317건 · 관련도순
Choose a small validation experiment based on your hypothesis, risks, time, and resources. Compare five methods, from technical checks to user interaction tests, and define pass, fail, and learning criteria before investing in a larger build.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze product usage in PostHog and manage experiments, dashboards, surveys, and feature flags, which control feature availability. Use connected tools to investigate user behavior and errors, run queries, and summarize findings with numbers and links.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Set up JavaScript and TypeScript projects using Anthony Fu’s conventions for code organization, development tools, and package publishing. Establish shared checks for formatting, types, unused code, and tests before committing changes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Match C# and .NET tasks to the appropriate specialist skill using the request and project evidence. Invoke available specialists, explain how to obtain missing plugins, and complete suitable tasks with standard tooling when possible.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review test files or suites for missing checks, hidden exceptions, unreliable timing, shared state, and maintenance problems. Get a severity-ranked report with specific locations, explanations, and concrete suggestions for improving the tests.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Coordinate multiple AI agents by assigning tasks, tracking dependencies, exchanging messages, and monitoring progress. Separate Git working directories help organize parallel changes, while context checks and snapshots support handoffs and recovery.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Inspect agent skills, plugins, and MCP servers locally before installation. Use a reviewed scanning policy to identify malicious instructions, risky commands, secret exposure, and dependency risks, then summarize findings and recommended next steps.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Guide bug bounty work from asset discovery to validation and reporting, while resolving local locations of security tools, wordlists, and cloned projects. It covers web and AI security testing with an emphasis on demonstrated impact.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Configures a language server so editor/agent tooling (diagnostics, go-to-definition, references, rename) works. Use when a project needs an LSP installed or wired, or a 'no LSP server configured' error appears.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Remote control of DOCA Flow over gRPC with the shipped `doca_flow_grpc` server: program Flow pipes and entries from Python, Go, Rust or Java, or from another host, instead of linking `libdoca_flow.so`. PLAINTEXT-ONLY: the server uses `grpc::InsecureServerCredentials()` with NO TLS / mTLS / token-auth knob; transport security must come from external infrastructure (e.g. an mTLS proxy / sidecar) on a trusted segment. Trigger even without "gRPC": "program Flow rules from Python on another host", "remotely configure pipes on the BlueField", "client times out connecting to the Flow server", "where is the .proto for Flow", "UNAUTHENTICATED / FAILED_PRECONDITION on a Flow RPC". Covers bringing up, configuring, hardening and debugging the server. Route elsewhere for the underlying doca-flow C API, generic gRPC tooling (protoc, language bindings), or DOCA install / BFB bring-up.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Comprehensive guide for Go database access — parameterized queries, struct scanning, NULLable columns, transactions, isolation levels, SELECT FOR UPDATE, connection pool, batch processing, context propagation, and migration tooling. Use when writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite; for database testing; or for questions about database/sql, sqlx, or pgx. Does NOT generate database schemas or migration SQL.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Modernize Golang code to use recent language features, standard library improvements, and idiomatic patterns. Use when reviewing Go code with old-style patterns, when encountering a deprecation warning, or when the user asks for modernization, a Go version upgrade (e.g. to Go 1.27), or a CI/tooling refresh. Not for structural refactors, extracting functions, or moving code between packages (→ See `samber/cc-skills-golang@golang-refactoring` skill).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Linting best practices and golangci-lint configuration for Golang projects — running linters, configuring .golangci.yml, suppressing warnings with nolint directives, interpreting lint output, and selecting linters. Use when configuring golangci-lint, asking about lint warnings or nolint suppressions, setting up code quality tooling, or choosing linters. Also use when the user mentions golangci-lint, go vet, staticcheck, or revive. Not for wiring a lint step into a GitHub Actions pipeline (→ See `samber/cc-skills-golang@golang-continuous-integration` skill).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Dependency management for Golang projects — go.mod and go.sum, `go get` install and upgrade flows, Minimal Version Selection, conflict resolution with replace/exclude/retract, `govulncheck` scanning of the module tree, outdated dependency and binary size auditing, vendoring, `tool` directives, and go.work workspaces. Use when adding, removing, or upgrading Go dependencies, deciding whether to take on a package, resolving version conflicts, or auditing what a module pulls in. Covers choosing and upgrading dependency versions, not the surrounding tooling: do NOT use for fixing an exploitable vulnerability in code (→ See `samber/cc-skills-golang@golang-security` skill) or for wiring Dependabot/Renovate update bots into CI workflows (→ See `samber/cc-skills-golang@golang-continuous-integration` skill).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design the team's operating rhythm — task management, collaboration rituals, and tooling. Use when the day-to-day cadence needs structure. For a time-boxed sprint, use `design-sprint-plan`.
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Auto-configure quality gates, hooks, and settings for a new project. Detects project type and sets up appropriate tooling. Use when onboarding a new codebase.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create or revise Agent Skills, host plugins and subagents, MCP servers and client configurations, hooks, and marketplace catalogs against their actual specifications. Use when building reusable agent capabilities or consolidating creator tooling without inventing a universal host format. Trigger with "create an agent skill", "build a plugin", "create an MCP server", "add a subagent", or "scaffold an agent integration".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create production-grade agent .md files aligned with the current Anthropic subagent contract. Also validates existing agents against the marketplace compliance rules. Use when building custom subagents, reviewing agent quality, or creating parallel agent architectures for orchestrator skills. Trigger with "/agent-creator", "create an agent", "build a subagent", or "validate my agent". Make sure to use this skill whenever creating agents/*.md files for plugins or standalone use.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
WebSocket handshake, CSWSH, tooling (wsrepl, ws-harness, Burp), and common flaws. Use when apps use real-time channels, chat, notifications, or WS-backed APIs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you need a deterministic inspection of a WordPress repository (plugin/theme/block theme/WP core/Gutenberg/full site) including tooling/tests/version hints, and a structured JSON report to guide workflows and guardrails.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Owns architecture, engineering delivery, infrastructure, data platform, and internal systems. Use this for build-versus-buy calls, technology selection, architectural direction, engineering capacity and delivery risk, technical debt tradeoffs, platform and tooling decisions, or when a technical choice has business consequences that need stating in business terms. Also use to judge whether a technical plan is sound before it is committed to.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.