autoskill
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
62
75%
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Low
Low-risk findings worth noting
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tessl review fix ./skills/autoskill/SKILL.mdLow
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
The required runtime flow `scripts/fetch_window.py -> scripts/run.py (events text/window_title) -> scripts/synthesize.py (_build_prompt)` ingests OCR/window-title free text coming from the local screenpipe daemon, which is not authored by the operating user (outsider source: user’s captured workspace content via a third-party local agent), and that text is placed into the LLM prompt context (indirect injection risk).
- Repository
- K-Dense-AI/scientific-agent-skills
- Commit
757b63b
- Audited
- Security analysis
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