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aipoch-ai

Target Novelty Scorer

by AIpoch · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install target-novelty-scorer-1
Description
Score the novelty of biological targets through literature mining and.
Usage Guidance
This skill is functionally inconsistent: its documentation promises real PubMed/PMC retrieval and lists NCBI/API dependencies, but the included script simulates results locally and doesn't use network calls or credentials. Before installing or using it: (1) Do not provide API keys to this skill — the registry metadata does not require them and the code doesn't use them. (2) Inspect scripts/main.py fully and run python -m py_compile scripts/main.py in a safe environment to confirm behavior. (3) If you need real literature mining, ask the author or maintainer to: implement actual PubMed/E-utilities calls (or clarify it's intentionally a demo), update requirements.txt, and declare any required environment variables explicitly. (4) Treat outputs as synthetic/demo data until the code is confirmed to perform real retrieval; do not use this for sensitive research decisions without verification.
Capability Analysis
Type: OpenClaw Skill Name: target-novelty-scorer-1 Version: 1.0.0 The skill bundle is a legitimate, albeit early-stage, tool for scoring biological target novelty. The primary script, `scripts/main.py`, currently uses a simulated implementation for literature searching via `numpy.random` and contains no evidence of malicious execution, data exfiltration, or unauthorized network activity. While `SKILL.md` mentions dependencies like `biopython` and `requests` that are missing from `requirements.txt`, this appears to be a development oversight rather than a security risk.
Capability Assessment
Purpose & Capability
SKILL.md and description state the tool retrieves literature (PubMed/PMC) and lists NCBI API key, requests, biopython, and pandas as dependencies. The shipped code (scripts/main.py) does not perform network calls; PubMedSearcher.search() returns simulated/randomized data. Declared capabilities (real literature retrieval) do not match the actual implementation (local simulation).
Instruction Scope
Runtime instructions are limited to running the packaged script and validating inputs; they do not ask the agent to read unrelated files or export secrets. However SKILL.md instructs users to provide an NCBI API key and mentions editing an in-file CONFIG block — the code does not require or use such credentials, creating a gap between instructions and actual behavior that could confuse users or cause them to expose keys unnecessarily.
Install Mechanism
No install spec — skill is instruction-only with a bundled script. That minimizes installation risk. Minor mismatch: SKILL.md lists many Python dependencies, but requirements.txt only lists dataclasses and numpy; this is an implementation/documentation inconsistency rather than a direct install risk.
Credentials
SKILL.md lists 'NCBI API Key' (and optional Europe PMC) as API requirements, but the registry metadata declares no required environment variables and the included script does not use any environment credentials. Asking for an API key in docs while not declaring or using it is disproportionate and could lead users to provide secrets unnecessarily if they attempted to wire them in.
Persistence & Privilege
The skill does not request persistent or elevated privileges (always: false). It does not install services or modify other skills. Autonomous invocation is allowed by default but is not combined with broad credential access or system modifications.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install target-novelty-scorer-1
  3. After installation, invoke the skill by name or use /target-novelty-scorer-1
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of Target Novelty Scorer. - Scores the novelty of biological targets based on literature mining from academic databases (e.g., PubMed, PMC). - Features include literature retrieval, multi-dimensional novelty scoring (0–100), trend analysis, cross-validation, and detailed report generation. - Provides both basic and advanced command-line usage, supporting output in text, JSON, or CSV. - Assesses targets using five main scoring criteria: research heat, uniqueness, research depth, collaboration network, and temporal trends. - Includes security, risk, and evaluation checklists to ensure safe and reliable operation. - Requires Python 3.9+, with dependencies including requests, pandas, biopython, and numpy.
Metadata
Slug target-novelty-scorer-1
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Target Novelty Scorer?

Score the novelty of biological targets through literature mining and. It is an AI Agent Skill for Claude Code / OpenClaw, with 88 downloads so far.

How do I install Target Novelty Scorer?

Run "/install target-novelty-scorer-1" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Target Novelty Scorer free?

Yes, Target Novelty Scorer is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Target Novelty Scorer support?

Target Novelty Scorer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Target Novelty Scorer?

It is built and maintained by AIpoch (@aipoch-ai); the current version is v1.0.0.

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