Plugins

Adds /pua to steer the agent to try another approach and verify results before claiming completion, with 15 company styles, persona modes, global and per-conversation settings, and an optional loop that continues until a verification command passes, the iteration limit is reached, or the user cancels.

Git worktree Session Targets with isolated task sessions, Ready for Review, reversible Local Preview, human-confirmed delivery, environment retention and recovery, and same-session iteration.

An AI-native browser workspace for DeepSeek Harness (DSH), combining multi-tab browsing, live page annotations, lightweight code editing, workspace previews, and Agent-assisted iteration across Web and Desktop.

Turns your AI agent application into an interactive workspace for ideation and design, with editable panels, structured knowledge and guided iteration.

A writable, git-versioned self for DeepSeek Harness: persona files injected every turn, a persona-only memory layer, topic-keyed journal→dream promotion gated by your 👍/😑/👎 ratings, whitelisted self-upgrades, and a Settings persona tab.

Skill for building a game design brief into a playable, publishable TapTap Maker project: it picks the browser workspace or local MCP route, extracts the five elements of the brief, writes a first-round prompt for a minimal core loop, iterates in the official order, checks preview and release materials, and keeps credit, credential and irreversible-action limits in view. Includes eight reference notes on the product, credits, themes and asset limits, the leaderboard, brief parsing, iteration, publishing and safety, four templates, and three read-only Python checks for brief completeness, project layout and release materials.

“What should we build next?” There’s a skill for that.

AI-native iteration cycle: 24 anic_* tools and 77 HTTP routes that run evaluation, convergence gates, and auto-fix loops across projects, with a live white-box panel (interactive workflow graph, invocation feed, autonomy gauge, gate ledger, HITL queue) and an inline installable SKILL.md workflow kit.

A dsh plugin: after a timeout, the time distribution from the previous iteration is fed to the model, enabling the model to autonomously identify and adjust its behavior; if the introspection cost becomes too high, the plugin enters a dormant state, allowing the model to be restarted at any time.