Use when the user wants to review Qodo PR feedback or fix code review comments. Capabilities: view issues by severity, apply fixes interactively or in batch, reply to inline comments, post fix summaries (GitHub, GitLab, Bitbucket, Azure DevOps, Gerrit)
62
73%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Fix and improve this skill with Tessl
tessl review fix ./skills/qodo-pr-resolver/SKILL.mdSecurity
3 findings — 3 medium severity. This skill can be installed but you should review these findings before use.
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.
Third-party content exposure detected (high risk: 0.95). Outsider-authored free text from Qodo review comments is fetched at runtime via the provider CLIs/APIs (e.g., `gh pr view ... --json comments`, `gh api .../pulls/<pr>/comments`, `glab mr view ... --comments`, Bitbucket/Azure DevOps `curl`/`az devops invoke`, and Gerrit `GET .../comments` + `.../messages`), then parsed and inserted into the agent’s LLM context (issue titles/details and especially the “Agent Prompt” fix instructions).
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.
Potentially malicious external URL detected (high risk: 0.90). The skill fetches Qodo review comments at runtime (including executable "Agent Prompt" text) from provider APIs such as the Gerrit comments endpoint "$GERRIT_URL/a/changes/<change-id>/comments", and those fetched prompts are executed literally to implement fixes.
Detected hidden or invisible Unicode characters (Format/Cf or Control/Cc categories) in the component’s content. These characters are invisible when rendered but are still processed by AI models, and attackers use them to smuggle instructions past human review — for example, zero-width spaces, bidirectional overrides, invisible formatters, or Unicode Tag characters (U+E0000–U+E007F) that encode an entire hidden message. Severity escalates to high when three or more distinct hidden character types are present, or when a hidden tag-encoded message is successfully decoded, as these strongly indicate intentional obfuscation.
Hidden Unicode characters detected (1 type(s) found)
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