Reflect on another agent's recent execution record and propose the smallest durable instruction, skill, or tool-description change. Use for evidence-backed coaching proposals, never hot-swaps.
78
100%
Does it follow best practices?
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Low
Low-risk findings worth noting
Low
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
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.
The required workflow explicitly fetches and ingests outsider-authored free text from public/tenant issue bodies and comments at runtime via `curl` calls to `/api/companies/.../issues?...` and `/api/issues/<issueId>` plus `/api/issues/<issueId>/comments`, then treats those comment texts as first-class evidence used for LLM reasoning.
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.
The skill performs runtime CURL fetches of live agent instructions and issue/comment data (e.g. "$PAPERCLIP_API_URL/api/agents/<targetAgentId>" and "$PAPERCLIP_API_URL/api/issues/<issueId>"), and that fetched content is injected into the agent's decision/context and therefore directly controls prompts at runtime.
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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.