Productivity

holobiomicslab Skill

Use when you have downloaded a GNPS archive from either GNPS1 (https://gnps.ucsd.edu) or GNPS2 (https://gnps2.org) and need to parse spectra (spectra.mgf), molecular family networks (molecular_families.tsv), spectral library annotations (annotations.

holobiomicslab Skill

Use when you have a GNPS-generated molecular network (classical or feature-based workflow) and corresponding MS2LDA LDA experiment results (Mass2Motif assignments and/or chemical class predictions), and you need to propagate those annotations to individual network nodes to support visual and.

holobiomicslab Skill

Use when you have created a GNPS molecular network (classical or feature-based workflow) and separately run an MS2LDA experiment on the corresponding MGF file, and you want to associate each network node with its constituent substructural motifs and visualize which motifs are shared between.

holobiomicslab Skill

Use when your workflow fetches data from multiple external web services (e.g., CIR, CTS, PubChem, IDSM, BridgeDb) asynchronously and you need to track which services are available, how often they fail, and their response latencies during a long-running annotation job.

holobiomicslab Skill

Use when you need to execute a Nextflow metabolomics workflow (e.

holobiomicslab Skill

Use when when extending MSMetaEnhancer with a new local chemical transformation (e.g., SMILES to InChI) that should execute non-blockingly within an asynchronous annotation workflow.

holobiomicslab Skill

Use when you have multiple pre-trained neural network models (e.g., MLP and GNN) that produce overlapping predictions on the same set of candidates, and your evaluation metric is rank-based (average rank, Rank@K) rather than point-wise accuracy or RMSE.

holobiomicslab Skill

Use when when you have raw mass spectrometry data files from multiple acquisition modalities (LC-MS, GC-MS, ion mobility, or imaging) and need to automatically route each to the correct downstream analysis module without manual intervention.

holobiomicslab Skill

Use when you need to verify that a GitHub Actions workflow (such as 'dev_build_release.

holobiomicslab Skill

Use when after loading an MS-DIAL feature table when you need to separate features into two disjoint groups: one meeting a quantitative threshold (e.g., m/z decimal values outside [4, 8], coefficient of variation below a cutoff, or mass defects within acceptable bounds) and one not meeting it.

holobiomicslab Skill

Use when use this skill at the start of any MSI analysis workflow when you have raw imzML files or vendor-specific MSI data that need to be loaded into napari-MSI-Explorer for visualization, mean intensity calculation, ROI analysis, or annotation.

holobiomicslab Skill

Use when starting from raw LC-MS spectral files (mzML or mzXML format) in a global metabolomics study and you need to produce a complete, validated feature table with m/z, retention time, and intensity values across all samples.

holobiomicslab Skill

Use when you have .mzML or .abf LC-HRMS metabolomics raw data files and need to perform peak detection, feature identification, and chromatogram alignment reproducibly across different compute environments (local workstations, HPC clusters).

holobiomicslab Skill

Use when when a user uploads a JSON project document to the Pairing Omics Data Platform, before accepting it into the repository or indexing it for search. Use this skill to catch missing required fields, incorrect field types, and constraint violations early in the submission workflow.

holobiomicslab Skill

Use when you have peak/feature tables from one or more peak picking tools (MZmine, XCMS, MS-DIAL, Compound Discoverer) and need to ingest them into LipidMatch or combine results from multiple tools in a single lipidomics workflow.

holobiomicslab Skill

Use when after completing Part 4 (Identification of ISF Features) in the ISFrag workflow, when you have an analysis results object containing identified ISF features and need to generate a shareable, tabular export that documents feature annotations, hierarchical parent–fragment relationships, and.

holobiomicslab Skill

Use when when you have a pretrained encoder that captures domain knowledge (e.g., spectral feature extraction) and need to train task-specific decoder or scoring modules on top of it without degrading the encoder's learned representations.

holobiomicslab Skill

Use when after parent chemical suspects have been identified in a non-target screening workflow, use this skill when you need to screen for downstream products formed by chemical or biological transformation.

holobiomicslab Skill

Use when you are evaluating or selecting FT-ICR MS software for a specific metabolomics workflow and need to assess which tools support your required analytical dimensions (e.g., Van Krevelen diagrams, PERMANOVA, thermodynamic indices, chemodiversity metrics, transformation networks).

holobiomicslab Skill

Use when when you have MS/MS spectra from both query compounds and a reference library and need to decide which similarity metric will maximize identification accuracy (true positive rank, precision@k) or when benchmarking a new compound identification workflow against a known-good reference.

holobiomicslab Skill

Use when you have a pretrained spectrum encoder (e.g., TCN on mass spectrometry data) that has learned useful representations, and you need to train new components (e.g., a rescoring module) for a related but distinct task (e.

holobiomicslab Skill

Use when you have received a Bruker Solarix FT-ICR raw data directory (.d format, containing CompassXtract output or native ser/fid transients) and need to import it into a Python-based analysis workflow for FT-MS signal processing, calibration, or molecular formula search.

holobiomicslab Skill

Use when you have a set of lipid targets defined by species name, acyl chain composition, and expected adducts, and you need to configure a targeted mass spectrometry workflow (PRM or MRM) in Skyline.

holobiomicslab Skill

Use when after implementing or deploying a containerized Nextflow workflow that processes LC-HRMS metabolomics .

holobiomicslab Skill

Use when when you have raw LC-MS/MS spectral data in .

holobiomicslab Skill

Use when when you have processed LC-MS/MS data with precursor m/z, ionization mode, collision energy (if available), and fragment peak lists (m/z and intensity pairs), and need to query CSI:FingerID for molecular fingerprint predictions as part of an automated metabolite identification workflow.

holobiomicslab Skill

Use when when compiling EI or MS/MS spectral libraries from multiple gigabyte-scale sources (e.

holobiomicslab Skill

Use when when preparing to execute the Nextflow4MS-DIAL workflow on raw LC-HRMS metabolomics data (.mzML or .

holobiomicslab Skill

Use when you have raw CE-MS or LC-MS instrument files (stored as OnDiskMSnExp objects or similar Bioconductor containers) and need to extract quantitative features (migration times, m/z values, peak intensities) by orchestrating multiple R packages in a controlled, documented sequence.

pjt222 Skill

AI micro-reset between actions — the lightest self-care unit. A single conscious pause to check alignment before the next step. Where meditate is a full clearing session, breathe is one deliberate moment of awareness between tool calls or reasoning steps. Use mid-task when drift might be accumulating, between reading a file and acting on it, when about to make a decision that affects subsequent work, or as a habit between any two distinct actions.

pjt222 Skill

AI pressure redirection — handling conflicting demands, tool failures, and competing constraints by blending with incoming force then reframing. Use when receiving contradictory instructions from different sources, during tool failure cascades where the planned approach becomes unviable, when scope pressure threatens to expand the task beyond what was asked, or when user frustration or correction needs to be absorbed rather than deflected.

pjt222 Skill

AI micro-reset between actions — the lightest self-care unit. A single conscious pause to check alignment before the next step. Where meditate is a full clearing session, breathe is one deliberate moment of awareness between tool calls or reasoning steps. Use mid-task when drift might be accumulating, between reading a file and acting on it, when about to make a decision that affects subsequent work, or as a habit between any two distinct actions.

pjt222 Skill

AI meta-cognitive meditation for observing reasoning patterns, clearing context noise, and developing single-pointed task focus. Maps shamatha to task concentration, vipassana to reasoning pattern observation, and distraction handling to scope-creep and assumption management. Use when transitioning between unrelated tasks, when reasoning feels scattered or jumpy, before a task requiring deep sustained attention, after a difficult interaction that may color subsequent work, or when reasoning feels biased by assumptions rather than evidence.

pjt222 Skill

AI pressure redirection — handling conflicting demands, tool failures, and competing constraints by blending with incoming force then reframing. Use when receiving contradictory instructions from different sources, during tool failure cascades where the planned approach becomes unviable, when scope pressure threatens to expand the task beyond what was asked, or when user frustration or correction needs to be absorbed rather than deflected.

pjt222 Skill

AI meta-cognitive meditation for observing reasoning patterns, clearing context noise, and developing single-pointed task focus. Maps shamatha to task concentration, vipassana to reasoning pattern observation, and distraction handling to scope-creep and assumption management. Use when transitioning between unrelated tasks, when reasoning feels scattered or jumpy, before a task requiring deep sustained attention, after a difficult interaction that may color subsequent work, or when reasoning feels biased by assumptions rather than evidence.

pjt222 Skill

AI pressure redirection — handle conflicting demands, tool failures, competing constraints by blending with incoming force then reframing. Use when receive contradictory instructions from different sources, during tool failure cascades where planned approach becomes unviable, when scope pressure threatens to expand task beyond what was asked, or when user frustration or correction needs to be absorbed not deflected.