「utc」の検索結果

Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.

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Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.

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Prepare a proportionate work plan with outcomes, steps, dependencies and acceptance evidence. Use to plan substantial work or coordinate an approved team; a plan-only request does not authorize implementation.

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When the user needs to organize product initiatives into a prioritized, time-sequenced plan with outcomes and dependencies.

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Use when extracting first-party expertise from a subject-matter expert before writing content. Produces a knowledge document of contrarian takes, specific examples, and surprising outcomes that AI can't fabricate.

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Use before running any confirmatory analysis or looking at outcome data, when testing a hypothesis, computing a p-value, or about to claim an effect - locks predictions and decision rules before results are seen

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Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any model

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You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched

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Forecast project outcomes using historical data: cost overruns, schedule delays, risk probabilities. Machine learning models for construction prediction.

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Answers business questions about the user's own spreadsheet or data export (CSV, TSV, XLSX from a CRM, Shopify, Stripe, QuickBooks, ad platforms, HR or payroll systems) correctly and auditably. Profiles every file first (grain, duplicate keys, embedded Grand Total rows, text-stored numbers, mixed currencies, UTC timestamps, Excel serial dates, hidden rows), states the metric definition used, computes every number in SQL or pandas instead of mental math, checks joins for fan-out, reconciles to a known total, and returns the query, filters, row counts, and rows behind the answer. Use this whenever the user says "analyze this spreadsheet", "how many...", "what's the total in this export", "which customers...", "pivot this", "revenue by month", "top 10", "what's our churn/MRR/AOV", or asks any question about their data, CSV, or Excel file, even a quick one. Also use when they ask to change values in a data file.

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Enhanced skill navigator that maps conversation history, recommends multi-skill chains, identifies patterns from past usage, and learns from session outcomes. Goes beyond basic scout with deep context analysis and workflow orchestration.

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Use when comparing an exposure-outcome association across countries with parallel national surveys (KNHANES, NHANES, CHNS). Harmonizes variables, runs parallel weighted analyses and builds comparison tables for 2-country (KR+US) or 3-country (KR+US+CN) designs.

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Use when one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.

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Use when exposure, outcome, covariate or eligibility definitions and cutoffs need a citable basis before the protocol. Reads the data dictionary first, then maps each variable to a guideline or published definition and the database columns in a citation-backed table.

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XML-specified Android UI journey testing, interactive step execution, assertion verification, and JSON outcome reporting.

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Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.

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Triage acc's open promises and close them with honest real-world verdicts via acc_act(runtime="outcome").

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This skill should be used when the user asks to "create a plot arc", "story structure", "add a plot point", "story timeline", "track foreshadowing", "pacing", "sagging middle", "act structure", "story arc", "plot outline", "snowflake method", or wants to plan and manage the narrative structure of a story. It owns book-level pacing; NOT for the craft of scene outcomes or chapter hooks, which this skill only plans in the outline (use scene-craft), or a pacing check as a revision pass (use revision-continuity).

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This skill should be used when the user asks to "plan a scene", "scene structure", "sequel scene", "dialogue subtext", "deep POV", "psychic distance", "try fail", "scene cards", "exposition", "info dump", "flashback", "time skip", "story opening", "first page hook", "introduce a character", "scene outcome", "yes-but no-and", "chapter hook", or wants scene-level craft for drafting or revision. NOT for book-level pacing or act structure (use plot-structure), or drafting the prose of a scene or chapter (use chapter-writing).

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This skill should be used when the user asks to "write a chapter", "next chapter", "chapter outline", "draft chapter", "continue the story", "write a scene", "outline a chapter", or wants to write prose for a story project. NOT for planning a scene's structure, subtext, or outcome (use scene-craft), drafting without an outline (use discovery-drafting), or revising existing chapters (use revision-continuity).

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Guides the Art. 36 prior consultation process when a DPIA indicates high residual risk that cannot be mitigated. Covers required documentation per Art. 36(3), the 8-week DPA response timeline, outcome management, and interaction protocols with supervisory authorities. Keywords: prior consultation, Art. 36, supervisory authority, DPA, high residual risk, DPIA escalation, consultation documentation.

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Guides the GDPR Article 36 prior consultation process with supervisory authorities when a DPIA indicates high residual risk. Covers timeline requirements, documentation, and outcome handling. Activate when DPIA residual risk remains high or when preparing regulatory submissions. Keywords: prior consultation, Article 36, DPIA, high risk, supervisory authority.

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Review whether a repository's coding-agent harness can reliably carry work from intent through controlled execution, verification, delivery, and learning. Use when asked to assess agent readiness, repeated agent failures, Rules/Skills/Hooks/Memory effectiveness, missing validation or recovery loops, or whether a harness repair improved later outcomes. Do not use for code-only audits, AGENTS-only audits, individual skill reliability reviews, or executing the task itself.

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Use when planning with fixed deadline or target outcome, working backward from future goal to present, defining milestones and dependencies, mapping critical path, identifying what must happen when, planning product launches with hard dates, multi-year strategic roadmaps, event planning, transformation initiatives, or when user mentions "backcast", "work backward from", "reverse planning", "we need to launch by", "target date is", or "what needs to happen to reach".

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。