draft-outreach

draft-outreach is a skill for Claude Code, Codex from microsoft/dataverse-business-skills. It costs 88 tokens per session (3,490 once invoked), scanned A, original, MIT.

A sales-writing helper that uses customer records in Dataverse, Microsoft's business data platform, to draft personalized outreach emails.

In plain words
What is it for?
Drafting introductory emails, re-engagement messages, follow-ups, cross-sell or renewal outreach, and event-based messages.
Why use it?
It removes the blank-page work and helps avoid generic messages by using known contact details, past interactions, needs, or owned products.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one.

Good fit Drafting introductory emails, re-engagement messages, follow-ups, cross-sell or renewal outreach, and event-based messages.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/microsoft/dataverse-business-skills/draft-outreach
About the project

Dataverse Business Skills is a collection of natural-language instructions that teach AI agents how to follow business processes, policies, and domain knowledge for Microsoft Dataverse. Organizations use the skills with Dataverse environments connected to products such as Power Apps, Dynamics 365, or Power Platform. The catalogue entries are skills from this collection.

microsoft/dataverse-business-skills · 50 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add microsoft/dataverse-business-skills --skill draft-outreach
Clone the repo
git clone --depth 1 https://github.com/microsoft/dataverse-business-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for draft-outreach

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/draft-outreach/github.svg)](https://agentmods.dev/skills/microsoft/dataverse-business-skills/draft-outreach)
Your own site
<a href="https://agentmods.dev/skills/microsoft/dataverse-business-skills/draft-outreach"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/draft-outreach/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for draft-outreach

Your own site · 80×15
<a href="https://agentmods.dev/skills/microsoft/dataverse-business-skills/draft-outreach"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/draft-outreach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,490 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00088 $0.03490
Opus 5 $0.00044 $0.01745
Sonnet 5 $0.00018 $0.00698
Haiku 4.5 $0.00009 $0.00349

Measured 9d ago against content hash 15a17353b35a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

draft-outreach scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/draft-outreach/SKILL.md · 457 lines

How it starts

The opening of the file, as written. The whole thing — 457 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Draft Outreach

Generic outreach gets ignored. Personalized messages that reference real context — a recent interaction, a known pain point, or a relevant product already owned — get responses. This skill eliminates the blank-page problem by pulling Dataverse data to craft contextually relevant outreach, saving reps time while improving reply rates. It works within Dataverse without requiring external enrichment tools.

Instructions

Step 1: Identify Target Contact and Outreach Trigger

Accept input from the user:

  • Target: Contact name, lead name, or account name
  • Outreach type: New intro, re-engagement, follow-up, cross-sell, renewal, or event-based
  • Goal: Meeting request, proposal follow-up, demo invite, check-in, etc.
  • Tone: Formal, conversational, or brief (default: conversational)
Step 2: Retrieve Contact or Lead Record

If contact:

SELECT contactid, fullname, firstname, jobtitle, accountid, emailaddress1,
       telephone1, description, createdon, modifiedon
FROM contact
WHERE fullname LIKE '%[name]%'

If lead:

SELECT leadid, fullname, firstname, jobtitle, companyname, emailaddress1,
       telephone1, description, leadsourcecode, leadqualitycode, createdon
FROM lead
WHERE statecode = 0
AND fullname LIKE '%[name]%'

Present matches if multiple found. Confirm the target with the user before proceeding.

Step 3: Gather Account Context

If a contact was found, retrieve their account:

SELECT accountid, name, industrycode, revenue, numberofemployees,
       description, address1_city, address1_stateorprovince,
       customertypecode, accountcategorycode
FROM account
WHERE accountid = '[accountid]'
Step 4: Review Relationship History

Recent activities (last 90 days):

SELECT activityid, activitytypecode, subject, actualend, description,
       statecode, regardingobjectid
FROM activitypointer
WHERE regardingobjectid = '[contactid or accountid]'
AND statecode = 1
ORDER BY actualend DESC

Limit to 10 most recent. Note:

  • Days since last interaction
  • Last activity type and topic
  • Any commitments made (from description)

Read the full file on GitHub · 457 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 457 lines · 88 tokens per session scan A 15a17353b35a

Subscribe to this mod's changes

draft-outreach is a skill published in the GitHub repository microsoft/dataverse-business-skills (50 stars, last pushed 6mo ago), licensed MIT. It adds 88 tokens to every session and 3,490 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens