Productivity

porsche-design-system Skill

Guided onboarding for OpenSpec - walk through a complete workflow cycle with narration and real codebase work.

primitivedotdev Skill

Use whenever you need an email address to receive something and then read or wait for it, especially during a signup or login flow. PROACTIVELY, and you can usually do this without asking the user first: a service is about to send a verification code, OTP, one-time code, 2FA or two-factor code, confirmation link, magic link, or password reset and you need to wait for it and read the value out; you are signing up for or registering with a service and must confirm an email address to continue; you need a fresh, managed, throwaway, or burner address instead of using a real one; you sent something and need to watch for what lands. REACTIVELY: the user asks "did it arrive?", "check the inbox", "what came in?", "wait for the email", or wants an address to catch replies, codes, receipts, or alerts. Provides a managed `*.primitive.email` address plus `primitive emails latest` and `primitive emails wait` to read and block for mail, and hosted Functions to run JavaScript on every inbound message. No SMTP, no DNS, no ma

prisma Skill

Cuts the next minor release of Prisma Composer: bumps the root package.json version, propagates it to every workspace package in lockstep, and opens a PR titled "chore(release): v<next-version>". When a maintainer merges the PR, the `Publish to npm` workflow runs automatically and ships the new version to npm under dist-tag `latest`, plus a matching GitHub Release with auto-generated notes. Use when a maintainer asks to "cut the next minor", "bump to the next version", "open a release PR", or "prepare a publish PR".

steipete Bundle

Plan and publish a GitHub Release in a tag-driven repository. Use when a user asks to cut, prepare, or publish a software release, propose the next vX.Y.Z tag, support prerelease tags like vX.Y.Z-beta.N, draft better release notes from PRs and direct commits since the last release, update CHANGELOG.md, create the tag pinned to an exact commit, and watch the publish workflow.

tenstorrent Skill

Route gh-aw workflow design/create/debug/upgrade requests to the right prompts.

holobiomicslab Skill

Use when you have a GitHub repository with published CI workflow badges (e.g., unit test or package test badges in the README) and need to independently verify that the workflows execute successfully, reproduce the pass/fail status, and collect structured test results.

holobiomicslab Skill

Use when you have received raw CE-MS or LC-MS output files in vendor-specific formats from a mass spectrometry instrument and need to process them through an untargeted metabolomics workflow (e.g., AriumMS) that requires standardized, interoperable file formats.

holobiomicslab Skill

Use when you have raw mzML files from a mass spectrometry instrument and need to begin a preprocessing workflow in xcms. This is the essential first step before any peak detection (centWave, MSWParam) or feature grouping can occur.

holobiomicslab Skill

Use when when beginning a metabolomics annotation workflow with raw MS2 spectral data in .mzML format. This step is necessary when you have vendor-converted or standard .

holobiomicslab Skill

Use when when you need to retrieve mass spectrometry spectrum data from a metabolomics repository but only have a USI string (e.g., 'mzspec:GNPS:TASK-c95481f0c53d42e78a61bf899e9f9adb-spectra/specs_ms.mgf:scan:1943' or 'mzspec:MASSBANK::accession:SM858102').

holobiomicslab Skill

Use when when you have a USI string (e.g., 'mzspec:GNPS:TASK-abc123:scan:1943') and need to retrieve the underlying spectrum data from its native repository without knowing a priori which backend stores it.

holobiomicslab Skill

Use when you need to support multiple plotting backends for the same data visualization task, and you want to centralize backend selection logic so that users can specify their preferred rendering engine (matplotlib, bokeh, or plotly) at call time without modifying the core plotting logic.

holobiomicslab Skill

Use when you have received a peak/feature table from an unknown or variable upstream peak-picking tool and need to ingest it into LipidMatch or a similar unified workflow. The input file format, column naming, or metadata structure is tool-specific (e.

holobiomicslab Skill

Use when you have downloaded a GNPS molecular networking job archive and need to extract its contents (spectra.mgf, molecular_families.tsv, annotations.tsv, file_mappings) but do not know which GNPS workflow version produced it, preventing correct file naming and downstream computational analysis.

holobiomicslab Skill

Use when you have raw LC-MS data (mzML, NetCDF) from multiple runs that require sequential feature detection, alignment, quantification, and optional filtering (e.g., blank exclusion, QC reproducibility, retention-time outlier removal) before spatial mapping or annotation.

holobiomicslab Skill

Use when investigating how a specific pull request (e.g., PR #72 introducing MS2Query's two-branch workflow split) modified the codebase architecture, control flow, or data routing.

holobiomicslab Skill

Use when when you have run a spectral networking job on GNPS (e.g. ProteoSAFe-METABOLOMICS-SNETS-V2) and need to reuse the network output files locally with MetaMiner or another tool that accepts spectral network input directories.

holobiomicslab Skill

Use when when beginning a MEMO analysis workflow with raw or unaligned MS2 spectra files and needing to extract fragmentation data and precursor information before counting MS2 peaks and neutral losses to generate sample fingerprints.

holobiomicslab Skill

Use when after automated peak detection has identified candidate peaks from LC-MS mzML files, but before exporting the final metabolite library. Use this skill when you need to: (1) optimize noise and peak-detection parameters by visualizing their effect on a representative subset of peaks;

holobiomicslab Skill

Use when you have created a GNPS molecular network (classical or feature-based workflow) and run an MS2LDA experiment on the corresponding MGF spectra, and you want to annotate network nodes with shared Mass2Motifs and chemical class information to interpret the structural basis of network.

holobiomicslab Skill

Use when you have downloaded a Jupyter notebook from a published metabolomics workflow repository (e.

holobiomicslab Skill

Use when when a spectral matching tool produces mixed output containing both exact library matches and analog search results, and your analysis or publication requires separate handling, interpretation, or reporting of these two match classes.

holobiomicslab Skill

Use when after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates. Use it when your untargeted LC-MS workflow needs to reduce feature redundancy caused by naturally occurring stable isotope patterns and common adduct formation.

holobiomicslab Skill

Use when you have developed an R-based workflow (e.g., data processing, peak detection, quality review) that is complex enough to warrant interactive parameter tuning and visual feedback, and you need to distribute it to collaborators or end-users who prefer a graphical interface over scripting.

holobiomicslab Skill

Use when you have raw mass spectrometry spectral data in common formats (MGF, MSP, mzML, mzXML, JSON) that requires standardized metadata cleaning, validation, and peak filtering before comparative analysis. Use this skill when you need to encode data quality constraints (e.

holobiomicslab Skill

Use when you have parsed LC-MS/MS spectral data (precursor m/z, ionization mode, collision energy, and a list of fragment m/z and intensity pairs) and need to submit it to the CSI:FingerID web service for molecular fingerprint prediction as part of a metabolite identification workflow.

holobiomicslab Skill

Use when you need to verify that a GitHub Actions workflow (such as dev_build_release.yml) successfully completes end-to-end, especially after code changes or to confirm that automated build infrastructure is functioning correctly.

holobiomicslab Skill

Use when you have acquired imaging mass spectrometry (IMS) data stored in imzML format (accompanied by an .ibd ion binary data file) and need to load it into a Python-based spatial metabolomics workflow.

holobiomicslab Skill

Use when you have raw mass spectrometry data in mzXML, mzML, or netCDF format from untargeted LC/HRMS analysis that has been pre-processed by IDSL.

holobiomicslab Skill

Use when before launching a multi-tool computational workflow (e.g., QCxMS2 mass spectra calculations) that depends on external programs with version-sensitive APIs or features. Apply this skill when: (1) the workflow has explicit minimum version requirements for one or more dependencies;

holobiomicslab Skill

Use when after completing MS2 annotation in the JPA metabolomics workflow, when you have aligned feature data (feature matrix with m/z, retention time, intensity, and sample assignments) and need to extract and export EIC traces for individual features or feature subsets for external validation.

holobiomicslab Skill

Use when when beginning a new mass spectrometry analysis workflow with raw spectral data files in mzML, mzXML, msp, MGF, or JSON format.

holobiomicslab Skill

Use when you have a feature table generated from LC-MS/MS non-targeted metabolomics data that spans multiple sample preparation batches, instrumental runs, or experimental conditions.

holobiomicslab Skill

Use when after running Paramounter's peak-height optimization on XCMS CentWave-extracted metabolomic features, if the downstream analysis or feature validation reveals an unacceptable rate of false positives, or if the extraction workflow is experiencing software crashes or timeout failures due to.

holobiomicslab Skill

Use when you need to execute a complex computational chemistry workflow (QCxMS2) that depends on multiple external semiempirical and ab initio quantum chemistry packages.

holobiomicslab Skill

Use when xCMS has produced aligned LC-MS features but alignment quality is suspected to be poor—especially when analyzing hundreds of samples, data acquired over extended periods (>1 week), or when individual m/z bins show inconsistent RT shifts.