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code-review

Paranoid architect review of code changes for bugs, security, missing tests, and undocumented assumptions. Works on local git diffs OR a GitHub pull request (e.g. `owner/repo N`). For PRs, can post findings as line-level review comments.

66

Quality

80%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/code-review/SKILL.md
SKILL.md
Quality
Evals
Security

Low

Low-risk findings.

2 low severity findings. Worth noting, but not necessarily harmful.

Low

W011: Third-party content exposure detected (indirect prompt injection risk).

What this means

The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.

Why it was flagged

The REQUIRED workflow for the “GitHub PR path” reads PR metadata/diff and then fetches full file contents from GitHub API endpoints (public URLs for the specified PR), meaning outsider-authored PR diff/code text is ingested into the LLM context at runtime.

Report incorrect finding
Low

W012: Unverifiable external dependency detected (runtime URL that controls agent).

What this means

The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.

Why it was flagged

The skill issues runtime GETs to GitHub (e.g. fetching PR metadata and raw file contents via https://api.github.com/repos/{owner}/{repo}/pulls/{number} and https://api.github.com/repos/{owner}/{repo}/contents/{urllib.parse.quote(path, safe='')}?ref={head_sha}) and then uses the returned file text (r["body"]) as input for the review, which means remote repository content can directly influence the agent's prompts/behavior (prompt-injection risk).

Repository
nearai/ironclaw
Audited
Security analysis
Snyk

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