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.
44
45%
Does it follow best practices?
Impact
—
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 for a niche forensic analysis skill. It excels at naming concrete actions and using domain-appropriate trigger terms that analysts would naturally use. Its only weakness is the absence of an explicit 'Use when...' clause, which would help Claude know precisely when to select this skill.
Suggestions
Add a 'Use when...' clause, e.g., 'Use when the user asks about heap spray detection, memory forensics, NOP sled analysis, or shellcode identification 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, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The when is only implied by the nature of the actions described. Per rubric guidelines, missing 'Use when' caps completeness at 2. | 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 specific niche: heap spray attack detection in memory dumps using Volatility3. This is unlikely to conflict with other skills due to the highly specialized domain and specific tooling mentioned. | 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 is essentially a high-level outline rather than actionable guidance. It wastes tokens explaining concepts Claude already knows (what heap spraying is, when to use it) while providing zero executable commands, code examples, or concrete detection criteria. The steps read like section headers for a document that was never written.
Suggestions
Replace each step with actual Volatility3 commands (e.g., `python3 vol.py -f dump.raw windows.malfind --pid <PID>`) and show how to parse/interpret the output.
Add concrete detection criteria: specific byte patterns to search for, size thresholds for suspicious allocations (e.g., >1MB contiguous RWX regions), and example Python code for scanning extracted memory regions.
Remove the 'When to Use' section and the explanation of what heap spraying is — Claude knows these concepts. Use those tokens for executable examples instead.
Provide an example JSON output schema for the expected report, and add validation steps (e.g., cross-referencing malfind hits with vadinfo to reduce false positives).
| 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 knowledge Claude possesses. 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...') without actual command syntax, flags, or output parsing. The expected output section describes a JSON report 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 constitutes a suspicious finding, no thresholds for 'large' allocations, no feedback loops for false positives, and no verification steps between stages. | 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, no bundle files exist, and the content is simultaneously too shallow to be useful inline and too verbose with boilerplate. For a skill this complex, detailed reference materials would be expected. | 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
9a588e6
- Reviewed
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