Loading...
Loading...
Found 992 Skills
Execute Tests - walks through test cases from the test plan, collects results, and updates the plan. Use when verifying a feature implementation.
Test Plan - creates a manual test plan with traceable test cases from specification documents. Use when preparing to verify a feature implementation.
Interactively create, install, activate, and verify custom claude-mem modes, including domain-specific observation types, concept tags, optional Telegram alerts, bot setup, worker restart, and startup-context verification. Use this whenever someone asks to customize what claude-mem remembers, create or change a mode, track domain-specific notes, add observation types or tags, or send Telegram notifications for particular memories—even if they do not use the word "mode."
Use when an n8n-mcp account targets more than one n8n instance — i.e. the `n8n_instances` tool is available, the user mentions multiple n8n instances or environments (prod vs staging, several teams or clients), a workflow / datatable / credential / execution call returns an unexpected NOT_FOUND or reads data you don't recognize, or a credential create/update/delete is refused with an `INSTANCE_AMBIGUOUS` error. Covers choosing and switching which instance this MCP session targets, verifying the target before high-stakes work — credential writes above all — and recovering from misroutes and ambiguous-write fail-closes. Always consult this skill before operating on a specific instance, before any credential create/update/delete on a multi-instance account, or when a call hits the wrong/empty data or an `INSTANCE_AMBIGUOUS` error.
Verify and debug native, React Native, Expo, or Flutter apps on an Android Emulator 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 Android virtual device.
Author Grafana Cloud Synthetic Monitoring checks, with deep coverage of k6 scripted and browser checks: SM's single-VU/single-iteration execution model, assertions that actually fail probe_success (expect() and fail() vs bare check()), secrets, deterministic scripts, robust browser locators, local validation with k6 run, deployment via UI/API/Terraform, verifying probe_success, and rollback. Also helps choose the simplest sufficient check type (HTTP/ping/DNS/TCP, MultiHTTP, scripted, browser). Use when writing a synthetic check, monitoring a login/checkout/signup flow in production, converting a k6 script or an OpenAPI spec into a check, authoring a browser check, validating a user journey, or asking "is my site up from multiple regions". NOT for load, stress, or performance testing — SM runs one iteration per execution; for load tests use the grafana-k6 plugin or Grafana Cloud k6. For the broad Grafana Cloud Testing overview (SM + k6 Cloud + Faro), use the testing skill.
Validate that analytics and marketing tracking fire CORRECTLY: GA4/GTM dataLayer events, Meta/TikTok/LinkedIn pixels, and ad-tech tags. Covers building a tracking plan as the contract, intercepting collect-endpoint beacons and dataLayer.push in Playwright, asserting event name + params + values + timing + de-duplication, Consent Mode v2 gating, CI regression gating, and news-media events (article-view, scroll-depth, paywall). Use when: "test analytics tracking," "GA4 event test," "verify the pixel fires," "dataLayer test," "tracking plan," "Meta Pixel dedup," "scroll-depth tracking test," "gate tracking in CI." Not for: whether tracking is ALLOWED to fire under consent law (GDPR/CMP) — that is compliance-testing; this skill checks the data is CORRECT. SEO meta tags / structured data — out of scope. Related: compliance-testing, playwright-automation, api-testing, qa-project-context.
Validate database integrity, test migrations forward and backward, verify schema constraints, manage seed data, detect migration drift, and identify query performance issues. Covers PostgreSQL, MySQL, MongoDB with Prisma, TypeORM, Drizzle, and SQLAlchemy, plus Testcontainers test databases. Use when: "database test," "migration test," "migration rollback," "rollback test," "data integrity," "SQL test," "schema validation," "seed data," "query performance," "Testcontainers." Not for: synthetic data generation/masking at scale — use test-data-management; Docker/IaC test-environment provisioning — use test-environments; SQL injection — use security-testing. Related: test-data-management, test-environments, security-testing, ci-cd-integration.
Validate simulations across three stages — run pre-flight checks on configuration files (parameter ranges, required fields, disk space), monitor runtime logs for residual growth, NaN/Inf, and adaptive dt collapse, and perform post-flight validation of results (physical bounds, mass/energy conservation, convergence). Diagnose failed simulations with probable-cause analysis and recommended fixes. Use when preparing to launch a simulation, checking whether a running job is healthy, verifying that finished results are trustworthy, or debugging a crash or blow-up, even if the user only says "my simulation crashed" or "can I trust these results."
Rigorously evaluate an Agent Skill end-to-end across ANY coding-agent CLI — verify its scripts emit the documented numbers (deterministic checks), test whether its description triggers on the right prompts, and measure whether an agent following the SKILL.md beats a no-skill baseline (with/without pass-rate delta, mean ± stddev, benchmarked). Use whenever you need to test, benchmark, validate, grade, or quantify a skill's quality, check if a skill "actually works," compare two skill versions, optimize a skill's triggering, or set up an eval suite — even if the user just says "is this skill any good," "does my skill work," or "benchmark this skill." Drives Claude Code, OpenAI Codex, Antigravity (agy), Cursor, GitHub Copilot, Amp, opencode, or Grok in headless mode.
Measures the task accuracy of text models served by MAX using standard benchmarks such as GSM8K, MMLU, HellaSwag, ARC, AIME, GPQA, TruthfulQA, WinoGrande, and BABILong. Use when benchmarking a served model, comparing it with model-card or reference scores, verifying that a new MAX model produces correct answers, or running repeatable dataset evaluations against a MAX OpenAI-compatible endpoint.
Debug silent corruption when a MAX model loads, compiles, serves, and generates tokens but output disagrees with a reference implementation. Use whenever parity debugging stalls on scalar taps, the model returns gibberish or wrong greedy tokens, logit cosine is high but argmax differs, or generation is coherent then diverges — during an architecture port, a quantization bring-up, a multi-GPU conversion, or after a MAX upgrade. Triggers on "parity failure", "silent corruption", "logits match but tokens diverge", "top-1 mismatch", "greedy divergence", and "model serves but generates garbage". Not for crashes on load or pre-serve scaffolding (use import-model). Mandates reference-vs-MAX tensor-dump comparators first, verify fixes numerically before recompiling, and serve-vs-pipeline bisect when dumps match but text diverges.