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autoharness is a self-learning skill layer for Claude Code. It learns skills from your real sessions, merges same-scenario ones instead of stacking near-duplicates, updates them in use, and prunes any that stop getting used — so the layer stays clean on its own, touching only the skills it wrote itself. Same model, different harness — 42% → 78% on CORE-Bench (HAL). The harness does much of the work (swyx's Big Model vs Big Harness), yet it's still rebuilt by hand every model generation.
Hi Product Hunt 👋
We built Auto-Harness around a simple question: what if Claude Code could learn from the work you are already doing, instead of requiring you to manually write and maintain every skill?
Auto-Harness runs alongside your normal Claude Code sessions and turns useful patterns from real work into reusable skills. When a similar scenario appears again, it updates or merges the existing skill rather than creating another near-duplicate. Skills that prove useful remain active, while unused ones are eventually archived, keeping the skill layer focused instead of letting it grow indefinitely.
A few principles shaped the project:
• Learn from real sessions — no separate training or replay workflow
• Consolidate instead of accumulate — related experiences improve one skill
• Validate through actual use — no benchmark or oracle required in the active loop
• Stay out of the way — no resident daemon and no additional recall system
• Respect user-owned skills — Auto-Harness only manages the skills it created itself
• Keep an evidence trail — every creation and update records why it happened
The result is a self-maintaining skill layer that gradually adapts to your projects, workflows, and corrections while remaining transparent: everything is stored locally as ordinary Claude Code skill files.
Auto-Harness is open source, MIT licensed, and available as a Claude Code plugin. We would especially appreciate feedback on the learning behavior, lifecycle design, and the kinds of workflows you would want it to capture.
Thanks for checking it out — we’d love to hear what you think.
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About Auto-Harness on Product Hunt
“Self-Learning Skills for Claude Code”
Auto-Harness was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #51 on the daily leaderboard. autoharness is a self-learning skill layer for Claude Code. It learns skills from your real sessions, merges same-scenario ones instead of stacking near-duplicates, updates them in use, and prunes any that stop getting used — so the layer stays clean on its own, touching only the skills it wrote itself. Same model, different harness — 42% → 78% on CORE-Bench (HAL). The harness does much of the work (swyx's Big Model vs Big Harness), yet it's still rebuilt by hand every model generation.
Auto-Harness was featured in Productivity (658.2k followers), Artificial Intelligence (475.9k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 292.4k products, making this a competitive space to launch in.
Who hunted Auto-Harness?
Auto-Harness was hunted by Tigerless Labs. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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