but provides no schema or example. | 1 / 3 |
Workflow Clarity | Steps are listed in a logical sequence (identify processes → analyze VADs → scan for patterns → extract shellcode), but there are no validation checkpoints, no error handling, no feedback loops for false positives, and no concrete criteria for what constitutes 'suspicious' at each step. | 2 / 3 |
Progressive Disclosure | The content is organized into sections with headers, but everything is inline with no references to external files for detailed procedures. The content is short enough that external references aren't strictly needed, but the lack of any concrete detail means there's nothing to progressively disclose—it's all surface-level. | 2 / 3 |
Total | 6 / 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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