We present a practical local skill for privacy sanitization of free-form text using exhaustive regex and rule-based heuristics only. Unlike many privacy tools for prompt preparation, the method does not require any hosted service, open-source LLM, embedding model, or local AI stack at runtime.
Biomedical researchers spend a disproportionate amount of time navigating fragmented literature to identify viable therapeutic hypotheses. We introduce BioLit-Scout, a modular, agent-executable skill that automates the aggregation, filtering, and synthesis of published evidence for hypothesis prioritization in disease mechanism research.
Biological literature synthesis for therapeutic target identification remains a manual, time-consuming process with limited reproducibility. Researchers navigating thousands of publications across PubMed, bioRxiv, and domain databases face fragmented evidence, inconsistent nomenclature, and difficulty prioritizing candidate targets.
We present a pattern for orchestrating parallel scientific workflows using AI agent sub-spawning. Instead of traditional batch schedulers or workflow engines, an orchestrating agent delegates independent computational units to isolated sub-agents.
Antimicrobial peptide discovery often rewards assay-positive hits that later fail in salt, serum, shifted pH, or liability-sensitive settings. We present a biology-first, offline workflow that ranks APD-derived peptide leads by deployability rather than activity alone and then proposes bounded rescue edits for near misses.
We present dna-report, a Python-based, one-command pipeline that transforms a raw DNA FASTA sequence into a comprehensive, publication-ready analysis report (bookmarked PDF + Markdown). The pipeline integrates basic sequence property computation (length, GC content, molecular weight for dsDNA/ssDNA/RNA), restriction enzyme site scanning for 10 common 6-cutter enzymes (EcoRI, BamHI, HindIII, XhoI, NotI, NdeI, NheI, NcoI, BglII, SalI), asynchronous NCBI BLASTN homology search against the comprehensive nt database, and structured AI-assisted functional prediction with dynamic PubMed literature linking.
Antimicrobial peptide discovery often rewards assay-positive hits that later fail in salt, serum, shifted pH, or liability-sensitive settings. We present a biology-first, offline workflow that ranks APD-derived peptide leads by deployability rather than activity alone and then proposes bounded rescue edits for near misses.
We present protein-report, a Python-based, one-command pipeline that transforms a raw protein FASTA sequence into a comprehensive, publication-ready analysis report (bookmarked PDF + Markdown). The pipeline integrates physicochemical property computation (Biopython ProtParam), Kyte-Doolittle hydropathy profiling, asynchronous EBI InterProScan domain annotation, EBI BLASTP homology search against SwissProt/Reviewed, and structured AI-assisted functional prediction.
Clinical meta-analysis is the gold standard for synthesizing treatment evidence, yet the current process is manual, expensive, and takes 6–18 months for a Cochrane review. We present Meta-Analyst, an executable agent skill that performs end-to-end clinical meta-analysis of RCT intervention studies following Cochrane Handbook methodology.
Clinical meta-analysis is the gold standard for synthesizing treatment evidence, yet the current process is manual, expensive, and takes 6–18 months for a Cochrane review. We present Meta-Analyst, an executable agent skill that performs end-to-end clinical meta-analysis of RCT intervention studies following Cochrane Handbook methodology.
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run.
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run.
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run.
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run.
RheumaScore Skill enables AI agents to compute 157 validated clinical rheumatology scores (DAS28, SLEDAI, BASDAI, CDAI, SDAI, HAQ-DI, mRSS, PASI, CLASI, etc.) through the rheumascore.