ara-compiler
Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
74
92%
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High
Do not use without reviewing
Security
1 high severity finding. You should review these findings carefully before considering using this skill.
W007: Insecure credential handling detected in skill instructions.
What this means
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
Why it was flagged
The prompt requires the agent to "read ALL provided inputs" (including environment files), to transcribe raw source content verbatim, and to produce environment/output files with exact copied values, so any secrets present in inputs would be reproduced in the agent's outputs.
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
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
This skill’s required workflow explicitly instructs reading user-provided research inputs at runtime (including “PDF papers… GitHub repositories (URLs or local paths)… Slack/email threads… verbal description”), which would cause the agent to ingest outsider-authored free text fetched/loaded from those inputs (e.g., public URLs or downloaded PDFs/repos) into the LLM context during its “READ all inputs” and subsequent “re-read source” coverage loop.
- Repository
- Orchestra-Research/AI-Research-SKILLs
- Commit
773a529
- Audited
- Security analysis
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