Target Novelty Scorer

Score the novelty of biological targets through literature mining and trend analysis

The package is internally inconsistent: SKILL.md describes a networked PubMed/PMC crawler that needs API keys and extra libraries, but the included script uses simulated data and requirements.txt is minimal. Before installing or running: (1) inspect scripts/main.py fully to confirm whether any real network calls or subprocesses are present (the shipped code appears simulated but double-check the untruncated file), (2) treat any request for API keys as sensitive—only provide them if you verify code uses them and that calls go to official endpoints (ncbi.nlm.nih.gov or europepmc.org) over HTTPS, (3) run the skill in a sandbox or isolated environment without network access initially to observe behavior, (4) if you expect real PubMed integration, require the author to reconcile requirements.txt and SKILL.md (add explicit environment variable declarations for API keys) and to provide provenance (repository/homepage and author identity) and tests, and (5) if you cannot verify these, avoid supplying secrets or running the skill on sensitive systems. Additional information that would raise confidence: a project repo/homepage, a clear list of declared env vars for API keys, and a version of the script that demonstrably uses official APIs with HTTPS and reasonable rate/timeout handling.

Type: OpenClaw Skill Name: target-novelty-scorer Version: 0.1.0 The skill bundle is a template or draft for a biological target novelty scorer. While the documentation in SKILL.md suggests high-risk activities like network access and literature mining, the actual implementation in scripts/main.py is a simulation that generates scores using numpy and random number generation rather than making external API calls. There is no evidence of malicious intent, data exfiltration, or unauthorized execution; the code is straightforward and follows its stated (simulated) purpose.