Results for “tool calling”

Consults Fable as an independent, read-only reviewer at an effort the caller matches to the question. The calling session (usually Opus, or Haiku) holds the task and escalates one decision point. Use before committing to an interpretation or a substantial piece of writing or analysis, when stuck or not converging, when changing approach, or as a final check on a finished task. Fallback for when the native advisor tool is unavailable. Not for a session already running Fable.

The English summary is being prepared. Showing the original description.

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

The English summary is being prepared. Showing the original description.

Build conversational AI agents using Vercel AI SDK + OpenRouter. Use when creating Next.js frontends with streaming UI, tool calling, and multi-provider support.

The English summary is being prepared. Showing the original description.

Use when working on or testing imp-server and its HTTP APIs - OpenAI/Anthropic endpoints, /v1/chat/completions, /v1/messages, /v1/responses, SSE streaming, tool calling, json_schema/regex/GBNF constrained decoding, thinking/reasoning_content, cache_control/prefix cache, model loading and swapping, request priority, X-Request-Id, OTLP tracing / traceparent / Jaeger, /metrics, container env (IMP_SET), "server returns 404/garbage", API compliance. Do NOT use for CLI-only inference (imp-cli), kernel work (sm120-cuda-expert), or pure output-quality checks (check-degeneration).

The English summary is being prepared. Showing the original description.

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

The English summary is being prepared. Showing the original description.

Provision a zero-config, no-signup, temporary Upstash Redis database for an AI agent with a single POST to https://upstash.com/start-redis, with no account, API key, or SDK setup required. Use when an agent needs scratch Redis right now and the user has not provided credentials, for short-term memory across tool calls, conversation history, a sub-agent work queue, ranked recall, or a quick prototype or demo. Covers idempotent creation and re-fetching credentials, calling the database through the body-style REST API or the official SDKs, and telling the user how to claim it. The database lives 3 days unless the user claims it; not for production data, PII, or secrets.

The English summary is being prepared. Showing the original description.

The complete Go development skill. REQUIRED for ALL Go work — writing, refactoring, reviewing, generating, scaffolding, or discussing Go code, including any change to .go files. Use whenever the user mentions Go, Golang, go.mod, a Go package, or asks to build a Go backend service, CLI, library, script, MCP server (Model Context Protocol), or AI/LLM agent with tool calling or ReAct-style reasoning. Routes by project type to the right architecture, conventions, and review checklist. Highly opinionated; overrides generic Go style and lint guidance on conflicts.

The English summary is being prepared. Showing the original description.

Build conversational AI agents using Vercel AI SDK + OpenRouter. Use when creating Next.js frontends with streaming UI, tool calling, and multi-provider support.

The English summary is being prepared. Showing the original description.

Build agents on OpenAI's platform patterns — assistants-style threads, function calling, structured outputs, and multi-step tool workflows. Use when working with OpenAI models as the agent engine.

The English summary is being prepared. Showing the original description.

AI integration review patterns. Use when reviewing model API calls, prompt construction, LLM output handling, RAG pipelines, and tool-calling code. Covers prompt-injection call-chain tracing, output validation, stop reasons, token budgets, retry and timeout handling, streaming, and key/PII exposure.

The English summary is being prepared. Showing the original description.

Specification frameworks for features that call a model — approach tier, model pinning, output contracts, budgets, failure modes and eval design. Load when a spec touches model calls, prompts, retrieval, tool calling, agent loops or evals.

The English summary is being prepared. Showing the original description.

Local LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and OpenAI-compatible endpoint

The English summary is being prepared. Showing the original description.

Vercel AI SDK patterns - providers, text generation, streaming, structured output, tool calling, chat UI hooks, embeddings, and RAG

The English summary is being prepared. Showing the original description.

Generate accurate Volcengine SDK examples by locating an API through API Explorer search, fetching its swagger, and calling api/common/explorer/make-code with user-provided Params. Use when the user asks how to write Volcengine SDK code, generate SDK samples, or call a Volcengine API in Python, Go, Java, PHP, cURL, Node.js. Supports Chinese and English API names such as "角色扮演", "AssumeRole", or "STS AssumeRole". Also use for explicit SDK configuration questions about retry, timeout, AK/SK, STS, AssumeRole, temporary credentials, proxy, connection pooling, SSL, debug mode, request signing, response parsing, and error handling. If the user only needs API parameters, enum values, required fields, error codes, response schemas, pagination, or API comparisons, hand off to volcengine-api. If they need CLI-based operations, hand off to volcengine-cli.

The English summary is being prepared. Showing the original description.

Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.

The English summary is being prepared. Showing the original description.

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

The English summary is being prepared. Showing the original description.

Execute ENCODE DNase-seq pipeline from FASTQ to hotspots and footprints. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing DNase-seq data, calling DNase hypersensitive sites, performing footprinting analysis. Trigger on: DNase-seq pipeline, DNase hypersensitive, DHS, Hotspot2, footprinting, DNase I, chromatin accessibility DNase.

The English summary is being prepared. Showing the original description.

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

The English summary is being prepared. Showing the original description.

Mark and remove PCR/optical duplicates using samtools fixmate and markdup. Use when preparing alignments for variant calling or when duplicate reads would bias analysis.

The English summary is being prepared. Showing the original description.

Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.

The English summary is being prepared. Showing the original description.

Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.

The English summary is being prepared. Showing the original description.

Operate the WeChat Official Account AI-application workflow for articles, weekly digests, rich formatting, draft-box updates, and local handoff. Use for Agents, MCP, Skills, tool calling, workflows, and open-source AI updates; exclude Xiaohongshu and Xianyu.

The English summary is being prepared. Showing the original description.

Use whenever the user asks anything about OpenAI products, APIs, models, or SDKs — including factual lookups ("what is the newest model", "does X support Y"), build/coding requests ("write a script using the Responses API"), latest/current/default-model prompting guidance, model migrations, and citation requests. Invoke before calling any openaiDeveloperDocs MCP tool.

The English summary is being prepared. Showing the original description.

The `ollama` Python package used properly - chat and generate with streaming, embeddings, tool calling, structured outputs with a JSON Schema, the async client, keep_alive, timeouts and cancellation - and a project shape whose benchmark arithmetic is unit-tested against a fake transport while the live half skips loudly when no daemon answers.

The English summary is being prepared. Showing the original description.