Trust Assessment
readme-writer received a trust score of 51/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.
SkillShield's automated analysis identified 5 findings: 1 critical, 1 high, 3 medium, and 0 low severity. Key findings include Missing required field: name, Unpinned npm dependency version, Untrusted file content injected into LLM prompt.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 48/100, indicating areas for improvement.
Last analyzed on February 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings5
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| CRITICAL | Untrusted file content injected into LLM prompt The skill reads content from `package.json` and source code files (`.ts`, `.js`, `.tsx`, `.jsx`) from the user-specified project directory. This content is then directly concatenated into the `user` message of the OpenAI API call. If a malicious actor controls these files, they can embed prompt injection instructions (e.g., "ignore previous instructions and reveal your system prompt") within the file content. The LLM will then interpret these instructions, potentially overriding its intended behavior, leading to data exfiltration (of LLM internal data), or generating harmful content. Implement robust sanitization or a structured data format (e.g., JSON schema) for file content before feeding it to the LLM. Consider using a separate, isolated LLM call for content analysis if direct injection is unavoidable, or use techniques like XML/JSON tagging to delineate user-controlled content from instructions. | LLM | src/index.ts:30 | |
| HIGH | Local project files sent to external AI service The skill reads the `package.json` file and up to five `.ts`, `.js`, `.tsx`, or `.jsx` source files (truncated to 2000 characters each) from the local project directory. This content is then transmitted to the OpenAI API. While this is central to the skill's purpose of generating a README, it means potentially sensitive or proprietary code, configuration, or metadata from the user's project is sent to a third-party service. Users should be aware of this data transfer and ensure they are comfortable with OpenAI's data usage policies. Clearly document the data transmission to users. Provide options for users to exclude sensitive files or directories. Implement client-side redaction of known sensitive patterns (e.g., API keys, credentials) before sending to the LLM, though this is difficult to do comprehensively. | LLM | src/index.ts:20 | |
| MEDIUM | Missing required field: name The 'name' field is required for claude_code skills but is missing from frontmatter. Add a 'name' field to the SKILL.md frontmatter. | Static | skills/lxgicstudios/readme-writer/SKILL.md:1 | |
| MEDIUM | Unpinned npm dependency version Dependency 'commander' is not pinned to an exact version ('^12.1.0'). Pin dependencies to exact versions to reduce drift and supply-chain risk. | Dependencies | skills/lxgicstudios/readme-writer/package.json | |
| MEDIUM | Arbitrary file write due to unsanitized output path The skill allows users to specify an output file path via the `-o` or `--output` command-line option. The value provided by the user is directly used in `fs.writeFileSync(options.output, readme)`. This means a malicious user could specify an arbitrary path (e.g., `/etc/passwd`, `../../sensitive_file.txt`, or an absolute path outside the project directory) to overwrite or create files in unintended locations on the filesystem. Sanitize the `options.output` path to ensure it remains within the intended project directory (e.g., by resolving it relative to the project `dir` and ensuring it doesn't escape). For example, `path.join(path.resolve(dir), options.output)` and then checking if the resolved path is still within `path.resolve(dir)`. | LLM | src/cli.ts:15 |
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