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SkillsMP は jeremylongshore/tons-of-skills-marketplace から 3,430 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
jeremylongshore/tons-of-skills-marketplace収集済み skill 3,430 件中 40 件を表示しています。
Handle Kling AI API rate limits with backoff and queuing strategies. Use when hitting 429 errors or planning high-volume workflows. Trigger with phrases like 'klingai rate limit', 'kling ai 429', 'klingai throttle', 'kling api limits'.
原文の言語: 英語
Production reference architecture for Kling AI video generation platforms. Use when designing scalable systems. Trigger with phrases like 'klingai architecture', 'kling ai system design', 'video platform architecture', 'klingai production setup'.
原文の言語: 英語
Production SDK patterns for Kling AI: client wrapper, retry logic, async polling, and error handling. Use when building robust integrations. Trigger with phrases like 'klingai sdk', 'kling ai client', 'klingai patterns', 'kling ai wrapper'.
原文の言語: 英語
Download and store Kling AI generated videos in cloud storage (S3, GCS, Azure). Use when persisting videos or building CDN pipelines. Trigger with phrases like 'klingai storage', 'save klingai video', 'kling ai s3 upload', 'klingai cloud storage'.
原文の言語: 英語
Apply artistic styles and visual effects to Kling AI video generation. Use when creating stylized content or using effects API. Trigger with phrases like 'klingai style', 'kling ai effects', 'klingai artistic video', 'stylize klingai video'.
原文の言語: 英語
Configure Kling AI for teams with per-project API keys, usage quotas, and role-based access. Trigger with phrases like 'klingai team', 'kling ai organization', 'klingai multi-user', 'shared klingai access'.
原文の言語: 英語
Generate videos from text prompts with Kling AI. Use when creating videos from descriptions, learning prompt techniques, or building T2V pipelines. Trigger with phrases like 'kling ai text to video', 'klingai prompt', 'generate video from text', 'text2video…
原文の言語: 英語
Migrate between Kling AI model versions safely. Use when upgrading from v1.x to v2.x or adopting new features. Trigger with phrases like 'klingai upgrade', 'kling ai migrate', 'klingai version update', 'upgrade kling model'.
原文の言語: 英語
Build usage analytics and reporting for Kling AI video generation. Use when tracking patterns, analyzing costs, or building dashboards. Trigger with phrases like 'klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats'.
原文の言語: 英語
Extend video duration using Kling AI continuation. Use when creating longer videos from shorter clips or building sequences. Trigger with phrases like 'klingai extend video', 'kling ai video continuation', 'klingai longer video', 'extend klingai clip'.
原文の言語: 英語
Configure webhook callbacks for Kling AI task completion. Use when building event-driven pipelines or replacing polling. Trigger with phrases like 'klingai webhook', 'kling ai callback', 'klingai notifications', 'video completion webhook'.
原文の言語: 英語
Wire LangChain 1.0 / LangGraph 1.0 tests into a GitHub Actions pipeline — unit tests with FakeListChatModel, VCR-gated integration tests, warning-filter policy, and eval-regression merge gates. Complements langchain-local-dev-loop (F23) which covers the inner…
原文の言語: 英語
Paste-match catalog of 14 real LangChain 1.0 / LangGraph 1.0 exceptions with named causes and named fixes, plus a triage decision tree. Use when you have a traceback and want the specific fix, not speculative documentation. Covers ImportError (0.2/0.3 → 1.0…
原文の言語: 英語
Works correctly with LangChain 1.0's typed content blocks on AIMessage.content — text, tool_use, image, thinking, document — across Claude, GPT-4o, and Gemini, including multi-modal composition and tool-call iteration. Use when composing multi-modal messages,…
原文の言語: 英語
Compose LangChain 1.0 chains with RunnableParallel, RunnableBranch, RunnablePassthrough.assign, and RunnableLambda — correct input/output shapes, debug probes, and typed composition that catches dict-shape bugs before invocation. Use when wiring multi-step…
原文の言語: 英語
Control LangChain 1.0 AI spend with accurate streaming token accounting, model tiering, provider-specific cache hit tuning, per-tenant budgets, and retry dedup. Use when AI spend grows faster than traffic, a cost regression lands, or you need per-tenant…
原文の言語: 英語
Load and chunk documents for LangChain 1.0 RAG pipelines correctly — language-aware splitters, table-safe PDF loaders, Cloudflare-compatible web loaders, chunk-boundary strategies that survive real-world structure. Use when building a RAG pipeline, diagnosing…
原文の言語: 英語
Produce a reproducible, sanitized diagnostic bundle for a LangChain / LangGraph incident — environment snapshot, version manifest, filtered astream_events(v2) transcript, propagating callback stack, LangSmith trace URL — so a debug colleague can reproduce the…
原文の言語: 英語
Build a LangGraph 1.0 Deep Agent — planner + subagents + virtual filesystem + reflection loop — without the state-growth and prompt-inheritance traps. Use when building a long-horizon agent that must plan, delegate subtasks, work against a scratchpad…
原文の言語: 英語
Build and query vector stores with LangChain 1.0 without getting burned by flipped score semantics, embedding-dim mismatches, reranker quirks, and chunk-splitter bugs. Use when building a RAG pipeline, choosing between FAISS / Pinecone / Chroma / PGVector,…
原文の言語: 英語
Enforce tenant isolation and role-based access across LangChain 1.0 chains and LangGraph 1.0 agents — per-request retriever construction, tenant-scoped rate limits, role-scoped tool allowlists, and structured audit logs. Use when building multi-tenant saas,…
原文の言語: 英語
Build reproducible evaluation pipelines for LangChain 1.0 chains and LangGraph 1.0 agents — golden datasets, LangSmith evaluate(), ragas RAG metrics, deepeval LLM-as-judge, agent trajectory analysis, and CI gating on quality regressions. Use when setting up…
原文の言語: 英語
Triage LangChain 1.0 / LangGraph 1.0 production incidents — LLM-specific SLOs, provider outage runbook, latency spike decision tree, cost-overrun response, agent loop containment. Use during an on-call page, in a post-mortem, or writing the team's first LLM…
原文の言語: 英語
Build a correct LangGraph 1.0 StateGraph — typed TypedDict state with reducers, nodes, edges, compile, and recursion budgets — without hitting the silent-termination and state-replacement traps. Use when writing your first LangGraph StateGraph, diagnosing why…
原文の言語: 英語
Persist LangGraph agent state correctly with MemorySaver and PostgresSaver — thread_id discipline, JSON-serializable state rules, time-travel, schema migration. Use when adding chat memory, migrating from ConversationBufferMemory, or time-traveling an agent…
原文の言語: 英語
Pick the correct LangGraph 1.0 stream_mode ("messages" vs "updates" vs "values"), wire it into SSE or WebSocket without proxy-buffering gotchas, and filter astream_events(v2) server-side before forwarding to the browser. Use when building a live-token chat…
原文の言語: 英語
Compose LangGraph 1.0 subgraphs correctly — shared state key propagation, Send / Command(graph=...) dispatch, callback scoping, per-subgraph recursion budgets, and testing each subgraph in isolation. Use when building a planner + executor, a nested agent…
原文の言語: 英語
Build a fast, deterministic local test loop for LangChain 1.0 / LangGraph 1.0 — FakeListChatModel fixtures, pytest config, VCR cassettes with key redaction, warning-filter policy. Use when adding tests to a new chain, fixing a flaky test, or making…
原文の言語: 英語
Build composable middleware for LangChain 1.0 chains and LangGraph 1.0 agents — PII redaction, caching, retry, token budgets, guardrails — with ORDERING rules that avoid cache-key leakage and double-counting. Use when adding cross-cutting behavior, hardening…
原文の言語: 英語
Invoke Claude, GPT-4o, and Gemini through LangChain 1.0 without tripping on the content-block, token-accounting, and structured-output quirks that silently break production code. Use when initializing chat models, routing across providers, iterating AIMessage…
原文の言語: 英語
Wire LangSmith tracing and custom metric callbacks into a LangChain 1.0 chain or LangGraph 1.0 agent correctly — env-var spelling, subgraph propagation, per-tenant dimensions, cost and latency counters. Use when setting up observability on a new service,…
原文の言語: 英語
Wire LangChain 1.0 / LangGraph 1.0 traces into an OpenTelemetry-native backend (Jaeger, Honeycomb, Grafana Tempo, Datadog) with LLM-specific SLOs, safe prompt-content policy, and subgraph-aware span propagation. Use when LangSmith is not the right fit…
原文の言語: 英語
Tune LangChain 1.0 / LangGraph 1.0 Python chains and agents for throughput, latency, and cost — streaming modes, explicit batch concurrency, semantic plus exact caches, persistent message history, and async-safe retriever patterns. Use when p95 latency…
原文の言語: 英語
Manage LangChain 1.0 prompts like code — LangSmith prompt hub versioning, XML-tag conventions for Claude, few-shot example selection, discriminated-union extraction schemas, and A/B test wiring. Use when taking ad-hoc prompts into version control, migrating…
原文の言語: 英語
Rate-limit LangChain 1.0 calls correctly across multi-worker deployments — Redis-backed limiters, asyncio.Semaphore, narrow exception whitelists, and provider-specific throttle handling. Use when hitting 429s in production, scaling workers horizontally, or…
原文の言語: 英語
A reference layered architecture for production LangChain 1.0 / LangGraph 1.0 services — LLM factory with version-safe defaults, chain/graph registry, retriever and tool DI, Pydantic-validated config, per-request tenant scoping, middleware ordering,…
原文の言語: 英語
Harden a LangChain 1.0 chain or LangGraph agent against prompt injection, tool abuse, PII leakage in traces, and secrets exfiltration — wrap user content in XML tags, enforce the tool allowlist via provider-native tool calling, redact PII in middleware…
原文の言語: 英語
Migrate a LangChain 0.3.x Python codebase to LangChain 1.0 / LangGraph 1.0 without breaking production — named breaking changes, codemod patterns, and a phased rollout. Use when upgrading LangChain or LangGraph from 0.2 or 0.3 to 1.0, when hitting ImportError…
原文の言語: 英語
Dispatch LangChain 1.0 chain/agent events to external systems — webhooks, Kafka, Redis Streams, SNS — via async fire-and-forget callbacks, subgraph-aware wiring, and HMAC-signed delivery with idempotency keys. Use when firing webhooks on tool calls, pushing…
原文の言語: 英語
Configure Langfuse CI/CD integration with GitHub Actions and automated testing. Use when setting up automated testing, configuring CI pipelines, or integrating Langfuse tests into your build process. Trigger with phrases like "langfuse CI", "langfuse GitHub…
原文の言語: 英語