Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.
Configures monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks or forecasting capacity needs.
Pre-deployment verification checklist. Use when about to ship a release, deploying a change with database migrations or feature flags, verifying CI status and approvals before going to production, or documenting rollback triggers ahead of time.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
Control Android devices and emulators from inside Orca over adb, with the live device view in Orca's emulator pane. Use when driving an adb-connected emulator or phone on Windows, Linux, or macOS: booting AVDs, taps, swipes, typing, hardware buttons, rotation, app install and launch, runtime permissions, the accessibility tree, and logcat. For an iOS simulator use the iOS emulator skill; build the APK with Gradle first.
AI-powered codebase security scanner that reasons about code like a security researcher — tracing data flows, understanding component interactions, and catching vulnerabilities that pattern-matching tools miss. Use this skill when asked to scan code for security vulnerabilities, find bugs, check for SQL injection, XSS, command injection, exposed API keys, hardcoded secrets, insecure dependencies, access control issues, or any request like "is my code secure?", "review for security issues", "audit this codebase", or "check for vulnerabilities". Covers injection flaws, authentication and access control bugs, secrets exposure, weak cryptography, insecure dependencies, and business logic issues across JavaScript, TypeScript, Python, Java, PHP, Go, Ruby, and Rust.
Smart contract development advisor based on Trail of Bits' best practices. Analyzes codebase to generate documentation/specifications, review architecture, check upgradeability patterns, assess implementation quality, identify pitfalls, review dependencies, and evaluate testing. Use when asking whether a smart contract project follows development best practices, reviewing on-chain/off-chain split, upgradeability, or delegatecall proxy patterns against guidelines, or seeking recommendations on contract design, inheritance, events, documentation, dependencies, or test strategy
Set up Husky pre-commit hooks in the current repository, integrating lint-staged (Prettier), type checking and tests. Useful when users want to add pre-commit hooks, set up Husky, configure lint-staged, or run formatting, type checking and tests on commit.
Measures and interprets what a fuzzing campaign actually reaches, using llvm-cov, lcov, or a fuzzer's own coverage output. Covers baselining a new campaign, reading coverage reports, and turning uncovered regions into harness, seed, or dictionary work. Use when a fuzzer plateaus, when judging whether a harness is effective, after changing a harness, or when asking why some code is never reached.
Run a structured 5-day process to prototype, test, and validate product ideas with real users. Use when the user mentions "design sprint", "validate before we build", "rapid prototype", "test with users", or "should we build this". Also trigger when a team is stuck in endless debate over a high-stakes product decision, or wants to de-risk a costly idea before investing in development. Covers mapping, sketching, deciding, prototyping, and testing across Monday-Friday. For ongoing experimentation and MVPs, see lean-startup. For customer job analysis, see jobs-to-be-done. For non-leading user interviews, see mom-test.
Inspect an agent repository and optional traces, interview the user, write reviewed Task Specs, build and audit Harbor tasks, and bootstrap reusable project World Knowledge Skills. Use for agent evals, benchmark design, Task generation, controlled Environments, synthetic data, Verifiers, Harbor runs, calibration, or continuous benchmark maintenance.
Designs and improves fuzzing harnesses for C/C++ and Rust. Covers mapping raw bytes onto a target API, generating structured inputs, avoiding non-determinism and false crashes, and deciding what to fuzz together. Use when writing a first LLVMFuzzerTestOneInput or fuzz_target! harness, when a campaign finds nothing or reports crashes that will not reproduce, or when the target API needs structured rather than raw input.
Sets up and runs cargo-fuzz, the standard fuzzing tool for Cargo-based Rust projects. Covers cargo fuzz init, the nightly toolchain requirement, fuzz_target! harnesses, Arbitrary-derived structured inputs, sanitizer options, cargo fuzz coverage, and reproducing a crash artifact. Use when fuzzing a Rust crate, writing a fuzz_target!, exercising unsafe blocks or FFI in Rust, or triaging a cargo fuzz crash.
Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation frameworks; analyzing coverage gaps; performance testing with k6 or Artillery; security testing with OWASP methods; debugging flaky tests; or working on QA, regression, test automation, quality gates, shift-left testing, or test maintenance.
Builds and runs code under AddressSanitizer to catch buffer overflows, use-after-free, and other memory errors during fuzzing or tests. Covers -fsanitize=address builds, ASAN_OPTIONS, reading the crash report, LeakSanitizer, and the overhead and platform trade-offs. Use when fuzzing C/C++ or Rust that has unsafe blocks or FFI, when debugging a memory corruption crash, or when reading an ASan stack trace.
Patches past the barriers that stop a fuzzer making progress — checksum and hash verification, magic-value validation, time-based seeds, and other non-deterministic global state. Covers locating the blocking check, neutering it behind a fuzzing build flag, and avoiding the false positives a patch can introduce. Use when a fuzzer is stuck at validation, when coverage shows large regions behind a checksum, or when valid inputs are impractical to generate.
Expert guidance for Swift Testing: test structure, #expect/#require macros, traits and tags, parameterized tests, test plans, parallel execution, async waiting patterns, and XCTest migration. Use when writing new Swift tests, modernizing XCTest suites, debugging flaky tests, or improving test quality and maintainability in Apple-platform or Swift server projects.
Use when writing E2E tests with Playwright, setting up test infrastructure, or debugging flaky browser tests. Invoke to write test scripts, create page objects, configure test fixtures, set up reporters, add CI integration, implement API mocking, or perform visual regression testing. Trigger terms: Playwright, E2E test, end-to-end, browser testing, automation, UI testing, visual testing, Page Object Model, test flakiness.
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.
Write tests using React Native Testing Library (RNTL) v13 and v14 (`@testing-library/react-native`). Use when writing, reviewing, or fixing React Native component tests. Covers: render, screen, queries (getBy/getAllBy/queryBy/findBy), Jest matchers, userEvent, fireEvent, waitFor, and async patterns. Supports v13 (React 18, sync render) and v14 (React 19+, async render). Triggers on: test files for React Native components, RNTL imports, mentions of "testing library", "write tests", "component tests", or "RNTL".
Builds and applies fuzzing dictionaries so a fuzzer can produce the keywords, magic bytes, and tokens a target expects. Covers extracting tokens from source, headers, binaries, and specifications, dictionary syntax, and wiring one into libFuzzer or AFL++. Use when fuzzing a parser, protocol, or file format, when coverage stalls at input validation, or when a target compares against fixed strings.
Gets independent code reviews from Codex or Antigravity for uncommitted changes, branch diffs, and commits. Use when the user requests an external review, a second opinion on code, a codex review, a gemini review, an antigravity review, or /second-opinion.
Generates Claude Code skills from the Trail of Bits Testing Handbook (appsec.guide), analyzing handbook pages and emitting SKILL.md files with the structure each skill type requires. Use when creating or refreshing a skill from handbook content, or when the user names the testing handbook or appsec.guide. Not for answering security testing questions — the generated skills cover those.
Sets up and runs libFuzzer, the coverage-guided fuzzer built into LLVM, on C/C++ code that compiles with Clang. Covers harness structure, -fsanitize=fuzzer builds, corpus and dictionary management, sanitizer integration, and campaign triage. Use when writing or debugging an LLVMFuzzerTestOneInput harness, starting fuzzing on a C/C++ library, choosing between libFuzzer and AFL++, or working out why a libFuzzer run finds nothing.
Measures timing side channels in cryptographic implementations by running them, using dudect for statistical analysis and Timecop over Valgrind for dynamic tracing. Covers the formal, symbolic, dynamic, and statistical tool categories and how to read a result. Use when testing whether a running implementation is constant-time, measuring timing variance on a compiled binary, or investigating a suspected timing attack. Not for statically inspecting compiler output — the constant-time-analysis plugin covers that.
Sets up and runs AFL++ for multi-core fuzzing of C/C++ projects built with afl-clang-fast or afl-gcc-fast. Covers instrumentation modes, parallel main and secondary campaigns, persistent mode, corpus minimization, and crash triage. Use when scaling fuzzing across cores, fuzzing a mature C/C++ codebase, reading the afl-fuzz status screen, or moving on after libFuzzer has plateaued.
Enrolls a project in OSS-Fuzz, Google's free continuous fuzzing service for open source, and drives it locally. Covers project.yaml, Dockerfile and build.sh setup, the helper scripts, reproducing OSS-Fuzz crash reports, and the acceptance criteria. Use when setting up continuous fuzzing for an open-source project, reproducing an OSS-Fuzz bug report, or testing an OSS-Fuzz build before submitting it.
Sets up and runs Atheris, the coverage-guided Python fuzzer built on libFuzzer. Covers TestOneInput harnesses, FuzzedDataProvider, instrumenting both pure Python and native C extensions, and running under AddressSanitizer. Use when fuzzing a Python package, hunting memory corruption in a Python C extension, or choosing between Atheris and Hypothesis for a Python target.
Validates cryptographic implementations against Project Wycheproof's test vectors, which encode known attacks and edge cases across AES, RSA, ECDSA, ECDH, and more. Covers loading test vectors, mapping result flags onto pass and fail expectations, and reading a failure. Use when testing a crypto implementation against known attacks, checking a library against standard test vectors, or investigating why two implementations disagree on the same input.
Outsider-perspective end-to-end review of a plan, PR, or code change. First questions intent and whether a simpler/more elegant approach would achieve the same goal, then traces the actual code path (not just the diff) to verify the change does what it claims. Output is concise, actionable, and every call carries its rationale. Trigger on /scrutinize and proactively whenever the user asks to review, audit, sanity-check, or get a second opinion on a plan, PR, diff, design doc, or proposed code change.
Builds custom fuzzers with LibAFL, the modular Rust fuzzing library. Covers composing observers, feedbacks, mutators, schedulers, and executors into a fuzzer for targets the standard tools do not fit. Use when writing a bespoke fuzzer or mutator, fuzzing a non-standard target or architecture, implementing a fuzzing research idea, or when libFuzzer and AFL++ lack the control you need.
Parses error messages, traces execution flow through stack traces, correlates log entries to identify failure points, and applies systematic hypothesis-driven methodology to isolate and resolve bugs. Use when investigating errors, analyzing stack traces, finding root causes of unexpected behavior, troubleshooting crashes, or performing log analysis, error investigation, or root cause analysis.
Sets up and runs Ruzzy, Trail of Bits' coverage-guided Ruby fuzzer and the only production-ready one for the language. Covers harness structure, fuzzing pure Ruby and the native C extensions in gems, and sanitizer builds. Use when fuzzing a Ruby library or gem, testing a Ruby C extension for memory safety, or asking how to fuzz Ruby at all.
Turn ambiguous or high-impact product and engineering changes into scoped, verifiable acceptance criteria before or alongside implementation. Use when a user asks to clarify a feature, define acceptance criteria, de-risk a security/data/migration/integration change, prepare implementation requirements for another agent, or make a complex request testable. Do not trigger for trivial edits, straightforward fixes, active debugging, code review, or implementation requests whose acceptance conditions are already clear unless the user explicitly invokes this skill.
Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.
Analyzes DevOps Center test failures and Code Analyzer violations in plain language — failure category, offending file/class/method/line, rule violated, fix direction, and prioritized improvement suggestions (test-code vs production-code) — then optionally creates a tracked fix WorkItem on explicit request. Analysis is pure reasoning; work-item creation is a confirmation-gated write. Use this skill to explain failures or improvement suggestions, translate Code Analyzer violations, or track a fix as a work item. TRIGGER when: a run failed and the user wants root cause; a quality gate failure needs explaining; violations need translating; the user shares a failure payload and asks how to address it; wants to strengthen tests; or wants to create a fix work item, log a remediation, or assign a failure. DO NOT TRIGGER when: the user wants fix code written (use platform-apex-generate) or new test classes authored (use platform-apex-test-generate).
Runs an autonomous review-and-fix improvement loop over a Claude Code skill until a review comes back clean, with a cross-round findings ledger, escalation when fixes stop converging, and a mechanical scope guard. Reviews are performed by the plugin-dev skill-reviewer agent. Use to fix skill quality issues, iteratively refine a skill, or resume a loop after an escalation ('fix my skill', 'improve this skill until it passes review', 'skill improvement loop'). NOT for a one-time review — use the plugin-dev skill-reviewer agent directly.
Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy patterns.
Use after completing any non-trivial task. The agent rates its output on five axes—accuracy, completeness, clarity, actionability, and conciseness—with concrete evidence for each criterion. Produces a structured scorecard from 1 to 5 with specific improvement suggestions.
Configures DevOps Center pipeline testing infrastructure: enables a test provider so its suites become available, re-syncs a configured provider to pull in new suites, or creates a quality gate with rules on a stage. Routes by intent across three modes after running shared prerequisite checks and an explicit confirmation gate. Use this skill when a user wants to set up, configure, enable, sync, or refresh a test provider, or set/configure a quality gate or coverage threshold on a DevOps Center pipeline stage. TRIGGER when the user wants to configure/enable/add/set up a test provider, re-sync or refresh a provider's suite list, pull in new suites, or set/configure a quality gate, coverage threshold, or testing benchmark on a stage. DO NOT TRIGGER when: assigning existing suites to a stage (use dx-devops-test-suite-assignments-configure), running or retriggering a suite (use dx-devops-test-suite-run), or non-DevOps-Center work.
Build and audit SwiftUI, UIKit, and AppKit accessibility for VoiceOver, Voice Control, Switch Control, Full Keyboard Access, Dynamic Type, focus restoration, labels/traits/actions, traversal, custom rotors, NSAccessibility, XCTest checks, adaptive system preferences, and App Store accessibility declarations. Use when implementing accessible UI, fixing an accessibility audit, testing assistive-technology behavior, or substantiating App Store Accessibility Nutrition Labels.
Recommends and manages DevOps Center test suite assignments for pipeline stages. Mode A analyzes a commit diff against assigned suite metadata to recommend relevant existing suites and flag coverage gaps (pure reasoning). Modes B-D assign a single suite, bulk-map multiple suites with a mandatory impact preview, or add/remove test classes with governance rules, via the testSuiteStages Connect API. Use this skill to recommend suites for a commit, assign or map suites to stages, or add/remove tests in a suite. TRIGGER when: the user asks which suites to run for a commit/diff or what covers their changes; a suite is unlinked and the user wants it assigned; the user wants to configure suite-to-stage mappings, assign multiple suites, or add/remove/sync tests in a suite. DO NOT TRIGGER when: configuring or syncing a test provider (use dx-devops-test-pipeline-configure), running suites (use dx-devops-test-suite-run), or authoring/running tests directly (use platform-apex-test-generate or platform-apex-test-run).
Runs DevOps Center test suites on a pipeline stage (Pre-Promote, Post-Promote, or Review event) end to end: triggers async execution via the Connect API after an explicit confirmation gate, then polls by runId at provider-specific intervals until it completes, fails, or times out, and hands results to failure analysis. Also retriggers a quality gate after fixes, but only once coverage meets the threshold. Use this skill when a user wants to run, kick off, or launch test suites on a stage, re-run a quality gate, or watch an in-progress run to completion. TRIGGER when: the user wants to run/launch suites on a stage, execute tests before or after promotion, re-run a quality gate after fixing failures, unblock a blocked promotion after adding tests, or poll/watch an in-progress run. DO NOT TRIGGER when: running sf apex run test directly (use platform-apex-test-run), or configuring a NEW gate or threshold (use dx-devops-test-pipeline-configure).
INVOKE THIS SKILL when building evaluation pipelines for LangSmith. Covers three core components: (1) Creating Evaluators - LLM-as-Judge, custom code; (2) Defining Run Functions - how to capture outputs and trajectories from your agent; (3) Running Evaluations - locally with evaluate() or auto-run via LangSmith. Uses the langsmith CLI tool.
Generate, validate, and review k6 test scripts — load, stress, spike, soak, smoke, breakpoint, functional, and protocol. Covers HTTP, WebSocket, gRPC, browser, all executors, thresholds, checks, custom metrics, the k6-testing library, k6 Cloud execution, and the xk6 extension ecosystem; uses the xk6-docs CLI (with grafana.com web fallback) for docs lookup and validates every script by running it. Use when writing, generating, validating, or debugging any k6 or load-test script (including plain-language asks like "load test this API" or "stress test my service"), choosing executors/scenarios, or setting thresholds. For end-to-end website performance suites use k6-perf-test-website; for documenting k6 itself use k6-docs; for a browser/UI test authored from a plain-language user journey (selectors discovered by exploring the live site) use k6-browser-test.
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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.