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Found 5,613 Skills
Query and filter Datadog logs from the shell using the Composio CLI. Run scoped log searches, pivot across services/environments, and export structured JSON for downstream agents instead of click-driving the Datadog UI.
Implement Cisco's Foundry specification for agentic AI security evaluation systems with multi-agent architecture
Expert in using next-devtools-mcp for Next.js development with AI coding agents
Accessibility (a11y) for CometChat UI Kit integrations across all families — React, React Native, Angular, Android (V5/V6), iOS, Flutter. Covers WCAG 2.1 AA targets, keyboard navigation in chat, screen reader announcements (live regions for new messages), color contrast, focus management on call screens, motion-reduction support, and the cross-family checks that catch the common production a11y bugs. Cross-family — applies wherever the agent is checking accessibility.
Queries Huawei Cloud identity and access management resources (IAM) via read-only Python SDK. Covers users, groups, policies, agencies, AK/SK, MFA devices, login/password/ACL policies, security compliance, and account quotas. No write operations. Use this skill when the user needs to query IAM identity info, check policies/permissions, view agency details, or inspect AK/SK/MFA status. Triggers: IAM, 用户, 用户组, 策略, 委托, 权限, AK/SK, MFA, 密码策略, 安全合规, 身份查询, 身份认证, identity, policy, agency.
Context layer for AI data agents - teach Claude Code, Codex, and AI agents to query data warehouses accurately with semantic layer, wiki knowledge, and MCP tools
Set up the Deepgram MCP server for your AI coding tool. Checks whether the Deepgram CLI (dg/deepctl) is installed: if so, uses the local CLI MCP server (dg mcp) for full tool access; otherwise offers the hosted documentation MCP or suggests installing the CLI. Use whenever someone wants to install Deepgram's agentic tools, set up the MCP server, or connect their editor to Deepgram.
Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-guard).
Review generated or changed test code against universal testing rules before it ships. Best used reactively after an agent writes, edits, generates, or refactors tests, before presenting, committing, or merging them. Use for pytest (test_*.py, *_test.py), PHPUnit/Pest (*Test.php), Jest/Vitest (*.test.ts, *.spec.js), Go (*_test.go), files under tests/, __tests__/, or spec/, and review requests like 'write tests for X', 'add tests', 'test this', 'review these tests', or PR diffs containing tests. Can also guide test writing when explicitly invoked before the work. This skill is the quality gate that prevents AI-generated test bloat.
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.
Comprehensive testing and development workflow specialist combining DDD testing, characterization tests, performance profiling, code review, and quality assurance. Use when writing tests, measuring coverage, creating characterization tests, performing TDD, running CI/CD quality checks, or reviewing pull requests. Do NOT use for debugging runtime errors (use expert-debug agent instead) or code refactoring (use moai-workflow-ddd instead).
Q-learning, DQN, PPO, A3C, policy gradient methods, multi-agent systems, and Gym environments. Use for training agents, game AI, robotics, or decision-making systems.