The skill intelligence platform for AI agents. Rate, track, and systematically improve every skill — with a security audit on each one before hidden risks take effect.
Most teams deploy AI agents and then hope for the best. Quality is anecdotal. Someone complains, you investigate, and discover the agent has been producing mediocre work for weeks. Improving it means guessing at the root cause, editing a SKILL.md file, and hoping it sticks. As you scale from 5 agents to 50, this doesn't stay manageable. It compounds.
You don't know which agents are underperforming, or by how much. There's no score, no trend, no benchmark — just vibes.
Read feedback, guess what's wrong, edit SKILL.md, deploy, wait. A painfully slow loop with no way to measure if your change actually helped.
Did that SKILL.md edit actually work? You'll never know. There's no before-and-after, no effectiveness score, no proof of progress.
Below-par results are the visible problem. There are two sources of failure you can't see at all.
A skill can be perfectly written but never trigger if its description doesn't match how users phrase their requests. Your carefully crafted SKILL.md sits idle — and you'd never know.
Malicious instructions hidden in files referenced by a SKILL.md — invisible to marketplace UIs, invisible to you. This attack vector has been demonstrated. Are your skills clean?
SkillSpec gives you a structured, data-driven improvement loop — from feedback to analysis, to fix, to proof it worked.
Collect structured feedback on agent skill performance. Track proficiency over time with scores that reflect real usage, not one-off reviews.
LLM-powered pattern analysis identifies what's consistently going wrong, what's working, and why — across every rating you've collected.
Generate targeted improvement directives with exact SKILL.md changes shown as diffs. The LLM proposes. You review and approve.
After changes are applied, SkillSpec tracks whether ratings actually improved. Effectiveness scores tell you what worked and what didn't.
A skill dashboard with proficiency tracking, trend analysis, and gap analysis across every agent and skill in your workspace. Stop guessing. Start knowing.
When an agent underperforms, SkillSpec shows you exactly how to fix it — and whether the fix worked. LLM analysis turns vague feedback into specific, actionable SKILL.md changes.
Agents shouldn't wait for humans to improve them. SkillSpec lets agents access their own skill context, review feedback, and apply approved directives — closing the improvement loop automatically.
Agent quality isn't a solo sport. SkillSpec gives your whole team a shared skill library with visibility controls, role-based access, and collaborative feedback — so everyone benefits from every improvement.
Join the waitlist to get early access and shape the platform as we build it.