All static findings were adjudicated as false positives caused by Markdown code fences, inline API references, and benign Python import examples. No evidence found of prompt injection, malicious shell execution, credential access, data exfiltration, or system reconnaissance intent.
All static findings were adjudicated as false positives caused by Markdown code fences, inline API references, and benign Python import examples. No evidence found of prompt injection, malicious shell execution, credential access, data exfiltration, or system reconnaissance intent.
Static analysis reported many high-risk patterns, but review found the C2, weak cryptography, network, and reconnaissance alerts are false positives from animation wording, constants, Markdown code fences, and Apache License text. The real risks are low: the skill provides executable Python examples, documents a pip install command, and writes GIF files to local paths. No evidence found of malicious intent, prompt injection, data exfiltration, or unauthorized network behavior.
7
Files scanned
1,280
Lines analyzed
5
Review items
2
False positives ignored
Capability review items (2)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Dependency Installation Command Requires User Consent
Verdict: TRUE_POSITIVE low risk. The skill documents a pip install command for Pillow, imageio, imageio-ffmpeg, and NumPy. Installing packages can execute package manager behavior, but no hidden install script was found in the skill.
The command is explicit and limited to known image-processing dependencies. The risk is supply chain exposure from dependency installation, not malicious code in the skill itself.
Local Filesystem Access for GIF Inputs and Outputs
Verdict: TRUE_POSITIVE low risk. The workflow opens local image files and writes GIF output paths. This is expected for GIF creation, but users should run it in trusted working directories.
The file access is visible and directly related to image input and GIF output. There is no evidence that the paths are used for stealthy file discovery or exfiltration.
Static false positives ignored (2)
These static matches were dismissed by semantic review or matched schema-only tokens, so they are shown for transparency but do not drive the quality score.
Verdict: FALSE_POSITIVE. The flagged lines are animation easing constants, module descriptions, and Apache License text. No evidence found of cryptography, command-and-control behavior, or reconnaissance.
The cited code contains animation math, descriptive text, or standard license wording. Review found no network endpoint, encryption routine, host probing, or control channel at those locations.
Markdown Code Blocks Misclassified as Backtick Execution
Verdict: FALSE_POSITIVE. Most external command alerts are fenced Python examples and inline Python expressions in SKILL.md, not Ruby or shell backtick execution.
The surrounding Markdown shows normal documentation examples for PIL drawing, GIF validation, easing, and animation concepts. They are not hidden command substitution or shell execution patterns.
Static analysis reported many high-risk patterns, but review found the C2, weak cryptography, network, and reconnaissance alerts are false positives from animation wording, constants, Markdown code fences, and Apache License text. The real risks are low: the skill provides executable Python examples, documents a pip install command, and writes GIF files to local paths. No evidence found of malicious intent, prompt injection, data exfiltration, or unauthorized network behavior.
7
Files scanned
1,280
Lines analyzed
5
Review items
2
False positives ignored
Capability review items (2)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Dependency Installation Command Requires User Consent
Verdict: TRUE_POSITIVE low risk. The skill documents a pip install command for Pillow, imageio, imageio-ffmpeg, and NumPy. Installing packages can execute package manager behavior, but no hidden install script was found in the skill.
The command is explicit and limited to known image-processing dependencies. The risk is supply chain exposure from dependency installation, not malicious code in the skill itself.
Local Filesystem Access for GIF Inputs and Outputs
Verdict: TRUE_POSITIVE low risk. The workflow opens local image files and writes GIF output paths. This is expected for GIF creation, but users should run it in trusted working directories.
The file access is visible and directly related to image input and GIF output. There is no evidence that the paths are used for stealthy file discovery or exfiltration.
Static false positives ignored (2)
These static matches were dismissed by semantic review or matched schema-only tokens, so they are shown for transparency but do not drive the quality score.
Verdict: FALSE_POSITIVE. The flagged lines are animation easing constants, module descriptions, and Apache License text. No evidence found of cryptography, command-and-control behavior, or reconnaissance.
The cited code contains animation math, descriptive text, or standard license wording. Review found no network endpoint, encryption routine, host probing, or control channel at those locations.
Markdown Code Blocks Misclassified as Backtick Execution
Verdict: FALSE_POSITIVE. Most external command alerts are fenced Python examples and inline Python expressions in SKILL.md, not Ruby or shell backtick execution.
The surrounding Markdown shows normal documentation examples for PIL drawing, GIF validation, easing, and animation concepts. They are not hidden command substitution or shell execution patterns.
All 64 static findings are false positives. The skill is a legitimate GIF creation utility using standard Python libraries (PIL, numpy, imageio). No network calls, no external command execution, no credential access, and no data exfiltration. Static scanner was triggered by keywords in license text and code documentation.
7
Files scanned
1,285
Lines analyzed
0
Review items
6
False positives ignored
Static false positives ignored (6)
These static matches were dismissed by semantic review or matched schema-only tokens, so they are shown for transparency but do not drive the quality score.
False Positive: C2 Keywords in Animation Constants
Variable names c1, c2, c3 in easing.py are animation easing constants (1.70158), not C2 (command-and-control) indicators. Standard animation math formulas.
The variables c1, c2, c3 are standard easing function coefficients used in back ease formulas - confirmed by mathematical context
Scanner detected backticks in SKILL.md but these are Markdown code fences (```python), not shell execution. The backtick detection is a scanner false positive.
These are Python code examples in Markdown fenced code blocks - standard documentation, not shell commands
No confirmed security findings were recorded for this completed audit.