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Found 77 Skills
Self-improving browser automation via the auto-research loop. Iteratively runs a browsing task, reads the trace, and improves the navigation skill (strategy.md) until it reliably passes. Supports parallel runs across multiple tasks using sub-agents. Use when you want to build or improve browser automation skills for specific website tasks.
Parallel adversarial review protocol that launches two independent blind judge sub-agents simultaneously to review the same target, synthesizes their findings, applies fixes, and re-judges until both pass or escalates after 2 iterations. Trigger: When user says "judgment day", "judgment-day", "review adversarial", "dual review", "doble review", "juzgar", "que lo juzguen".
Use ONLY when the user explicitly asks to *create* a new SeeFlow flow — "create a flow", "generate a flow", "scaffold a SeeFlow flow", "add a flow to this repo" — or when a previous `/seeflow-lookup` already reported no matching flow exists. **Do NOT invoke for inspection phrasing** ("show me", "how does X work", "diagram our system", "explain the flow") — those route to `/seeflow-lookup` first; it will auto-hand off here only when nothing is registered. Orchestrates five sub-agents and the `seeflow` CLI to turn a natural-language prompt into a registered, validated SeeFlow flow at `<project>/flow.json` (node-attached files live under `<projectPath>/nodes/<id>/`).
Review changes starting from a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (Does the code comply with the coding standards documented in this repository?) and Spec (Does the code meet the requirements from the source issue/PRD?). The two reviews run in parallel sub-agents and report side by side. This applies when users want to review a branch, PR, in-progress changes, or request a "review since X".
The complete AI web agency toolkit. One skill to run a full client website project — from intake to design to build to deploy. Orchestrates sub-skills and sub-agents for fast, high-quality delivery.
Spawn specialized sub-agents with context handoff for complex multi-phase tasks. Enables expertise delegation within a session with automatic context merging and depth limiting to prevent infinite loops.
Design a module's interface using parallel sub-agents producing radically different designs ("design it twice"). Compare on depth, simplicity, and efficiency. Embedded grill on the synthesized choice. Use when designing a new API, exploring interface options, or deciding the shape of a refactor before writing code.
Use when the user asks to create, generate, or scaffold a SeeFlow flow from a natural-language prompt — "create a flow", "show how X works", "diagram our checkout system", "add a flow to this repo". Orchestrates four sub-agents and bun scripts to write a registered, validated flow under <project>/.seeflow/<slug>/.
Use when tasks are complex and require full microservices collaboration: The main agent acts as a pure Orchestrator, strictly prohibited from writing code personally, and is responsible for accurately assigning responsibilities such as positioning, planning, coding, testing, and review to corresponding sub-agents (explorer, planner, worker, verifier, reviewer, fixer). This Skill enforces microservices workflow discipline, requiring full Chinese communication, minimal routing output, and minimized context transfer.
Use when the user asks for a broad codebase review, substantial PR/branch review, architecture audit, tech-debt scan, cleanup assessment, structural sanity check, or design-alignment review. Default workflow: use sub-agents when available unless specifically forbidden; do not require the user to mention sub-agents, council mode, delegation, or parallel review. Focus on cruft, duplication, weak boundaries, missed reuse, lifecycle/concurrency risks, test/roadmap drift, and code aesthetics. Do not use for narrow bug fixes, ordinary small-diff reviews, frontend visual QA, repo-onboarding docs, or OpenAI Agents SDK production-readiness review. Output evidence-backed findings first, then pressure points, design alignment, open questions, and follow-through.
Parallel DAG-plan implementation skill. Reads a v-planning plan directory (root.md + step-<n>.md files), topologically schedules ready steps, and fans them out as parallel sub-agents. Use whenever the user invokes /v-implement, points at a plan directory produced by /v-plan, or asks to "run the parallel plan", "implement the DAG", or "fan out the steps" — even without those exact words. For linear plans (single .md file), use `implementing` instead.
Execute individual plan phases as background sub-agents for context-efficient implementation.