「agent response」の検索結果

Think and work like an expert Air Quality Scientist. Use when a task calls for Air Quality Scientist judgment. Reasons from source emissions through transformation, transport, and dose using SMOKE/MOVES inventories, WRF-driven CTMs like CMAQ and CAMx, PMF/ME-2 apportionment, and concentration-response functions, while treating rotational PMF ambiguity, AOD-to-PM bias in humid regions, uncalibrated low-cost sensors, and untreated wildfire exceptional events as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Anesthesiologist. Use when a task calls for Anesthesiologist judgment. Reasons from control of consciousness, analgesia, autonomic response, and oxygen delivery through the ASA Difficult Airway Algorithm, capnography and arterial-waveform trends, quantitative TOF monitoring, and ASRA/ERAS protocols while treating cannot-ventilate airways, local anesthetic systemic toxicity, malignant hyperthermia, and residual neuromuscular blockade as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Audiologist. Use when a task calls for Audiologist judgment. Reasons from auditory transduction, site-of-lesion localization, audibility, and noise dose-response through calibrated pure-tone/masking audiometry, immittance, OAE and ABR/ASSR, and real-ear verification against NAL-NL2/DSL targets while treating unmasked cross-hearing, collapsed ear canals, transducer miscalibration, and non-organic loss as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Animal Geneticist & Breeder. Use when a task calls for Animal Geneticist & Breeder judgment. Reasons from additive genetic variance, response to selection (R = i h sigma_A), accuracy, and inbreeding depression through REML/BLUP and ssGBLUP pipelines (BLUPF90, ASReml, WOMBAT), economic selection indices, optimum-contribution mate allocation, and Interbull MACE while treating confounded contemporary groups, prediction bias/dispersion, popular-sire inbreeding and rising deleterious haplotypes, GxE reranking, and pedigree or genotype-calling errors as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Build production realtime voice agents with Hume's Empathic Voice Interface (EVI): speech-to-speech sessions, empathic/prosodic response design, EVI 3 versus EVI 4-mini selection, WebSocket and SDK integration, tools/function calling, interruption/barge-in, turn-taking, transcripts/audio artifacts, pricing/limits, privacy, safety, consent, and QA.

日本語の概要は準備中です。原文の説明を表示しています。

Generate, edit, composite, stream, and production-review still images with OpenAI GPT Image models. Use when an agent must choose between GPT Image 2 and legacy GPT Image/DALL-E integrations, select the Image API or Responses API, build prompts and reference-image workflows, use masks or multiple inputs, preserve identity or brand details, render in-image text, handle output files and partial images, estimate cost and rate limits, migrate deprecated image code, or apply OpenAI image safety, consent, privacy, provenance, and rights requirements. Do not use for OpenAI video or audio generation.

日本語の概要は準備中です。原文の説明を表示しています。

Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).

日本語の概要は準備中です。原文の説明を表示しています。

Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

日本語の概要は準備中です。原文の説明を表示しています。

Run the TMDb integration tests against the live TMDb API (the Integration test plan; requires TMDB_API_KEY/USERNAME/PASSWORD). Use before a PR, or after model/fixture changes, to validate against real API responses — delegates to the tooling-runner agent (Haiku) and returns counts + failures, distinguishing genuine failures from transient live-API/env issues. For mocked unit tests, use /test.

日本語の概要は準備中です。原文の説明を表示しています。

Routes, triages, flags and rates a piece of text with a probability for every option: which department or queue a ticket goes to, which intent a message expresses, whether a yes/no condition holds (is this tool call grounded, should the agent ask first, is the customer angry), and how severe or urgent it is on a rubric, each with a calibrated confidence. Runs Claude Haiku in the request and response shapes of OpenAI's Decisions API, for when no purpose-built decision model is at hand. Use for "route this ticket", "triage these", "classify with a confidence", "give me a probability that", "rate the severity", "score against this rubric", guardrail checks before a tool call, or "a Decisions API without OpenAI".

日本語の概要は準備中です。原文の説明を表示しています。

The secret-source gate. Routed to when changed code reads, writes, or passes a secret — API key, token, password, connection string, signing key, certificate, or any value that grants access. Validates that every secret comes from the approved store and never lands in source, log, test fixture, error response, image, or LLM prompt. The auth-crypto-reviewer agent is dispatched as the reviewer.

日本語の概要は準備中です。原文の説明を表示しています。

Use when auditing a user-facing app — web, mobile (iOS/Android/React Native/Flutter), desktop, CLI, or games — for accessibility barriers or WCAG 2.2 conformance, before shipping UI changes, or in response to concerns about screen-reader, keyboard, low-vision, motor, cognitive, or photosensitive users. Not for general bug hunting or code correctness.

日本語の概要は準備中です。原文の説明を表示しています。

Fit first-order dynamic models to experimental step response data and extract K (gain) and tau (time constant) parameters.

日本語の概要は準備中です。原文の説明を表示しています。

Designs DevOps and platform engineering systems. Use when planning Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, or cloud-native operations.

日本語の概要は準備中です。原文の説明を表示しています。

Responding to incidents: identifies impact scope, formulates recovery procedures, creates postmortems. Use when incident response or disaster recovery is needed. Delegates fixes to Builder.

日本語の概要は準備中です。原文の説明を表示しています。

Design a wise systems intervention from an existing analysis. Maps proposed actions against Meadows' leverage points, checks for unintended consequences, and generates alternatives.

日本語の概要は準備中です。原文の説明を表示しています。

Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.

日本語の概要は準備中です。原文の説明を表示しています。

Generate follow-up teaching moves that extend student thinking after a specific classroom response. Use when a student says something worth exploring and the teacher wants to deepen the dialogue.

日本語の概要は準備中です。原文の説明を表示しています。

Design a document set for a document-based lesson — selecting and sequencing sources for analytical tension around a central question. Use when assembling sources for a new lesson or when an existing set produces flat responses.

日本語の概要は準備中です。原文の説明を表示しています。

Design an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment cycles.

日本語の概要は準備中です。原文の説明を表示しています。

Design a multi-round questioning sequence for interrogating AI chatbot answers, tracking how responses shift and distinguishing genuine updates from sycophantic capitulation. Use when teaching students to probe AI critically.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluate a teacher-drafted central historical question for its capacity to drive genuine historical inquiry. Use when assessing whether a question will generate real evidence-weighing or produce shallow responses.

日本語の概要は準備中です。原文の説明を表示しています。

Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.

日本語の概要は準備中です。原文の説明を表示しています。

Send a Pavlok stimulus via the Pavlok API using scripts/pavlok.py. Use when you need to trigger vibe/beep/zap with a numeric value in this repo and print the API response.

日本語の概要は準備中です。原文の説明を表示しています。