القائمة
Manages connected MCP sources for enterprise search. Detects available sources and handles source priority ordering.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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.
Strategic scientific problem selection, project ideation, and troubleshooting based on the Fischbach & Walsh framework.
Deep learning for single-cell analysis using scvi-tools and scverse ecosystem. 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, (7) RNA velocity with veloVI, or (8) QC analysis of single-cell RNA-seq data. Triggers include scVI, scANVI, totalVI, QC, quality control, batch correction, integration, multi-modal.
A conversational framework for systematic scientific problem selection, project ideation, troubleshooting, and strategic decision making.
| name | enterprise-source-management |
| description | Manages connected MCP sources for enterprise search. Detects available sources and handles source priority ordering. |
If you see unfamiliar placeholders or need to check which tools are connected, check your MCP settings.
Knows what sources are available, helps connect new ones, and manages how sources are queried.
Determine which MCP sources are connected by checking available tools. Each source corresponds to a set of MCP tools:
| Source | Key capabilities |
|---|---|
| ~~chat | Search messages, read channels and threads |
| Search messages, read individual emails | |
| ~~cloud storage | Search files, fetch document contents |
| ~~project tracker | Search tasks, typeahead search |
| ~~CRM | Query records (accounts, contacts, opportunities) |
| ~~knowledge base | Semantic search, keyword search |
If a tool prefix is available, the source is connected and searchable.
When a user searches but has few or no sources connected:
You currently have [N] source(s) connected: [list].
To expand your search, you can connect additional sources in your MCP settings:
- ~~chat — messages, threads, channels
- ~~email — emails, conversations, attachments
- ~~cloud storage — docs, sheets, slides
- ~~project tracker — tasks, projects, milestones
- ~~CRM — accounts, contacts, opportunities
- ~~knowledge base — wiki pages, knowledge base articles
The more sources you connect, the more complete your search results.
When a user asks about a specific tool that is not connected:
[Tool name] isn't currently connected. To add it:
1. Open your MCP settings
2. Add the [tool] MCP server configuration
3. Authenticate when prompted
Once connected, it will be automatically included in future searches.
Different query types benefit from searching certain sources first. Use these priorities to weight results, not to skip sources:
Decision queries ("What did we decide..."):
1. ~~chat (conversations where decisions happen)
2. ~~email (decision confirmations, announcements)
3. ~~cloud storage (meeting notes, decision logs)
4. Wiki (if decisions are documented)
5. Task tracker (if decisions are captured in tasks)
Status queries ("What's the status of..."):
1. Task tracker (~~project tracker — authoritative status)
2. ~~chat (real-time discussion)
3. ~~cloud storage (status docs, reports)
4. ~~email (status update emails)
5. Wiki (project pages)
Document queries ("Where's the doc for..."):
1. ~~cloud storage (primary doc storage)
2. Wiki / ~~knowledge base (knowledge base)
3. ~~email (docs shared via email)
4. ~~chat (docs shared in channels)
5. Task tracker (docs linked to tasks)
People queries ("Who works on..." / "Who knows about..."):
1. ~~chat (message authors, channel members)
2. Task tracker (task assignees)
3. ~~cloud storage (doc authors, collaborators)
4. ~~CRM (account owners, contacts)
5. ~~email (email participants)
Factual/Policy queries ("What's our policy on..."):
1. Wiki / ~~knowledge base (official documentation)