Know what your teams have built.
Bring capabilities from connected repositories and supported AI platforms into one inventory. See the owner, source, version history, and review status in one place.
SEE THE AI WORK YOUR TEAMS ARE CREATING
Do you really know what your teams are building with AI?
See the AI skills, instructions, and workflows your teams are creating, who owns them, and which ones are approved to use.
Find what already exists. Stop teams rebuilding the same thing. Review the good work and make trusted versions easy to reuse.
01 Find it
02 Trust it
03 Reuse it
COMMONSET / ORGANIZATION OVERVIEW
Your AI work, in view.
- Capabilities inventoried
- 184
- Retrieved in 30 days
- 62
- With approved versions
- 31
What teams are reusing
MCP retrievals · 30 days- Contract ReviewLegal Ops · Approved v2.1.0128 retrievals
- Revenue ForecastFinance · Approved v1.3.096 retrievals
- Customer ResearchProduct · Approved v1.0.074 retrievals
Where to focus next
Needs attention14
Open reviewsDecide what is ready to share.
7
Drifted implementationsCheck changes against approved versions.
THE WORK EXISTS. THE SHARED VIEW DOESN’T.
Useful AI work should travel beyond the team that built it.
Teams build skills, instructions, and workflows in different places. Others repeat the work because they cannot find it, do not know who owns it, or cannot tell which version to trust.
“The skills are a mess.”
“People are sharing skills through repos, docs, and drives — and nobody knows where to go or what they should be doing.”
“I can’t spend my life modifying the skill repo.”
“Even just knowing what people have created, who owns it, and where versions differ seems useful.”
Find it. Review it. Make it reusable.
Give teams a shared place to find the work your organization has approved. Learn about the AI agent skills registry → · See AI skill governance →
Anonymous excerpts from early product interviews, lightly edited for clarity. Not customer endorsements.
FROM INDIVIDUAL WORK TO SHARED STANDARDS
See what exists.
Give useful work a wider reach.
Start with visibility. Review the capabilities your organization wants to rely on, make approved versions available, and follow their adoption.
Agree on what others should use.
Give each capability an owner. Review its content and security findings, approve a specific version, and define who can use it through Commonset.
Help the next team start ahead.
Make approved work discoverable and available through Commonset MCP and supported integrations. Teams can build on a shared capability instead of starting from scratch.
See adoption. Decide where to focus.
Follow recorded views, downloads, publishing, and MCP retrievals. Use those signals alongside team feedback to decide what to improve, share more widely, or retire.
GOVERNANCE WITHOUT GRIDLOCK
Experiment locally. Standardize deliberately.
A short path from local experimentation to broader, governed use. Follow the practical six-step governance workflow →
1
Create or import
Bring a capability into Commonset from the browser, an integration, or MCP.
2
Check evidence
Validate structure and surface security, integrity, and risk signals.
3
Review + approve
Apply ownership, policy, separation of duties, and explicit approval.
4
Distribute + understand
Make approved versions available through supported runtimes and track lifecycle activity.
THE SYSTEM OF RECORD FOR YOUR AI CAPABILITIES
Keep the version your organization trusts clear.
Commonset connects ownership, version history, review, access, and distribution in one place. For platform and engineering teams, it acts as the control plane behind the shared view: approved versions stay immutable, and connected implementations can be compared with the approved baseline when they change.
Publish an approved baseline
Distribute an approved Commonset version through a supported integration and record the exact platform version it created.
Observe platform state
Sync the connected platform without changing the approved Commonset capability.
Detect + alert on drift
If the observed platform version no longer matches the managed baseline, Commonset marks it Drifted and alerts organization administrators once.
Compare exact versions
Review the approved Commonset artifact against the exact external source Commonset observed. The comparison is read-only.
Reconcile deliberately
Import the external change as a new Draft for review, or republish the approved Commonset version to keep the approved baseline authoritative.
OPENAI API SKILLSContract Review
Drifted
Commonset approvedv3
Managed platform version7
Observed platform version8
VerificationDrifted
SKILL.mdModified
@@ external change @@
+ <!-- Changed outside Commonset -->Preserve platform version 8 as a new Draft. Approved v3 and its managed baseline stay unchanged until normal review and publication.
Publish the approved Commonset artifact through the existing platform controls, then verify the new platform version on the next sync.
Illustrative product flow. Detection, comparison, reconciliation, and resulting platform changes are explicit and auditable.
CURRENT INTEGRATION DEPTH
Keep your standards as your AI stack changes.
Use supported integrations with Claude, OpenAI, Google, and GitHub. Each platform has its own publishing and access controls; see exactly what Commonset supports below.
Compare integration support
| Platform / integration | Discover + import | Publish | Verify + reconcile | Access model |
|---|---|---|---|---|
| Claude API SkillsConnected workspace Skills API | SupportedDiscover skills and metadata. Import source when the connected API makes it retrievable. | SupportedPublish approved Commonset versions to Claude skills. | SupportedTrack exact version identities, detect drift, and compare source when content is retrievable. | Workspace-scopedNo Commonset-style per-skill user or group access through this API. |
| OpenAI API SkillsConnected OpenAI API project | SupportedDiscover skills and download version-specific skill bundles into Commonset. | SupportedCreate skills or immutable new versions from approved Commonset versions. | SupportedDetect exact-version drift, compare the observed source, and reconcile deliberately. | Project-scopedThe API does not expose Commonset-style per-skill user or group audiences. |
| Google Agent RegistryStandalone Skills in a configured project and location | SupportedDiscover Skills and import the current default SkillRevision archive. | SupportedCreate standalone Skills or immutable SkillRevisions; long-running publishes resume automatically. | SupportedCompare the current default revision with the managed revision and exact retrieved source. | Platform policyGoogle policy bindings govern platform-side availability. |
| GitHub / Copilot sourcesConfigured repository sources | SupportedDiscover supported packages and import source as Commonset drafts with repository provenance. | Supported via PROpen a pull request for approved capabilities linked to a native SKILL.md package. | SupportedTrack remote content identity and source commit against the managed baseline and surface drift. | Repository / org policyGitHub visibility and Copilot organization or enterprise settings govern availability. |
Supported
Limited / platform-specific
Current as of August 2026. OpenAI API Skills are supported above; ChatGPT workspace assets are not currently exposed through a supported Commonset integration surface. Platform APIs change, and Commonset keeps integration support explicit as those surfaces evolve. MCP is a Commonset delivery and access path rather than an external inventory API.
MAKE TRUST VISIBLE
Make trust visible before work is shared.
See where a capability came from, what it can do, and which version passed review. Give teams a clear basis for choosing what to use.
- 01Provenance
Trace the source, parent version, actor, and immutable content digest.
- 02Integrity
Verify stored artifact bytes still match the approved registry record.
- 03Agent-aware analysis
Surface network, execution, credential, persistence, and supply-chain capabilities for review.
- 04Policy gates
Prevent blocked or stale-analysis versions from being approved, downloaded, or published.
Evidence reportCurrent policy
REVIEW PRIORITYLow
Evidence based✓
Artifact integrityStored bytes verified
Passed✓
Provenance bindingDigest matches recorded origin
Passed!
Outbound networkExternal service access declared
Review✓
Secret scanNo embedded credentials found
PassedCAPABILITY FOOTPRINT
NetworkFilesAPI tools
ENCRYPTED BY DESIGN
Protect the capability itself, not just the storage around it.
Commonset encrypts capability files and selected sensitive metadata at the application layer using organization-specific keys. Integration credentials use a separate credential-encryption path.
Read our security approachVersioned per-organization data keys protect capability files before bytes reach storage.
Selected fields use authenticated encryption bound to organization, record, and field context.
External service secrets are decrypted only for authorized integration operations.
ORGANIZATIONAL OWNERSHIP
The capability belongs to your organization.
Not the platform implementing it.
Models and vendors may change. The capability identity, approval history, access, provenance, and version record should endure.
Platform-specific managementCommonset
Governs one AI environmentGoverns the organizational capability
Platform-specific ownershipOrganization-owned source of truth
Platform-specific versionsCanonical version history + provenance
Platform-specific sharingCross-platform access and policy model
Platform status as a proxyVisible evidence + approval
FREQUENTLY ASKED QUESTIONS
Common questions about AI skill governance and Commonset.
What Commonset is, who it is for, and how it fits into the tools teams already use.
What is AI skill governance?
AI skill governance is the process of making reusable AI skills and capabilities visible, owned, reviewable, versioned, and approved before they are shared more broadly. It gives an organization a way to know what exists, which version is trusted, who is responsible for it, and where it can be used. Learn more about AI skill governance
Why do companies need governance for AI skills and agents?
As teams build reusable skills, instructions, workflows, and agents, that work spreads across repositories, AI platforms, and individual accounts. Without a shared governance process, people can duplicate work, use stale versions, or lose track of who owns and approved something. Governance creates a clear path from local experimentation to broader, trusted reuse.
How is Commonset different from managing AI skills in GitHub?
GitHub is excellent for source control and collaboration. Commonset can use GitHub as a source and distribution path, then adds capability-specific ownership, review and approval, access, version trust, distribution to supported AI runtimes, and drift reconciliation against an approved baseline. Compare Commonset and GitHub
Does Commonset work with Claude, ChatGPT, and other AI platforms?
Commonset is provider-neutral. Today it supports integrations with Claude API Skills, OpenAI API Skills, Google Agent Registry, and GitHub / Copilot sources, with Commonset MCP as a delivery and access path. OpenAI API Skills are supported; ChatGPT workspace assets are not currently exposed through a supported Commonset integration surface. Integration support stays explicit as provider APIs evolve.
Is Commonset for individuals or teams?
An individual can use Commonset to organize and version reusable AI capabilities, but the product is designed primarily for the problems that emerge when AI work becomes shared across a team or organization: knowing what exists, who owns it, which versions are approved, and how trusted work is reused across people and tools.
How does Commonset help with AI skill ownership, approval, and versioning?
Commonset gives capabilities explicit ownership and immutable version history, then puts review and approval around specific versions. That keeps the approved organizational baseline clear while new drafts or connected implementations change, and lets teams see provenance, trust status, and access before reusing a capability.
Is Commonset an AI security scanner?
No. Commonset is an AI capability management platform, not a standalone security scanner. It surfaces security, integrity, provenance, and capability-risk signals as evidence inside review and policy workflows. Those findings support governance and human review; they do not prove that a skill is safe. Read our security approach
COMMONSET
Turn useful AI work into a shared standard.
Start with what your teams have built. See it clearly, choose what to trust, and make it easier for the next team to reuse.