dv-solution skill
✓ OfficialManages the Dataverse solution lifecycle including creation, exporting, unpacking, packing, and importing across environments using PAC CLI and Python SDK.
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- Nov 11, 2026
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What does the dv-solution skill do?
dv-solution manages the Dataverse solution lifecycle including creation, exporting, unpacking, packing, and importing across environments using PAC CLI and Python SDK.
Summary generated from the skill's SKILL.md.
About the dv-solution skill
Managing the lifecycle of Dataverse solutions is what developers do when they need to package customizations, move work between development and production environments, or validate deployments. This skill automates the creation, exporting, unpacking, packing, and importing of solutions using the PAC CLI and a Python SDK. It is designed for engineers who need to promote changes across different environments while maintaining a clean source of truth through unmanaged solution files.
An agent should load this skill when a user needs to create a new publisher, add components like tables or forms to a solution, or deploy a packaged solution to a target environment. The skill relies on the PAC CLI for file operations and the Python SDK for interacting with Dataverse records, such as creating solution records or performing post-import validation. It distinguishes itself by providing structured validation steps, such as checking if tables exist or if forms are published, to ensure deployments were successful.
What an agent can do with it
- 1Create a new solutionSet up a new Dataverse solution by first finding or creating a publisher with a specific customization prefix.
- 2Move customizations between environmentsExport a solution from a development environment, unpack it into editable files, and then pack and import it into a test environment.
- 3Verify deployment successCheck that specific tables, forms, or views are live and functional in the target environment after an import job completes.
- 4Add components to solutionsAttach existing tables, views, or canvas apps to a specific solution using component type codes.
Written from the skill's SKILL.md. Every use case is checked against a quote from it.
How to install the dv-solution skill
Each command copies the skill folder into the client's skills directory. Restart the client or start a new session to load it.Install the dv-solution skill in Plugin
/plugin marketplace add microsoft/Dataverse-skills /plugin install dataverse@dataverse-skills
Install the dv-solution skill in Claude Code
~/.claude/skills/dv-solution/SKILL.md
$ npx degit microsoft/Dataverse-skills/.github/plugins/dataverse/skills/dv-solution ~/.claude/skills/dv-solutionInstall the dv-solution skill in Claude Code (project)
.claude/skills/dv-solution/SKILL.md
$ npx degit microsoft/Dataverse-skills/.github/plugins/dataverse/skills/dv-solution .claude/skills/dv-solutionInstall the dv-solution skill in Codex
~/.agents/skills/dv-solution/SKILL.md
$ npx degit microsoft/Dataverse-skills/.github/plugins/dataverse/skills/dv-solution ~/.agents/skills/dv-solutionInstall the dv-solution skill in Gemini CLI
~/.gemini/skills/dv-solution/SKILL.md
$ npx degit microsoft/Dataverse-skills/.github/plugins/dataverse/skills/dv-solution ~/.gemini/skills/dv-solutionInstall the dv-solution skill in Claude.ai
npx degit microsoft/Dataverse-skills/.github/plugins/dataverse/skills/dv-solution dv-solution && zip -r dv-solution.zip dv-solution # claude.ai → Customize → Skills → + → Create skill → Upload a skill → dv-solution.zip
SKILL.md
View on GitHubSkill: 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/unpackare local file operations (no auth) but need a host that can run PAC -- do them on a capable machine or CI runner. Verify egress withpython scripts/auth.py --check. Seedv-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>",
})
Rules:
- Always ask the user before creating a new publisher or choosing a prefix. Never hardcode a prefix.
- The prefix must match any tables already created in the solution — you cannot mix prefixes.
- One publisher can own many solutions. Reuse an existing publisher when possible.
Step 2: Create the Solution Record
Use the SDK to create the solution record (preferred over raw Web API):
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")
# Create the solution record
solution_id = client.records.create("solution", {
"uniquename": "<UniqueName>",
"friendlyname": "<Display Name>",
"version": "1.0.0.0",
"publisherid@odata.bind": "/publishers(<publisher_guid>)",
})
print(f"Created solution: {solution_id}")
The required fields:
Table: solution
Fields: uniquename = "<UniqueName>"
friendlyname = "<Display Name>"
version = "1.0.0.0"
publisherid = <publisher GUID from step 1>
Note: There is no
pac solution createcommand. PAC CLI handles export/import/pack/unpack, not solution record creation. Use the SDK or Web API to create the record.
Step 3: Add Components
Use pac solution add-solution-component to add tables, forms, views, and other components:
pac solution add-solution-component \
--solutionUniqueName <UniqueName> \
--component <ComponentSchemaName> \
--componentType <TypeCode> \
--environment <url>
Note: PAC CLI uses camelCase args here (
--solutionUniqueName,--componentType), not kebab-case.
Common component type codes:
| Type Code | Component |
|---|---|
| 1 | Entity (Table) |
| 2 | Attribute (Column) |
| 26 | View |
| 60 | Form |
| 61 | Web Resource |
| 300 | Canvas App |
| 371 | Connector |
Repeat the command for each component you need to add.
Alternative: Auto-add via MSCRM.SolutionName Header
When creating metadata via the Web API, include the MSCRM.SolutionName header to auto-add components to the solution:
headers = {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json",
"MSCRM.SolutionName": "<UniqueName>"
}
Important: After using this approach, verify components were added by querying the solutioncomponent table with the SDK (pac solution list-components is not available in current PAC):
sol = client.records.list("solution",
filter="uniquename eq '<UniqueName>'", select=["solutionid"], top=1).first()
if sol is not None:
components = client.records.list("solutioncomponent",
filter=f"_solutionid_value eq {sol['solutionid']}",
select=["componenttype", "objectid"])
print(f"{len(components)} components in the solution")
If the header was misspelled or the solution doesn't exist, components will be created in the default solution instead — silently. Always verify.
Find the Solution Name
Before exporting, confirm the exact unique name:
pac solution list --environment <url>
The UniqueName column is what you pass to other commands. Display names have spaces; unique names do not.
Pull: Export + Unpack
Confirm the target environment before exporting or importing. Run
pac auth list+pac org who, show the output to the user, and confirm it matches the intended environment. Developers work across multiple environments — do not assume.
Export the solution as unmanaged (source of truth):
pac solution export \
--name <UniqueName> \
--path ./solutions/<UniqueName>.zip \
--managed false \
--environment <url>
Unpack into editable source files:
pac solution unpack \
--zipfile ./solutions/<UniqueName>.zip \
--folder ./solutions/<UniqueName> \
--packagetype Unmanaged
Windows file-lock race. Run export and unpack as separate commands (as above); chaining them immediately can hit a transient ZIP file-lock right after export. If
unpackfails with a lock / "in use" error, retry after a moment, and verify the unpacked folder has the expected components before deleting the zip.
Delete the zip — the unpacked folder is the source:
rm ./solutions/<UniqueName>.zip
Commit:
git add ./solutions/<UniqueName>
git commit -m "chore: pull <UniqueName> baseline"
git push
Push: Pack + Import
Pack the source files back into a zip:
pac solution pack \
--zipfile ./solutions/<UniqueName>.zip \
--folder ./solutions/<UniqueName> \
--packagetype Unmanaged
Import (async recommended for large solutions):
pac solution import \
--path ./solutions/<UniqueName>.zip \
--environment <url> \
--async \
--activate-plugins
Poll Import Status
After async import, check the job:
pac solution list --environment <url>
Post-Import Validation
After importing a solution, verify that components are live. Use the Python SDK to check directly — no external scripts needed.
Check a table exists
info = client.tables.get("<logical_name>")
if info:
print(f"[PASS] Table '{info.logical_name}' exists")
else:
print(f"[FAIL] Table '<logical_name>' not found")
Check a form is published
forms = client.records.list(
"systemform",
filter="objecttypecode eq '<entity>' and type eq <form_type_code>",
select=["name", "formid"],
top=5,
)
# Form type codes: 2 = main, 7 = quick create
Check a view exists
views = client.records.list(
"savedquery",
filter="returnedtypecode eq '<entity>'",
select=["name", "savedqueryid", "statuscode"],
top=10,
)
Check a user's role assignment (N:N $expand)
records.list passes $expand straight through, so read the N:N navigation property directly with the SDK:
users = list(client.records.list(
"systemuser",
filter="internalemailaddress eq '<email>'", # fallback: domainname eq '<upn>'
select=["fullname"],
expand=["systemuserroles_association($select=name)"],
top=1,
))
roles = [r["name"] for r in users[0].get("systemuserroles_association", [])] if users else []
Alternatively, the managed Dataverse CLI escape hatch (dataverse api request — not urllib), or FetchXML with a link-entity:
dataverse api request --target dataverse --method GET \
--path "/api/data/v9.2/systemusers?%24filter=internalemailaddress eq '<email>'&%24select=fullname&%24expand=systemuserroles_association(%24select=name)&%24top=1" \
--environment <DATAVERSE_URL> \
--context "app=dataverse-skills/<ver>;skill=dv-solution;agent=<agent>"
The response value[0].systemuserroles_association is the list of assigned roles (each with name).
Check import errors
jobs = client.records.list(
"importjob",
select=["importjobid", "solutionname", "startedon", "completedon", "progress"],
orderby=["startedon desc"],
top=5,
)
For detailed error history, also query msdyn_solutionhistory:
history = client.records.list(
"msdyn_solutionhistory",
filter="msdyn_status eq 1", # 1 = failed
select=["msdyn_name", "msdyn_starttime", "msdyn_exceptionmessage"],
orderby=["msdyn_starttime desc"],
top=5,
)
Validation error reference
| Error | Cause | Fix |
|---|---|---|
| Table not found after import | Component not in solution | Add via pac solution add-solution-component |
| Form check fails immediately | Publishing is async | Wait 30 seconds and retry |
| Role not assigned | User not provisioned | Assign the role via pac admin assign-user or the Power Platform Admin Center |
| Import job at 0% | Import still running | Poll again in 60 seconds |
Notes
- Always use
--managed false/--packagetype Unmanagedfor the development solution. Managed packages are for deployment to downstream environments (test, prod). --activate-pluginsensures any registered plugins in the solution are activated on import.- If you see "solution already exists" errors, use
--import-mode ForceUpgradeto overwrite. - Large solutions (Sales, Customer Service) can take 10–20 minutes to import. Be patient and poll rather than re-importing.
- All validation queries above require auth. Use
scripts/auth.pyfor credential/token acquisition. Seedv-queryfor SDK query patterns anddv-datafor write patterns.
Files in this skill
1dv-solution skill questions
How do I install the dv-solution skill in Claude Code?
Run npx degit microsoft/Dataverse-skills/.github/plugins/dataverse/skills/dv-solution ~/.claude/skills/dv-solution. Claude Code loads skills from ~/.claude/skills (all projects) or .claude/skills (one project) and uses a skill when its description matches the task.
Which clients can use the dv-solution skill?
Any client that reads the Agent Skills format: Claude Code, Codex, Gemini CLI and claude.ai (upload as a ZIP). Scripts in the skill run only where the client allows code execution.
What is the difference between managed and unmanaged solutions?
Unmanaged solutions are used for development and serve as the source of truth. Managed packages are intended for deployment to downstream environments like test or production.