Preseason.ai vs Skill Federation

Preseason.ai and Skill Federation are both agent frameworks tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Preseason.ai

Orbit sits between your backlog and your coding agent, selecting one dependency-ordered task at a time, running the agent, then forcing the result through tests, lint, and type checks before marking the task done. Every run writes structured JSON artifacts — what the agent returned, how the output scored against a rubric, whether a human should accept or iterate — so you are reviewing evidence, not trusting a diff. The agent-neutral contract means you can run Claude, Codex, and Cursor against the same task and compare artifacts instead of impressions. The harness is intentionally minimal; it does not schedule, it does not host, and it does not manage secrets — which means the moment your workflow needs cross-repo coordination or cloud execution, you are writing the glue yourself.

Skill Federation

Skill Federation runs locally on your machine and connects to a catalog of over 100,000 vetted skills. When an agent hits a gap, it surfaces matches in milliseconds — each one license-checked, security-scanned, and provenance-tracked — then waits for your approval before installing into .claude/skills/. The benchmark evidence from the vendor is specific: a bare Claude Code agent solves 17.5% of SkillsBench tasks; with Skill Federation retrieving the top match, that climbs to 22.8%, roughly closing 27% of the gap to a hand-crafted ideal skill. The privacy boundary is narrow by design — only an abstract wish crosses the wire, never your code, plan, or outputs. The hard ceiling is integration breadth: Claude Code is supported, with Codex, Cursor, and Gemini listed as coming.

Attribute	Preseason.ai	Skill Federation
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Linux, macOS, Windows (CLI/Python-based)	Node.js, Python, cross-platform via curl
Pros	Validation gates enforce proof before task completion, so a coding agent cannot mark a fix done while tests are still failing — which eliminates the silent regression problem that plagues unguarded agent loops. Agent-neutral adapter contract means you can run Claude, Codex, and Cursor against identical tasks and compare structured evaluation artifacts, so you stop arguing about which agent is better and start looking at data. Four machine-readable artifacts per orbit (agent result, evaluation, recommendation, progress log) give audit teams a complete, inspectable record of what the agent returned and how validation scored it — without relying on anyone's memory of what happened. Dependency-ordered backlog selection keeps each agent run focused on one unblocked task, which means agents cannot start work that depends on incomplete prior steps — a failure mode that costs hours of untangling in unconstrained agent loops. Deterministic replay with no API key required means you can verify the harness behavior itself in isolation, so debugging a broken validation run does not require burning API credits or standing up a live agent.	Two-scanner security vetting at catalog ingestion rather than at install time, so you are never pulling live from an unreviewed repo and your team avoids the malware-by-star-count gamble. License class and provenance shown before every install, which means teams with compliance requirements can audit what skills entered the codebase without reconstructing that history after the fact. Retrieval triggered automatically when the agent hits a gap, so the agent does not require manual skill reminders at the start of every session — a friction point the vendor explicitly benchmarks against. All execution happens on your machine with only an abstract wish transmitted, so codebases, plans, and outputs stay local even when skill search is delegated to an external catalog. Open-source and self-hostable, which means teams that need an air-gapped or fully controlled registry can run their own instance rather than depending on a hosted endpoint.
Cons	Orbit has no scheduler, no cloud execution layer, and no cross-repo awareness — the moment your workflow requires tasks that span more than one repository or need to run on remote infrastructure, you are assembling that plumbing yourself on top of the harness. The adapter contract requires agents to speak JSON over CLI, so agents with browser-only or proprietary API interfaces need a wrapper built before they can run inside an orbit — that wrapper is not provided and is the team's responsibility to maintain. Orbit has no built-in backlog management UI or integration with issue trackers; the backlog is whatever structured input you feed it, which means teams used to Jira or Linear-driven workflows will spend setup time before the first orbit runs. Teams that need parallel agent execution — running multiple tasks simultaneously to cut wall-clock time on large backlogs — will hit the single-orbit-at-a-time model as a hard ceiling and switch to a purpose-built agent orchestration platform rather than extending Orbit.	Claude Code is the only documented supported integration; teams running Cursor, Codex, or Gemini as their primary agent tool cannot use Skill Federation in its current state — those integrations are listed as forthcoming with no committed timeline on the vendor page. The benchmark ceiling exposes the retrieval model's limit: even the top retrieved skill closes only 27% of the gap to a hand-crafted ideal skill, meaning tasks that require precise, purpose-built skills will still be partially solved at best — teams with narrow, specialized workflows will hit this ceiling faster than teams with general-purpose tasks. No API is available, so teams that want to integrate skill retrieval into a custom agent pipeline or CI workflow cannot call Skill Federation programmatically — teams needing that surface will need to build their own retrieval layer, at which point Skill Federation's catalog is no longer in the loop.

Bottom line

Preseason.ai and Skill Federation are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Preseason.ai and Skill Federation?

Preseason.ai is Free and open source, while Skill Federation is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Preseason.ai better than Skill Federation?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.