draft-offer
The draft-offer skill assembles a complete offer letter package for hiring managers, including base salary, equity details, signing bonus, vesting schedules, benefits summary, and formal offer text. Use it when a candidate has passed interviews and is ready for an offer, or when a hiring manager needs help structuring total compensation and preparing negotiation talking points.
git clone --depth 1 https://github.com/openyak/openyak /tmp/draft-offer && cp -r /tmp/draft-offer/backend/app/data/plugins/human-resources/skills/draft-offer ~/.claude/skills/draft-offerSKILL.md
# /draft-offer > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). Draft a complete offer letter for a new hire. ## Usage ``` /draft-offer $ARGUMENTS ``` ## What I Need From You - **Role and title**: What position? - **Level**: Junior, Mid, Senior, Staff, etc. - **Location**: Where will they be based? (affects comp and benefits) - **Compensation**: Base salary, equity, signing bonus (if applicable) - **Start date**: When should they start? - **Hiring manager**: Who will they report to? If you don't have all details, I'll help you think through them. ## Output ```markdown ## Offer Letter Draft: [Role] — [Level] ### Compensation Package | Component | Details | |-----------|---------| | **Base Salary** | $[X]/year | | **Equity** | [X shares/units], [vesting schedule] | | **Signing Bonus** | $[X] (if applicable) | | **Target Bonus** | [X]% of base (if applicable) | | **Total First-Year Comp** | $[X] | ### Terms - **Start Date**: [Date] - **Reports To**: [Manager] - **Location**: [Office / Remote / Hybrid] - **Employment Type**: [Full-time, Exempt] ### Benefits Summary [Key benefits highlights relevant to the candidate] ### Offer Letter Text Dear [Candidate Name], We are pleased to offer you the position of [Title] at [Company]... [Complete offer letter text] ### Notes for Hiring Manager - [Negotiation guidance if needed] - [Comp band context] - [Any flags or considerations] ``` ## If Connectors Available If **~~HRIS** is connected: - Pull comp band data for the level/role - Verify headcount approval - Auto-populate benefits details If **~~ATS** is connected: - Pull candidate details from the application - Update offer status in the pipeline ## Tips 1. **Include total comp** — Candidates compare total compensation, not just base. 2. **Be specific about equity** — Share count, current valuation method, vesting schedule. 3. **Personalize** — Reference something from the interview process to make it warm.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.
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