signature-request
This signature-request skill prepares finalized documents for electronic signature by running a pre-signature checklist to verify completeness, configuring the signing order and signer sequence, and routing the document for execution through connected e-signature platforms or generating instructions for manual signing. Use it when a contract has been finalized and both parties are ready to execute, when verifying entity names and signature blocks before sending, or when setting up signing envelopes with sequential or parallel signers.
git clone --depth 1 https://github.com/openyak/openyak /tmp/signature-request && cp -r /tmp/signature-request/backend/app/data/plugins/legal/skills/signature-request ~/.claude/skills/signature-requestSKILL.md
# /signature-request -- E-Signature Routing > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). Prepare a document for electronic signature — verify completeness, set signing order, and route for execution. **Important**: This command assists with legal workflows but does not provide legal advice. Verify documents are in final form before sending for signature. ## Usage ``` /signature-request $ARGUMENTS ``` Prepare for signature: @$1 ## Workflow ### Step 1: Accept the Document Accept the document in any format: - **File upload**: PDF, DOCX - **URL**: Link to a document in ~~cloud storage or ~~CLM - **Reference**: "The Acme Corp MSA we finalized yesterday" ### Step 2: Pre-Signature Checklist Before routing for signature, verify: ```markdown ## Pre-Signature Checklist - [ ] Document is in final, agreed form (no open redlines) - [ ] All exhibits and schedules are attached - [ ] Correct legal entity names on signature blocks - [ ] Dates are correct or left blank for execution date - [ ] Signature blocks match the authorized signers - [ ] Any required internal approvals have been obtained - [ ] Document has been reviewed by appropriate counsel ``` ### Step 3: Configure Signing Gather signing details: - **Signers**: Who needs to sign? (names, emails, titles) - **Signing order**: Sequential or parallel? - **Internal approval**: Does anyone need to approve before the counterparty signs? - **CC recipients**: Who should receive a copy of the executed document? ### Step 4: Route for Signature **If ~~e-signature is connected:** - Create the signature envelope/request - Set signing fields and order - Add any required initials or date fields - Send for signature **If not connected:** - Generate a signing instruction document - Provide the document formatted for wet signature or manual e-sign - List all signers with contact information ## Output ```markdown ## Signature Request: [Document Title] ### Document Details - **Type**: [MSA / NDA / SOW / Amendment / etc.] - **Parties**: [Party A] and [Party B] - **Pages**: [X] ### Pre-Signature Check: [PASS / ISSUES FOUND] [List any issues that need attention before sending] ### Signing Configuration | Order | Signer | Email | Role | |-------|--------|-------|------| | 1 | [Name] | [email] | [Party A Authorized Signatory] | | 2 | [Name] | [email] | [Party B Authorized Signatory] | ### CC Recipients - [Name] — [email] ### Status [Sent for signature / Ready to send / Issues to resolve first] ### Next Steps - [What to expect after sending] - [Expected turnaround time] - [Follow-up if not signed within X days] ``` ## Tips 1. **Check entity names carefully** — The most common signing error is incorrect legal entity names. 2. **Verify authority** — Make sure each signer is authorized to bind their organization. 3. **Keep a copy** — Executed copies should be filed in ~~cloud storage or ~~CLM immediately after execution.
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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