business/sales/common-room-compose-outreach/SKILL.md
Generate personalized outreach messages using Common Room signals. Triggers on 'draft outreach to [person]', 'write an email to [name]', 'compose a message for [contact]', or any outreach drafting request.
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library compose-outreachInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Generate three personalized outreach formats — email, call script, and LinkedIn message — grounded in Common Room signals for a specific company or contact.
Use Common Room MCP tools to find and retrieve data for the target (company and/or specific contact). Pull:
If the user specified a person, run contact-level research. If only a company was given, identify the best contact to target based on title, engagement, and role.
If CR returned strong signals (recent activity, engagement, product usage), those should drive personalization — skip web search. If CR signals are thin or the prospect has little CR activity, run a web search for external hooks:
What to search:
"[company name]" funding OR acquisition OR launch OR announcement — last 30 days"[contact full name]" "[company name]" — look for recent articles, interviews, LinkedIn posts, or conference talksPrioritize external hooks that are:
If the user explicitly asks for web search or external hooks, run it regardless of CR signal richness.
If Spark is available, run enrichment on the target contact to get persona classification, background, and influence signals. Use this to calibrate tone and message angle.
From the signal data, identify the 1–3 strongest personalization hooks. Rank by:
Good hooks: posted a question in the community about X, just hired 5 engineers, recently started using [feature], company just raised Series B, trial nearing expiration, champion just changed jobs.
Bad hooks: "I noticed you're a customer" or generic industry trends.
Use the strongest hooks to write all three formats. Each format has different constraints and conventions — follow the format-specific guidelines in references/outreach-formats-guide.md.
Always produce all three, clearly labeled.
When the user's company context is available (see references/my-company-context.md), ground the value bridge and pitch in the user's specific product and positioning.
After the three drafts, include a brief note (2–4 sentences) explaining:
## Outreach for [Name / Company]
### 📧 Email
**Subject:** [Subject line]
[Email body — 3–5 sentences]
---
### 📞 Call Script
**Opening:**
[Opening line — conversational, 1–2 sentences]
**Value Bridge:**
[Why you're calling and why now — 2–3 sentences tied to a signal]
**Ask:**
[Single, low-friction ask — e.g., 15-minute call, specific question]
---
### 💼 LinkedIn Message
[Under 300 characters. Warm, personal, no pitch.]
---
### Signal Notes
[2–4 sentences: which signals were used, why, and any alternative angles]
If Common Room returns minimal data on the target (e.g., just name, title, tags — no activity, no scores, no Spark):
## Outreach for [Name / Company] — Limited Data
**What I found:**
[Only the real data from CR and web search]
**I don't have enough signal to draft personalized outreach yet.** To write something strong, I'd need:
- Recent activity or engagement signals
- Context you have from prior conversations
- A specific reason for reaching out now
Can you share any of the above?
references/outreach-formats-guide.md — detailed format rules, examples, and tone guidelines for each channeltesting
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.
tools
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.
testing
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".
development
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.