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Found 787 Skills
Run large codebase migrations and multi-file refactors. Uses the Composio CLI to coordinate issue tracking, batched PRs, and CI verification while the agent executes the transforms locally across hundreds of files.
AI-powered GA4 + GTM event tracking automation — analyzes sites, designs event schemas, syncs GTM containers, runs preview verification, and publishes tracking implementations.
ZOLOZ product integration and support specialist. Use this skill whenever the user mentions ZOLOZ, eKYC, RealID, Real ID, Connect, Face Capture, Face Compare, ID Recognition, ID Network, NFC Reader, CN Authority, AML screening, Deeper, RealDoc, GeoTrust Locator, or any identity verification / biometric / document recognition product from Ant Group. Also trigger when users mention ZOLOZ-related concepts like gateway signing, transaction IDs, checkresult, initialize API, metaInfo, ZLZFacade, ZLZRequest, or ZOLOZ Portal. This skill covers product questions, technical integration, API troubleshooting, SDK issues, Portal operations, and pricing — use it broadly for anything ZOLOZ-related.
Desktop & Tauri app testing for AI agents — Tauri v2 + WebKitGTK in Docker (AppImage extraction, Gemini Computer Use, virtual display, DOCX export verification) plus Electron app automation (VS Code, Slack, Discord, Figma) via `agent-browser skills get electron`. Use when testing a Tauri desktop app (Cicero), Electron app, or any non-browser desktop UI. For web browser testing, see `browser-test-agent`.
Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
Four-step image referring-expression pipeline: turns images plus KITTI bounding-box labels into region descriptions, scene captions, grounded referring expressions, and (optionally) verified expressions via VLM distillation. Use when the user wants to generate referring-expression annotations from images with KITTI labels, build region descriptions, produce grouped grounding phrases tied to bboxes, run a double-check verification pass on grounding expressions, auto-label traffic / scene images for referring datasets, or run the image_referring_expression pipeline. Triggers include 'referring expression', 'region description', 'KITTI labels', 'spatial relationship annotation', 'auto-label image referring expression', 'image_referring_expression'.
AI SDLC context-aware navigation workflow. Use when an AI assistant needs to determine what to do next, select the right installed skill, start or resume a feature, explain blockers, inspect available capabilities, or provide evidence-backed required and optional next actions from repository state. Supports `--quick-flow` for compact guidance and `--full-flow` for stricter context verification.
AI SDLC QA workflow. Use when an AI assistant is asked for QA planning, acceptance validation, regression scope, exploratory checks, smoke tests, release verification, or change-focused manual validation evidence. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
Guidance for implementing Adaptive Rejection Sampling (ARS) algorithms. This skill should be used when implementing rejection sampling methods, log-concave distribution samplers, or statistical sampling algorithms that require envelope construction and adaptive updates. It provides procedural approaches, performance considerations, and verification strategies specific to ARS implementations.
Batch check whether Skills comply with best practice specifications; automatically verify dimensions such as naming, preface section, structure, file cleanup, and dependencies; support single or batch checks; generate detailed inspection reports; suitable for quality verification after Skill development is completed
Multi-Model Collaboration — Invoke gemini-agent and codex-agent for auxiliary analysis **Trigger Scenarios** (Proactive Use): - In-depth code analysis: algorithm understanding, performance bottleneck identification, architecture sorting - Large-scale exploration: 5+ files, module dependency tracking, call chain tracing - Complex reasoning: solution evaluation, logic verification, concurrent security analysis - Multi-perspective decision-making: requiring analysis from different angles before comprehensive judgment **Non-Trigger Scenarios**: - Simple modifications (clear changes in 1-2 files) - File searching (use Explore or Glob/Grep) - Read/write operations on known paths **Core Principle**: You are the decision-maker and executor, while external models are consultants.
Unified setup hub: project init, tool setup, 2-agent config, harness-mem, codex CLI, and rule localization. Use when user mentions setup, initialization, new projects, workflow files, CI setup, LSP setup, MCP setup, codex setup, opencode setup, 2-Agent setup, PM coordination, Cursor setup, harness-mem, claude-mem integration, cross-session memory, localize rules, adapt rules. Do NOT load for: implementation work, reviews, build verification, or deployments.