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Official agent skill

Dv Overview

by microsoft in microsoft/Dataverse-skills

Foundational cross-cutting context for Dataverse / Power Platform work — scope and the skill map, the tool-capability reference, the safety rules, and the safe change lifecycle.

OfficialMITAuto-check: notesSales & Support

Install Dv Overview

skills CLI
$ npx skills add microsoft/Dataverse-skills --skill dv-overview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install microsoft/Dataverse-skills dv-overview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/microsoft/Dataverse-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/plugins/dataverse/skills/dv-overview .claude/skills/dv-overview && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dv-overview
GitHub stars
241
Token cost
~5.3k tokens
SKILL.md length
2,595 words
Files
3 (incl. references)
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Foundational cross-cutting context for Dataverse / Power Platform work — scope and the skill map, the tool-capability reference, the safety rules, and the safe change lifecycle.

  • Works in 8 steps: Check Init State First → Python for scripting; the CLIs and MCP… → Pick the surface that fits — capability… → …
  • The user mentions Dataverse
  • SKILL.md covers What This Plugin Covers, Hard Rules, Tool Capabilities — Which Tool… and The Change Lifecycle — Operate…, plus 2 more sections
  • Calls git, python and pip

What it does

Dv Overview is an agent skill from microsoft/Dataverse-skills, published by the product's own GitHub organization. Foundational cross-cutting context for Dataverse / Power Platform work — scope and the skill map, the tool-capability reference, the safety rules, and the safe change lifecycle. Use when the user mentions Dataverse, Dynamics 365, Power Platform, CRM, or ERP; load this first for orientation. Specialist skills self-route via their own frontmatter triggers. Finance and Operations / F&O business-data requests also require this skill first, then dv-query for reads.

Its SKILL.md is about 5.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/erp-target.md` and `references/windows-scripting.md`).

It sits in Sales & Support. It works with Power Automate, Model Context Protocol and Microsoft Azure. The repository describes itself as: Microsoft Dataverse skills for AI coding agents. Wraps the Dataverse MCP server, Dataverse CLI, Python SDK, and PAC CLI behind specialist skills for building, querying… The licence is MIT.

When your agent uses it

  • The user mentions Dataverse
  • Load this first for orientation

Example prompts

  • “/dv-overview”

Requirements

  • Python 3
  • Node.js

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Check Init State First
  2. Python for scripting; the CLIs and MCP are first-class
  3. Pick the surface that fits — capability awareness, not a fixed order
  4. Use Documented Auth Patterns
  5. Be honest about gaps — don't hallucinate
  6. Confirm the Environment (MANDATORY)
  7. Confirm the Solution (before any metadata change)
  8. Pull to Repo (MANDATORY)

What it can do on your machine

Read from SKILL.md and the folder at commit 3be592f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • python
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git and pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dv Overview loads about 5.3k tokens when it runs, and up to ~8.1k if it reads all its reference files. Until then it costs about 119 tokens; SKILL.md has 2,595 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~119
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:44
    auth is host-managed and does not need `.env` or `scripts/auth.py`. Never declare MCP unavailable based solely on the i
  • NoteMentions a .env fileSKILL.md:52
    see `dv-query`/`dv-data` examples) — no `.env`, `auth.py`, or workspace setup needed. For explicit "connect" or "set up"
  • NoteMentions a .env fileSKILL.md:57
    ls .env scripts/auth.py 2>/dev/null
  • NoteMentions a .env fileSKILL.md:104
    reate` (or `pac auth create`), or check `.env`. See the `dv-connect` skill.
  • NoteMentions a .env fileSKILL.md:179
    the active PAC auth profile, values in `.env`, or anything from memory or a previous session reflects the correct targe
  • NoteMentions a .env fileSKILL.md:196
    2. If a solution name is in `.env` (`SOLUTION_NAME`), confirm it with the user

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from microsoft/Dataverse-skills at commit 3be592f, republished under its MIT licence (© microsoft). 2,595 words, ~5,338 tokens.

Download SKILL.mdSave it as .claude/skills/dv-overview/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
dv-overview
description
Foundational cross-cutting context for Dataverse / Power Platform work — scope and the skill map, the tool-capability reference, the safety rules, and the safe change lifecycle. Use when the user mentions Dataverse, Dynamics 365, Power Platform, CRM, or ERP; load this first for orientation. Specialist skills self-route via their own frontmatter triggers. Finance and Operations / F&O business-data requests also require this skill first, then dv-query for reads.

Skill: Overview — What to Use and When

Load this skill first for any Dataverse work — it holds the cross-cutting context every task needs: scope, the tool-capability reference, the hard rules, and the change lifecycle. It does not route; the agent auto-selects specialist skills via their own WHEN/DO NOT USE WHEN frontmatter triggers. Users describe what they want in plain English; the agent chains skills automatically and never asks the user to name a skill or command.

What This Plugin Covers

Dataverse / Power Platform work for every persona — builders and agent devs, data scientists, environment admins, and business users — delivered by specialist skills. The agent loads and routes to these automatically via their frontmatter triggers — you never invoke them by name.

AreaSkill
Connect, authenticate, configure MCP, verify the environmentdv-connect
Schema — tables, columns, relationships, forms, views; inspect existing schemadv-metadata
Data writes — record CRUD, bulk create/update/upsert, CSV/FK-ordered import, sample datadv-data
Data reads & analytics — OData queries, QueryBuilder, FetchXML (aggregation + N:N joins), DataFramesdv-query
Solution ALM — create, export, import, pack/unpack, post-import validationdv-solution
Finance and Operations X++ — author, build, deploy, DB sync, verifyerp-xpp
Environment administration — bulk delete, retention/archival, org & OrgDB settings, recycle bindv-admin
Security & access — roles, users, application users, business units, self-elevation (PAC CLI)dv-security

Model-driven apps: the building blocks (tables, forms, views) are covered by dv-metadata; composing the app shell itself — site map and navigation — is not yet a first-class skill.

Out of scope:

  • Canvas apps — a different technology; use pac canvas or the maker portal
  • Power Automate flows — use the maker portal or the Power Automate Management API
  • Azure infrastructure beyond what's needed for service-principal setup
  • Business Central or other Dynamics products

Hard Rules

Safety rules (init, auth, env confirmation) are non-negotiable. Tool selection (Rules 1, 2, 4) is capability-based.

0. Check Init State First

Before writing ANY code or creating ANY files, actively search your callable tools for any tool whose name or description contains dataverse (tools may be registered under environment-specific names like mcp__dataverse_<orgid>__read_query, not just generic names). If any Dataverse MCP tool is found, use it directly — skip the init check and all setup. MCP auth is host-managed and does not need .env or scripts/auth.py. Never declare MCP unavailable based solely on the initially displayed tool list.

If no MCP tool is found, check for an existing CLI profile — it's the fastest path for data operations:

bash
dataverse auth who

If that shows an active profile with an environment URL, use CLI directly for data operations (see dv-query/dv-data examples) — no .env, auth.py, or workspace setup needed. For explicit "connect" or "set up" requests, run dv-connect regardless — it configures MCP, SDK, and PAC.

If no CLI profile exists, check workspace init:

bash
ls .env scripts/auth.py 2>/dev/null
  • If BOTH exist: proceed to the task.
  • If EITHER is missing: run python <plugin-scripts>/auth.py --ping. If it prints REACHABLE (exit 0), the workspace is bootable without pip -- confirm the URL and proceed. If --ping fails, run dv-connect.
1. Python for scripting; the CLIs and MCP are first-class

Python is the language for automation logic (transformation, control flow, retry, CSV). The toolchain (scripts/auth.py, the SDK, skill examples) is Python-based. But MCP tools, the Dataverse CLI (dataverse), the Python SDK, and the PAC CLI (pac) are all first-class tool invocations — use whichever fits. The Dataverse CLI has the same standing as pac, which is invoked freely across the solution and metadata skills.

NEVER:

  • Write automation logic in JavaScript/TypeScript/Node.js (npm, yarn, pnpm, package.json, node_modules/)
  • Use @azure/msal-node, @azure/identity, or any Node.js Azure SDK
  • Implement a bespoke MSAL / device-code flow — auth is scripts/auth.py, pac auth, and the Dataverse CLI

ALWAYS:

  • Use pip install and the Python SDK (PowerPlatform-Dataverse-Client) for data and schema logic
  • Use scripts/auth.py for tokens/credentials; azure-identity (Python) for Azure credential flows
  • Treat the Dataverse CLI (dataverse) and pac as allowed first-party CLIs
2. Pick the surface that fits — capability awareness, not a fixed order

No mandated tool order. Each surface has a capability profile; pick what fits the job and the surface you are already in — soft defaults, not a required sequence. The full matrix is in Tool Capabilities below; the principles:

  • Prefer a managed surface (MCP, the Dataverse CLI, or the SDK) over hand-rolled raw OData — they carry auth, paging, retry, and geo routing that raw HTTP re-implements. When MCP can't handle it (bulk >25 records, large reads, advanced schema like forms/views/N:N relationships/global option sets/alternate keys, multi-step workflows, analytics, or MCP isn't available), the Python SDK is the default.
  • Raw Web API is the last-resort escape hatch for surfaces with no managed path (unbound actions like PublishXml, global option sets, anything without a first-class SDK/CLI command) — and even then prefer dataverse api (managed auth, exit codes) over hand-rolled urllib/get_token. Forms/views are not raw-only (SDK records.create/update on systemform/savedquery; only PublishXml needs dataverse api). Aggregation/N:N joins aren't raw-only either: client.query.fetchxml() (aggregates + link-entity), or the CLI's data associate for N:N writes.
  • If an SDK method fails or a PAC command seems missing, check the relevant skill before hand-rolling raw HTTP.

Field casing: $select/$filter use lowercase logical names (new_name). $expand and @odata.bind use Navigation Property Names that are case-sensitive and must match $metadata (e.g., new_AccountId). Getting this wrong causes 400 errors. SDK record payloads: provide the correct SchemaName casing on @odata.bind keys (e.g., new_AccountId@odata.bind); the SDK does not auto-correct wrong casing. Raw Web API calls (forms, views, metadata): casing is entirely manual — a lowercase new_accountid@odata.bind will 400.

Publisher prefix: Never hardcode a prefix (especially new); query existing publishers and ask the user. The prefix is permanent. See the solution skill's publisher discovery flow.

3. Use Documented Auth Patterns

Three entry points, one shared sign-in:

  • dataverse auth create (Dataverse CLI) writes a shared MSAL token cache. That sign-in serves CLI, MCP proxy, and scripts/auth.py via msal-extensions.
  • scripts/auth.py is the Python/SDK auth entry point. Order: service principal → shared CLI cache → device-code. Use get_client(skill) (SDK) or get_plugin_headers(skill, get_token()) (raw Web API) — both stamp attribution.
  • pac auth create (PAC CLI) authenticates pac for dv-solution, dv-admin, and erp-xpp.

Telemetry attribution (keep it deterministic): every request carries a closed-schema app=dataverse-skills/<ver>;skill=<skill>;agent=<agent> context so the server sees which skill routed each OData call. It is baked in — get_client(skill) and get_plugin_headers(skill, ...) stamp it on the SDK and raw-HTTP paths; the Dataverse CLI auto-stamps DataverseCli/<ver> + the command, and you add the skill with --context "app=dataverse-skills/<ver>;skill=<skill>;agent=<agent>" (the CLI wraps it in parentheses itself — do not pre-wrap). Never modify, omit, or free-form this context — it is a closed schema (allowlisted skill/agent, no PII).

NEVER:

  • Read or parse raw token cache files (e.g., tokencache_msalv3.dat) — reuse the cache only through scripts/auth.py / msal-extensions
  • Implement your own MSAL device-code flow
  • Hard-code tokens or credentials in scripts
  • Invent a new auth mechanism

If auth is expired or missing, re-run dataverse auth create (or pac auth create), or check .env. See the dv-connect skill.

4. Be honest about gaps — don't hallucinate

Each skill documents a tested sequence — follow it when it fits. The skills are the source of truth for the supported, non-deprecated API. If a call fails with AttributeError, the installed SDK version may not have it — check the skill's version note and use the documented alternative.

The honesty guard: if you hit a gap the skills don't cover, say so and suggest a workaround. Do not hallucinate an unsupported path — do not invent a method, parameter, or endpoint that isn't documented. If unsure, say so.

Connectivity is not auth. A login.microsoftonline.com token can succeed while the org's data-plane domain is unreachable (restricted-egress hosts like ChatGPT Work Mode). Never report a count or result you didn't get from a real call that returned — verify with python scripts/auth.py --check; if it fails, say "unreachable," never a fabricated number. On a constrained host, lead with the SDK, not CLI/MCP. See dv-connect/references/headless-hosts.md.

Show full SKILL.md (1,327 more words)Show less

Tool Capabilities — Which Tool for Which Job

Understanding the real limits of each tool prevents hallucinated paths. This is the one piece of context no individual skill owns.

ToolUse forDoes NOT support
MCP ServerData CRUD (create/read/update/delete records, batch up to 25 per call), table create/update/delete + column add (incl. local choice/multiselect + lookup/customer), schema + record inspection via describe, metadata search (search), data + file-content search (search_data, when Dataverse search is enabled), file upload/downloadForms, Views, global Option Sets, N:N relationships, alternate keys, Solutions (lookup + local choice/multiselect columns are supported via create_table/update_table). Note: table creation may timeout but still succeed — always describe (e.g. describe('tables/{name}')) before retrying. Run queries sequentially (parallel calls timeout). Column names with spaces normalize to underscores (e.g., "Specialty Area" → cr9ac_specialty_area). SQL (read_query): supports JOIN, GROUP BY (COUNT/SUM/AVG/MIN/MAX), TOP, WHERE, ORDER BY; does NOT support DISTINCT, HAVING, subqueries, OFFSET, UNION, CASE/IF, CAST/CONVERT, CTE, or date functions. For those, use client.query.sql() (also allows DISTINCT, <5K rows), $apply, or a builder->DataFrame with pandas — see dv-query. Bulk: MCP create_record/update_record/delete_record batch up to 25 records per call; for larger bulk use the SDK CreateMultiple — see dv-data.
Python SDK (dv-data)Scripted data writes, especially at volume. Record CRUD, upsert (alternate keys), bulk create/update/upsert (CreateMultiple/UpdateMultiple/UpsertMultiple), CSV import with lookup resolution, file column uploads (chunked >128MB)global Option Sets, record association ($ref), $apply aggregation, table/column/relationship creation (use dv-metadata), custom action invocation
Python SDK (dv-query)Bulk reads and analytics. Multi-page record iteration, OData queries (select/filter/expand/orderby), QueryBuilder fluent API, GUID-free display (formatted values), $expand to resolve lookups, aggregation and N:N joins via client.query.fetchxml() (aggregate FetchXML + link-entity), pandas DataFrame handoff (client.query.builder(...).execute().to_dataframe()) for exports, Jupyter notebook snippetsOData $apply and N:N $expand on the QueryBuilder path — use records.list(expand=...) for N:N, or fetchxml() for aggregates (not raw urllib)
Dataverse CLI (dataverse)Headless data plane: data CRUD, associate/disassociate (N:N + $ref), data upload; api request/invoke (Web API escape hatch); api list/describe (Custom API discovery)Metadata/schema (use SDK — dv-metadata), solution ALM (use PAC), forms/views; blocked on ChatGPT web / Codex cloud (no .NET runtime) — use the SDK
PAC CLISolution export/import/pack/unpack, ERP X++ SDK install + package scaffold/compile/deploy/DB sync, environment create/list/delete/reset, auth profile management, plugin updates (pac plugin push — first-time registration requires Web API), user/role assignment (pac admin assign-user), add solution components (pac solution add-solution-component)Data CRUD, Dataverse metadata creation (tables/columns/forms), listing solution components (no list-components — query solutioncomponent via SDK/CLI); X++ SDK install/compile require Windows
Azure CLIApp registrations, service principals, credential managementDataverse-specific operations
GitHub CLIRepo management, GitHub secrets, Actions workflow statusDataverse-specific operations
Raw Web API (last resort)Only when no managed surface exposes the operation — i.e. not doable via MCP, the Python SDK, the Dataverse CLI, or the dataverse api escape hatch. Genuine cases: unbound actions like PublishXml, global option sets, and similar edge cases (not forms/views — those are SDK record CRUD on systemform/savedquery). Even then, prefer dataverse api (managed auth + skill attribution) over hand-rolled urllib.Functionally nothing (full OData/MetadataService) — but raw urllib bypasses managed auth, paging, retry, and skill attribution, so treat it as the path of last resort

Routing: the table shows what each surface does; the how to choose principle (soft defaults, not a fixed order) is Hard Rule 2. MCP tools not in your list? Load dv-connect.

Volume guidance: CLI dataverse data create/query/count for one-off commands; MCP for up to ~25 records per call or simple filters; the SDK's CreateMultiple for larger bulk writes (chunk large sets starting ~1,000 — see dv-data) and dv-query for bulk reads; Web API for $apply aggregation.

SDK method cheat-sheet (anti-hallucination, not a preference signal): SDK method names are the least discoverable surface, so agents invent them. This maps common ops to the exact call. Each op is equally reachable via MCP/CLI per Hard Rule 2; see the noted skill for the full pattern.

OperationSDK callSkill
Create / update / delete recordsclient.records.create() / .update() / .delete() (pass a list for bulk)dv-data
Upsert on an alternate keyclient.records.upsert()dv-data
Query / filter recordsclient.records.list(...) (flat) or .list_pages(...) (streaming)dv-query
One record by GUIDclient.records.retrieve(table, guid) (None if missing)dv-query
Aggregation / server-side joinsclient.query.fetchxml(xml) (aggregates + link-entity)dv-query
Fluent query build (chainable)client.query.builder(Table).where(...).execute()dv-query
Limited SQL readclient.query.sql("SELECT ...")dv-query
Load into pandasclient.query.builder(table).select(...).execute().to_dataframe()dv-query
Upload to a file columnclient.files.upload(...)dv-data
Create tables / columns / lookups / N:Nclient.tables.create() / .add_columns() / .create_lookup_field() / .create_many_to_many_relationship()dv-metadata
Create an alternate key (enables upsert)client.tables.create_alternate_key(...)dv-metadata
Inspect existing schemaclient.tables.list_columns(table) / .list_table_relationships(table)dv-metadata
Create publisher / solutionclient.records.create("publisher" / "solution", {...})dv-solution
MCP Availability Check

If the user's request involves MCP — explicitly or implicitly — search your callable tools for any tool whose name or description contains dataverse (same search as Hard Rule 0).

If MCP NOT available and user explicitly asked for MCP ("use MCP to query"):

  1. Do NOT silently fall back to the Python SDK or Web API
  2. Tell the user: "Dataverse MCP tools aren't configured in this session yet."
  3. Load dv-connect to set up the MCP server
  4. After MCP is configured, stop — the session must restart for MCP tools to appear. Do not proceed with SDK.

If MCP NOT available and user asked a data question ("how many accounts?"):

  1. Use the CLI (if profile exists) or SDK to answer. Do not block the user.
  2. After answering, offer: "MCP would handle this conversationally — want me to set it up?"

The distinction matters: explicit MCP request → block and set up MCP; implicit question → answer with SDK, offer MCP setup.

If MCP tools ARE available, prefer MCP for simple reads/queries/small CRUD. Use the SDK only when a script is needed.

The Change Lifecycle — Operate Safely

For any real change, walk these three steps in order: confirm where, confirm the container, then persist the result.

Step 1 — Confirm the Environment (MANDATORY)

Dataverse work often spans multiple environments (dev, test, staging, prod) and multiple sets of credentials. Never assume the active PAC auth profile, values in .env, or anything from memory or a previous session reflects the correct target for the current task.

Before the FIRST operation that touches a specific environment — creating a table, deploying a plugin, pushing a solution, inserting data — you MUST:

  1. Show the user the environment URL you intend to use
  2. Ask them to confirm it is correct
  3. Run pac org who to verify the active connection matches

"I'm about to make changes to <URL>. Is this the correct target environment?"

Do not proceed until the user explicitly confirms. This is the single most important safety check in the plugin. Skipping it risks making irreversible changes to the wrong environment. Once confirmed for a session, you do not need to re-confirm for every subsequent operation in the same session against the same environment.

Step 2 — Confirm the Solution (before any metadata change)

Before creating tables, columns, or other metadata, ensure a solution exists to contain the work:

  1. Ask the user: "What solution should these components go into?"
  2. If a solution name is in .env (SOLUTION_NAME), confirm it with the user
  3. If no solution exists yet, load the dv-solution skill and follow its publisher discovery + solution creation flow. Use the SDK — never raw Web API — to create publisher and solution records:
python
# Quick reference — full pattern with publisher discovery is in dv-solution
publisher_id = client.records.create("publisher", {
    "uniquename": "<name>", "friendlyname": "<display>",
    "customizationprefix": "<prefix>", "description": "<desc>",
})
solution_id = client.records.create("solution", {
    "uniquename": "<Name>", "friendlyname": "<Display>",
    "version": "1.0.0.0",
    "publisherid@odata.bind": f"/publishers({publisher_id})",
})
  1. Pass solution="<UniqueName>" on all SDK calls, or include "MSCRM.SolutionName": "<UniqueName>" header on raw Web API metadata calls.

Creating metadata without a solution means it exists only in the default solution and cannot be cleanly exported or deployed. Always solution-first.

Step 3 — Pull to Repo (MANDATORY)

Any time you make a metadata change (via MCP, Web API, or the maker portal), you must end the session by pulling:

bash
pac solution export --name <SOLUTION_NAME> --path ./solutions/<SOLUTION_NAME>.zip --managed false
pac solution unpack --zipfile ./solutions/<SOLUTION_NAME>.zip --folder ./solutions/<SOLUTION_NAME>
rm ./solutions/<SOLUTION_NAME>.zip
git add ./solutions/<SOLUTION_NAME>
git commit -m "feat: <description>"
git push

The repo is always the source of truth.

Scripts

The plugin ships scripts/auth.py (Azure Identity token/credential acquisition — used by all other scripts and the SDK). Any Web API call beyond a one-off query should be a Python script committed to /scripts/, using scripts/auth.py for tokens. For writes see dv-data; queries and analytics see dv-query; post-import validation see dv-solution.

Windows Scripting

Platform-specific shell rules (ASCII in .py, no multiline python -c, PAC PowerShell wrapper, unbuffered background output) live in references/windows-scripting.md. Read it when running on Windows.

© microsoft, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in .github/plugins/dataverse/skills/dv-overview of microsoft/Dataverse-skills.

  • SKILL.md
  • references/erp-target.md
  • references/windows-scripting.md

Open the folder on GitHubat commit 3be592f

Compare with similar skills

Dv Overview next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dv Overview compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dv Overview this skillmicrosoft/Dataverse-skills241—~5.3kAutomated safety check: NotesMIT
Microsoft DocsMicrosoftDocs/mcp1.9k1 repos~723Automated safety check: PassCC-BY-4.0
Microsoft Docsmicrosoft/ai-agents-for-beginners77k3 repos~1.2kAutomated safety check: PassMIT
Microsoft Docsmicrosoft/ai-agents-for-beginners77k—~1.5kAutomated safety check: PassMIT
Microsoft Docsmicrosoft/ai-agents-for-beginners77k—~1.5kAutomated safety check: PassMIT
Microsoft Docsmicrosoft/ai-agents-for-beginners77k—~1.6kAutomated safety check: PassMIT

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    שאילתה בתיעוד הרשמי של Microsoft למציאת מושגים, מדריכים ודוגמאות קוד ב-Azure, .NET, Agent Framework, Aspire, VS Code, GitHub ועוד.

    77k GitHub stars~1.3k tokensUpdated 18 days ago
    AI & LLM EngineeringAuto-check passed

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Works with

Categories

Questions about Dv Overview

What does Dv Overview do?

Foundational cross-cutting context for Dataverse / Power Platform work — scope and the skill map, the tool-capability reference, the safety rules, and the safe change lifecycle. Dv Overview is an agent skill from microsoft/Dataverse-skills, published by the product's own GitHub organization. Foundational cross-cutting context for Dataverse / Power Platform work — scope and the skill map, the tool-capability reference, the safety rules, and the safe change lifecycle.

When should I use Dv Overview?

Dv Overview fits situations like: the user mentions Dataverse; load this first for orientation.

How do I install Dv Overview in Claude Code?

Run `npx skills add microsoft/Dataverse-skills --skill dv-overview -a claude-code`. Or copy the skill folder (.github/plugins/dataverse/skills/dv-overview in microsoft/Dataverse-skills) into .claude/skills/dv-overview in your project. Claude Code loads it when a task matches its description.

How do I install Dv Overview in Codex?

Run `npx skills add microsoft/Dataverse-skills --skill dv-overview -a codex`. Or copy the skill folder (.github/plugins/dataverse/skills/dv-overview in microsoft/Dataverse-skills) into .agents/skills/dv-overview in your project. Codex loads it when a task matches its description.

Can I use Dv Overview in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add microsoft/Dataverse-skills --skill dv-overview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dv-overview, .gemini/skills/dv-overview, .github/skills/dv-overview and .opencode/skills/dv-overview in your project.

What does Dv Overview need to run?

Going by SKILL.md and its folder, Dv Overview needs the command-line tools its instructions call (git, python and pip). Our summary lists: Python 3; Node.js.

Does Dv Overview access the network?

SKILL.md contains no URLs. Its commands use git and pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Dv Overview safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Dv Overview use?

Dv Overview is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dv Overview use?

About 5.3k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.7k tokens, read only when the agent opens those files.

What are the alternatives to Dv Overview?

Skills that share tags, products or a category with Dv Overview: Microsoft Docs (MicrosoftDocs/mcp, 1.9k stars), Microsoft Docs (microsoft/ai-agents-for-beginners, 77k stars), Microsoft Docs (microsoft/ai-agents-for-beginners, 77k stars) and Microsoft Docs (microsoft/ai-agents-for-beginners, 77k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dv Overview?

microsoft (a GitHub organization, an official publisher) maintains it in microsoft/Dataverse-skills, which has 241 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 5, 2026.

Source: microsoft/Dataverse-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.