Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Unity 6 performance profiling and optimization guide. Use when profiling, optimizing frame rate, reducing memory usage, debugging performance bottlenecks, or using the Profiler, Memory Profiler, or Frame Debugger. Based on Unity 6.3 LTS documentation.
TMDL and PBIR validation, linting, and pre-deployment testing. PROACTIVELY activate for: (1) validating TMDL syntax before deploy, (2) validating PBIR schema, (3) catching TmdlFormatException / TmdlSerializationException early, (4) Best Practice Analyzer (BPA) rules and BPA CLI, (5) Tabular Editor BPA scripting, (6) PBI-Inspector / PBI-InspectorV2 / Fab Inspector, (7) PBIR JSON schema validation, (8) pre-deployment validation in CI, (9) fabric-cicd parameter.yml validation, (10) catching breaking changes between TMDL versions. Provides: BPA rule library, validation CLI commands, CI integration for validation, error catalog (TmdlFormatException, etc.), and a pre-deploy validation playbook.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Verify work against explicit criteria using direct, source-faithful evidence, reproducible checks, and clear verdicts. Use before declaring an artifact, implementation, or claim complete; do not use for exploratory research without pass/fail criteria.
Help with migrating classic ABAP custom code toward higher clean core levels (Level A/B/C/D) including custom code adaptation, identifying released and classic API replacements, generating wrapper classes for non-released objects, ATC clean core and Cloud Readiness checks, changelog for SAP objects, handling incompatible language constructs, and step-by-step remediation workflows. Use when users ask about migrating to ABAP Cloud, custom code migration, cloud readiness, clean core level remediation, eliminating Level D, reducing Level C, non-released API replacement, wrapper pattern, ATC clean core checks, code adaptation, classic to cloud migration, or clean core compliance. Triggers include "migrate to ABAP Cloud", "cloud readiness check", "clean core level", "eliminate Level D", "reduce Level C", "non-released API", "replace with released API", "custom code adaptation", "wrapper for non-released", "ATC clean core", "clean core migration", "changelog for SAP objects", or "ABAP Cloud compatibility".
Use when the user asks to "generate (test) scenarios", "generate evaluators", "create an evaluator", "create evals", "create a scenario", "write a test scenario", "design a test case", "test my agent", "build eval coverage", "plan a test suite", "create red team tests", "set up test profiles", "configure conditional actions", "build a deterministic test", "design an IVR test", "write a unit test for a voice agent", "build a regression test", "scripted scenario", "structured evaluator", or "run evals". Also for CHANGING existing evaluators — "update an evaluator", "improve my evals", "make these evaluators stricter", "add a DTMF step", "fix the expected outcome", "attach metrics to these" — and for debugging how the testing agent speaks: "why did it read the number as a word", "make it spell digits", "wrong language". Covers evaluator design and review, coverage, test profiles, mock-tool data, conditional actions, and red-team / edge-case practice.
Code review and validation for CloudBase projects. After writing code for Web / miniprogram / CloudRun / cloud-function projects, call this skill to check for known pitfalls — auth guard misuse, missing database tables, RLS misconfiguration, storage domain setup, and SDK API misuse. Supports automated lint scripts (regex-based) + LLM semantic review.
Tailors Codacy configuration to a project by discovering its stack, enabling the right tools and patterns, running analysis, and intelligently reducing noise — disabling irrelevant or noisy patterns, tuning thresholds, and excluding files that shouldn't be analyzed. Produces a machine-readable summary of all changes. Use whenever the user wants to configure Codacy, reduce noise, fix false positives, enable or disable tools or patterns, tune code quality rules, deal with too many warnings, or align Codacy with their project's conventions. Also trigger when the user complains about irrelevant issues, noisy linters, or wants to set up Codacy for the first time on a repo.
Provides comprehensive guidance for Detox mobile testing framework including React Native testing, E2E testing, and test synchronization. Use when the user asks about Detox, needs to test React Native applications, write E2E tests for mobile apps, or configure Detox.
Prevent panics, silent corruption, and subtle runtime bugs in Go: typed-nil interfaces, slice aliasing, integer overflow on conversion, float comparison, defer in loops, defensive copying at API boundaries, and zero-value design. Use when hardening code against crashes, reviewing for nil-safety, converting between numeric types, or deciding what to copy at a package boundary. Trigger examples: "nil pointer panic", "why is my error not nil", "slice aliasing", "integer overflow", "compare floats", "defer in a loop", "defensive copy", "make this crash-proof". Not for: data races and goroutine lifecycle (go-concurrency-review), injection and auth (go-security-audit), a panic that already happened (go-troubleshooting).
Validate Dockerfiles against best practices for syntax errors, image tagging, package pinning, and shell script issues in RUN commands. Auto-detects hadolint binary or Docker image fallback. Checks project config or falls back to sensible defaults. Use when creating or modifying Dockerfiles, reviewing container images, or catching Dockerfile anti-patterns before building.
Hybrid adaptive memory: Cheatsheet (positive patterns pre-generation) and Immune (negative patterns post-generation) with Hot/Cold tiered auto-learning. Triggers on: "scan for errors", "immune scan", "check output quality", "antibody scan". NOT for PR review (use pr-review) or repo audits (use repo-sentinel).
Validate Life Sciences Cloud for Customer Engagement org prerequisites. Use when an admin needs to verify that all required settings, permissions, OWD sharing rules, and feature toggles are correctly configured before deploying Life Sciences Cloud. Checks user profile, permission sets, Life Sciences CE settings, surveys, OWD, inventory, account plans, care plans, Chatter, data protection, multi-currency, state/country picklists, and person accounts. Reports pass/fail for each check with remediation steps. TRIGGER when: user says 'check prerequisites', 'verify org setup for Life Sciences', 'LSC readiness check', 'pre-requisites for Life Sciences Cloud', 'validate org for LSC'. DO NOT TRIGGER when: user wants to automatically enable or configure these settings, or when the user asks for the full end-to-end Life Sciences Cloud setup / orchestration (that is the `life-sciences-fieldsalesrep-coordinate` orchestrator's job — this skill runs only as a standalone prerequisites check).
Use when validating subjective quality criteria that cannot be deterministically tested — applies LLM-based evaluation with structured rubrics for tone, aesthetics, UX feel, documentation quality, and code readability. Triggers: documentation quality check, error message tone review, UX copy evaluation, code readability assessment, design aesthetic review.
Use when the user says 'my site looks bad on mobile', 'check mobile layout', 'responsive audit', or 'site broken on phones'. Diagnoses breakpoint problems, text sizing, column stacking failures, hidden elements, and navigation menu behavior, device by device.
Interpret validation errors and guide fixing them. Use when encountering validation errors, validation warnings, false positives, operator structure issues, or need help understanding validation results. Also use when asking about validation profiles, error types, the validation loop process, or auto-fix capabilities. Consult this skill whenever a validate_node or validate_workflow call returns errors or warnings — it knows which warnings are false positives and which errors need real fixes.
Validate that implementation matches change artifacts using `/opsx:verify`, checking completeness, correctness, and coherence. Use when the user says "verify implementation", "check my work", "/opsx:verify", or wants quality validation before archiving.
OpenSpec-flavored end-to-end Jira bug-fix workflow that persists root cause, behavior change, fix plan, verification, and archive into OpenSpec artifacts (openspec/changes/<name>/, archived into openspec/specs/) instead of leaving them only in chat context or Jira comments. Use when a Jira issue needs long-term behavioral-contract traceability, team review, or auditability. Do NOT use for a quick fix needing no traceability — use jira-fix-workflow instead. Triggers: "opsx-jira-fix", "OpenSpec Jira fix", "规范化修复 Jira", "opsx修复Jira", "Jira OpenSpec 修复", "opsx自动修复Jira", "用OpenSpec修复Jira", "opsx-jira-fix-workflow"; "ai-proxy mode", "AI 代理模式", "切换 ai-proxy" / opsx jira fix, OpenSpec Jira fix workflow, ai-proxy mode, switch to ai-proxy.
Run a structured rigor audit on how a team validated (or plans to validate) its assumptions — for a discovery plan, experiment, test, or any "we validated it" claim. Produces a severity-rated issue list with concrete fixes — catches testing the comfortable assumption instead of the riskiest, untestable phrasing, method fidelity mismatched to decision risk, stated intent mistaken for behavior, success criteria set after the fact, goalpost-moving, and underpowered evidence. Use when reviewing a validation/discovery approach or as the validation step after generating an assumption-test plan.
Run a structured quality audit on a product decision or its rationale — a build/buy/sunset/re-platform/pricing call, a decision log, a retrospective, or any "we decided X because…". Produces a severity-rated issue list with concrete fixes — catches outcome-based reasoning (resulting), deliberation mismatched to reversibility, uncalibrated confidence, confirmation/anchoring/framing/sunk-cost bias, missing pre-mortems, and undocumented reasoning. Use when reviewing a major decision or its write-up, or as a validation step after a decision is made.
Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts.
Strategic regression testing with test selection, impact analysis, and continuous regression management. Use when verifying fixes don't break existing functionality, planning regression suites, or optimizing test execution for faster feedback.
Repo Updater - Multi-repo synchronization with AI-assisted review orchestration. Parallel sync, agent-sweep for dirty repos, ntm integration, git plumbing. 17K LOC Bash CLI. Use when syncing multiple GitHub repositories, running agent-sweep on uncommitted changes, or orchestrating AI-assisted code review across repos.
Plan, execute, audit, and verify behavior-preserving migrations from Java Maven or Gradle projects to project-shaped Rust Cargo workspaces, including derived crate boundaries, scale-appropriate topology, 100% lossless source-test/case migration, byte-identical test assets, and complete per-case differential parity. Use when comparing repositories at module, package, object, file, method, parameter, documentation, example, test, fixture/data, dependency-reuse, JavaBean/script-property, concurrency, runtime-behavior, oversized Rust-file, or test-organization level; producing migration documents; continuing an incomplete port; or repairing workspace drift. Enforces source-authoritative inventories, Rust-native APIs, a 500-line cohesion-review threshold and 800-line authored-file blocker, idiomatic unit/integration test placement, strict non-completion states, frozen baselines, and unified verification.
Reviews Rust test code for unit test patterns, integration test structure, async testing, mocking approaches, and property-based testing. Covers Rust 2024 edition changes including async fn in traits for mocks, #[expect] lint suppression, LazyLock test fixtures, and temporary scope changes affecting test assertions. Use when reviewing _test.rs files, #[cfg(test)] modules, or test infrastructure in Rust projects. Covers tokio::test, test fixtures, and assertion patterns.
Validate shell scripts for syntax errors, common bugs, quoting issues, and portability problems. Auto-detects shellcheck binary or guides installation. Checks project config or falls back to sensible defaults. Use when creating or modifying shell scripts, validating bash/sh files, or catching shell scripting mistakes before committing.
Analyzes a repository and any existing test suite, grades existing tests for weakness, classifies mocks, emits a phased roadmap for building a test suite that catches real regressions, then executes those phases one at a time. Use when planning or building a test suite, assessing whether existing tests are worth anything, adding tests to a legacy codebase, or when the user mentions test coverage, test strategy, or weak tests. Not for reviewing a branch for bugs, and not for fixing the bugs it logs.
Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization. Applies the RED-GREEN-REFACTOR cycle to process documentation by running a baseline without the skill, writing to address failures, and iterating to close loopholes.
[user] Organizes affected screens and entry paths for users to check themselves, and creates a single HTML QA sheet that saves checks and notes by default. Separates automated verification evidence from manual checks in the same session; returns Markdown or a list without a file only when explicitly requested. Not for product approval or PR submission.
Unity 6 testing guide. Use when writing unit tests, integration tests, or doing TDD with Unity Test Framework. Covers Edit Mode and Play Mode tests, NUnit attributes ([Test], [UnityTest], [SetUp], [TearDown]), testing MonoBehaviours and coroutines, and CI/CD integration. Based on Unity 6.3 LTS documentation.
Runs project compile, test, and lint commands between implementation and quality review. Gates simplify-and-harden behind machine verification. If checks fail, routes back to implementation with diagnostics for a fix loop. If checks pass, signals ready for the quality pass. Use after any implementation work completes and before simplify-and-harden. Essential for the inner loop's verify step.
Multimodal UI understanding and single-step planning via OpenAI-compatible Responses APIs. Use when you need AIQuery/AIAssert and plan-next to extract UI element coordinates, validate UI assertions, summarize screenshots, or decide the next UI action from an image. External agents handle execution via adb/hdc and multi-step loops. Defaults to Doubao models but can be pointed at other multimodal providers via base URL, API key, and model name.
Audit a website with the squirrelscan CLI and fix the findings in code. Runs SEO, performance, security, technical, content, accessibility, and 15 other rule categories (260+ rules), returns an LLM-optimized report, then drives an iterative fix loop, mapping issues to source files, applying fixes, and re-auditing until the site scores well. Use to discover and assess website or webapp issues and drive them to fixed.
Use when assessing project compliance against checkpoint-enabled skills, running /assess, triaging assessment results, or planning measurable improvements to coverage, mutation testing, or quality gates. Also use for checkpoint-backed release-readiness checks. A standalone code review or a small prose edit does not by itself require a project-wide assessment.
Use to close the loop on agent quality — turn a failure signal into a verified fix. Triggers: "improve my agent", "self-improving agent", "auto-tune / iterate on my prompt", "fix my agent from test results" — and production-call bug fixing: "fix this prod call issue", "debug and fix call ID", "reproduce this production bug", "regression test before raising PR". Works on any stack: provider-dashboard agents (VAPI, Retell, ElevenLabs, Bland) AND agents whose config lives in the customer's own repo, database, or prompt registry with the provider agent created at deploy time, or with custom mock servers. Fixed safety invariants (must-fail-first reproduction, attestation, no-prod-in-loop) + a per-project capability manifest declaring where config lives and how to read/render/apply/deploy/verify it.
Coding standards for writing, editing, and reviewing code. Routes to language/framework-specific rules (Next.js, NestJS, Laravel, vanilla JS/TS, NativeScript, React Native/Expo, C#/.NET, Node Express/Fastify, Cocos Creator, Vue/Nuxt, Spring Boot, Django, FastAPI, Flask, Go HTTP) PLUS universal clean-code rules (functions, naming, objects/data, formatting, error handling, SOLID/KISS/DRY, universal structural rules) that apply to every language. Consult before any code change. Use when the user says "write a component", "add an endpoint", "refactor this", "review this diff/PR", or "is this clean?". Make sure to consult this skill for ANY code authoring or review task, even when the user does not explicitly ask for "standards" — every write/edit/review must comply.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.