dv-solution

A tool for packaging and moving Microsoft Dataverse customizations between development, test, and production environments. It can create, export, unpack, pack, import, and validate these packages.

In plain words
What is it for?
Use it to package customizations, promote them across environments, and validate deployments through the PAC command-line tool or Dataverse web APIs.
Why use it?
It removes much of the manual work involved in moving application changes between Dataverse environments and checking that deployments succeeded.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/microsoft/dataverse-skills/dv-solution
Any agent
npx skills add microsoft/Dataverse-skills --skill dv-solution
Clone the repo
git clone --depth 1 https://github.com/microsoft/Dataverse-skills

Made for: Claude Code, Codex.

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,923 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
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 $0.00048 $0.02923
Opus 5 $0.00024 $0.01461
Sonnet 5 $0.00010 $0.00585
Haiku 4.5 $0.00005 $0.00292

Measured 2d ago against content hash b847302a0b8c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dv-solution scanned grade A with 1 finding 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 2d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

**Use the Python SDK for publisher and solution record creation — not raw HTTP.** Publishers and solutions are standard Dataverse tables. `client.records.create()` and `client.records.list()` handle auth, pagination, and
.github/plugins/dataverse/skills/dv-solution/SKILL.md · 333 lines

How it starts

The opening of the file, as written. The whole thing — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Solution

Create, export, unpack, pack, import, and validate Dataverse solutions via PAC CLI. Includes post-import validation using the Python SDK.

Headless / restricted-egress hosts: use the raw Web API (ExportSolution / ImportSolution) for the online steps. pac solution pack/unpack are local file operations (no auth) but need a host that can run PAC -- do them on a capable machine or CI runner. Verify egress with python scripts/auth.py --check. See dv-connect/references/headless-hosts.md.

Skill boundaries

Need Use instead
Create tables, columns, relationships, forms, views dv-metadata
Create, update, or delete data records dv-data
Query or read records dv-query
Connect to Dataverse / set up MCP dv-connect

Create a New Solution

Use the Python SDK for publisher and solution record creation — not raw HTTP. Publishers and solutions are standard Dataverse tables. client.records.create() and client.records.list() handle auth, pagination, and error handling automatically, avoiding the URL encoding, header boilerplate, and GUID-parsing bugs that raw urllib calls introduce.

Step 1: Find or Create the Publisher

Every solution belongs to a publisher. The publisher's customizationprefix (e.g., contoso, sa, lit) is prepended to every custom table, column, and relationship schema name. This prefix is effectively permanent — existing components keep their prefix forever, even if you change the publisher later.

Never use the default new prefix. It provides no organizational identity, risks naming collisions, and signals the developer did not follow best practices.

Discovery flow — always run this before creating a publisher:

import os, sys
sys.path.insert(0, os.path.join(os.getcwd(), "scripts"))
from auth import get_client

# get_client sets a plugin attribution context on the User-Agent header.
# Do not modify the context value — it is a closed schema for server-side
# telemetry (app/skill/agent). Never include secrets or PII.
client = get_client("dv-solution")

# 1. Query for existing non-Microsoft publishers
publishers = client.records.list(
    "publisher",
    filter="customizationprefix ne 'none' and uniquename ne 'MicrosoftCorporation' and uniquename ne 'Microsoftdynamic'",
    select=["publisherid", "uniquename", "friendlyname", "customizationprefix"],
    top=10,
)

if publishers:
    # Show existing publishers and ask user which to use
    print("Existing publishers in this environment:")
    for p in publishers:
        print(f"  {p['uniquename']} (prefix: {p['customizationprefix']}_)")
    # ASK THE USER: "Which publisher should this solution use?"
    # Or: "Should I reuse '<name>' (prefix: <prefix>_)?"
    publisher_id = publishers[0]["publisherid"]  # after user confirms
else:
    # No custom publisher exists — ASK THE USER for prefix
    # "What publisher prefix should I use? (e.g., 'contoso', 'sa', 'lit' — 2-8 lowercase chars)"
    publisher_id = client.records.create("publisher", {
        "uniquename": "<publisheruniquename>",
        "friendlyname": "<Publisher Display Name>",
        "customizationprefix": "<prefix>",   # from user input, NOT 'new'
        "description": "<description>",
    })

Read the full file on GitHub · 333 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. 2d ago First seen · 333 lines · 48 tokens per session scan A b847302a0b8c

Subscribe to this mod's changes

dv-solution is a skill published in the GitHub repository microsoft/Dataverse-skills (213 stars, last pushed 3d ago), licensed MIT. It adds 48 tokens to every session and 2,923 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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