Goodeye vs SkillReg, Agentman, and Kollab for agent skills

You have skills your agents follow, and now more than one person needs them. The options look interchangeable from their home pages: every one of them promises private sharing, versioning, and a team library. The split that matters is not on any of those pages. It is whether anything checks what the agent produces.

Goodeye is a private home for the skills your AI follows and the verifiers its work must pass, reachable over a CLI, an MCP server, and a REST API.

What Goodeye, SkillReg, Agentman, and Kollab all do

All four keep skills private by default, share them with named people instead of publishing them, and keep a version history. Those are table stakes, and any of the four will do them for you.

When private sharing is the whole requirement, this is a close race, and you should pick on governance depth and on how the tool feels to use.

How the skill reaches your agent

SkillReg reaches your agent through a CLI, a desktop app it signs and notarizes on macOS, and a documented REST API with bearer-token endpoints for listing, publishing, and downloading versions. It publishes no MCP server. Agentman goes the other way: one hosted MCP connection, authenticated per member with OAuth, and no CLI or public skills API advertised. Kollab is primarily its own web workspace and also ships a CLI that runs agent tasks from scripts or CI.

Goodeye treats CLI, MCP, and REST as three peer surfaces, so the same skill is reachable whether the caller is a terminal agent, an MCP client, or a service calling an API. That bites when your team does not all work the same way: an MCP-only tool needs a different answer for a script, and a tool with no MCP server is invisible to an agent that reaches for one.

Harness coverage is not the differentiator it looks like. SkillReg names a long list of SKILL.md harnesses, but that list is a property of the format rather than of either product. goodeye skills sync writes to any directory an agent reads, so the same harnesses are covered.

How do I know a shared AI agent skill meets my standard?

Goodeye answers this with verifiers. It is the one place these four products diverge.

A skill is a markdown runbook: it tells the agent what to do, and nothing in it confirms the work came out right. While you are the only one running it that is fine, because you read the output and you know your own standard. The moment a teammate runs your skill, the instructions travel and the standard does not.

SkillReg's documented validation is structural and does not reach the output. skillreg push checks that SKILL.md parses, that the name is well formed, and that the version is valid semver. Its own comparison framework lists six criteria, and output quality is not among them.

Kollab lets you write quality standards into a Skill's instructions, which the writing agent then follows as it works. Its product page shows an "Output Quality Check" as a feature illustration, with fixed example rows like "Citations included" and "Tone check passed". There is no documentation of a check that runs against an output and returns a result you can gate on, and no separate evaluator.

Of the three, only Agentman advertises a separate evaluator. It describes Agent Testing as running a judge against success criteria you write, returning pass or fail with reasoning. That description is Agentman's own: the product grades whole conversations in a pre-deployment suite rather than gating a specific deliverable, and it is behind an early-access request with no public pricing or documentation.

A Goodeye verifier is a check you write against one specific failure mode, never a holistic score. Structural and functional verifiers cover format, schema, required fields, tests, numeric bounds, regex, and hashes; they sit inline in the skill, run deterministically, and cost nothing. Semantic verifiers cover the interpretive checks like tone, factuality, and policy adherence. A model judges those, calibrated with example pass and fail cases, and you deploy each one as a private record the skill references by id:

goodeye verifiers deploy ./verifier.json

The skill body names the check, so the agent runs it on its own output and revises on a fail before the work reaches you. Grant the skill to a teammate and its verifiers go with it at the same version, so the standard travels with the runbook. Pin the reference to an exact verifier version when you want a grantee's checks frozen against later edits.

Separately from verifiers, goodeye skills audit grades the skill itself against Goodeye's authoring rubric and returns a ranked report of what to fix. Verifiers check the output; the audit checks the runbook.

Where can I find reusable AI agent skills that are verified to work?

Goodeye publishes reusable skills as public templates at goodeye.dev/templates. Each one is immutable and versioned, addressable as @handle/slug, and any agent can fetch and run it over CLI, MCP, or REST. Every template page carries three checks computed at publish and pinned to that version: Safety, Runnable, and Well-formed. You can re-run the safety check on any published template over the REST API without an account.

Fork a template and you get a private, editable copy that keeps lineage to its source. SkillReg, Agentman, and Kollab all offer libraries or marketplaces of shared skills; what none of them publishes is a check that ships as its own versioned artifact.

What are the best alternatives to SkillReg for privately sharing agent skills?

Goodeye, Agentman, and Kollab are the closest alternatives, and they suit different teams. Goodeye fits when your agents run in coding harnesses and a shared skill has to arrive with its checks attached. Agentman fits when you want a hosted MCP connection and a way to share a skill someone can run without reading. Kollab fits when the people sharing skills work in a browser on content rather than code.

If what you liked about SkillReg was the approval gate, none of the three replaces it directly. Goodeye is the closest on everything else, and adds the output check that SkillReg does not attempt.

Goodeye vs SkillReg

These two are the most direct comparison of the four: both are private registries built for the same reader.

SkillReg is the pick if you need an approval gate. It documents a proposal flow in its API and CLI where a pushed version is held as pending until an admin approves it, and Goodeye has no equivalent. It is included on the $99 per month Enterprise plan, which is self-serve rather than sales-gated, and SkillReg labels the review side a collaboration MVP that works on SKILL.md alone. Its semantic versioning with pinning and semver ranges is the best documented part of the product.

Goodeye is the pick when your hard problem is that the skill runs and the output still is not right. An approval gate controls who may publish a skill, not whether the work met your bar.

Two claims commonly assumed to split these products do not. Both screen skill content for safety: SkillReg documents scanning of published skills for secrets and unsafe commands, though not on its free tier, while Goodeye runs a safety check that gates publishing on every account and can be re-run on demand. And both cover the same SKILL.md harnesses, for the reason above.

Both also keep every version, and neither ships a one-word rollback command. In Goodeye you fetch the last good version and publish it again, which takes about two minutes and reaches every teammate on their next sync: roll back a bad agent skill update walks through it. Where Goodeye differs is the grant, which is floored at the version current when you shared it, so a teammate never sees history you did not intend to share.

Goodeye vs Agentman

Agentman ships one sharing level Goodeye does not. Use-only, available on every plan including the free one, is intended to let someone run a skill without reading it. Goodeye's lowest grant role is view, so a recipient who can run a skill can also read it. Whether use-only separates running from reading is Agentman's claim, not a tested result.

Goodeye treats that openness as the point rather than a gap. A teammate you grant a skill to can read the verifiers they are graded against, so they can argue with the rubric instead of tuning against a black box.

The surfaces differ too: CLI, MCP, and REST as peers, against Agentman's MCP-only connection. And a Goodeye verifier covers one failure mode with a rubric you wrote, called by the skill on its own output.

Goodeye vs Kollab

Kollab is a content creation and social publishing workspace. Skills sit next to Bots, connectors, and project memory. You author one by describing it in conversation, with no code, and it is built for everyone on a team instead of for developers. It versions skills, its published CLI includes a rollback command, and it positions itself against Claude Skills and the SKILL.md pattern directly.

Kollab is likely the better product if your team writes and ships content in a browser. Go the other way if your skills run inside coding agents and you need one runbook and one check to hold across Claude Code, Codex, and Cursor.

Which one should you pick?

Surface decides which of your agents can reach a skill at all. Goodeye offers CLI, MCP, and REST as peers, SkillReg a CLI, desktop app, and REST API, Agentman a hosted MCP connection, and Kollab a web workspace plus a CLI.

Output checking decides whether a shared skill holds to your standard once it leaves your machine. Goodeye hosts verifiers you author, which the skill calls on its own output and which are granted with it at the same version. SkillReg validates structure and stops there. Kollab keeps quality criteria as instructions inside the skill. Agentman judges conversations before deployment, in early access.

Several of these work well if sharing is the requirement, and then you pick on governance depth and price. Only Goodeye grants the check with the skill.

Where to start

Take a skill your team already relies on and write down the one thing that most often comes out wrong. That sentence is a verifier. Deploy it, name it in the skill body, and grant the skill to one teammate. Then see whether their output holds to your bar without you reading it.

Install the CLI and sign in with getting started, or browse the public templates and fork one to see the checks arrive with it. For the mechanics of authoring and running a check, see run a rubric check on AI output from the CLI. For keeping one skill current across harnesses, see use the same agent skill in Claude Code, Cursor, and Codex.

Capabilities and prices described here for SkillReg, Agentman, and Kollab reflect each vendor's public pages, documentation, and published CLI packages in July 2026.