「snapshot」の検索結果

Best practices for writing R package tests with testthat version 3+. Use this skill when writing or organizing tests for an R package or when improving existing tests that use testthat. It covers test structure and expectations, self-sufficient test design, proper cleanup with withr, fixtures, mocking external dependencies, snapshot testing, and BDD-style describe/it patterns.

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Write `output/DRAFT.md` (or `output/SNAPSHOT.md`) from an approved outline and evidence packs, using only verified citation keys from `citations/ref.bib`.

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Deterministically merge the final fingerprinted section snapshot into `output/DRAFT.md`, preserving outline order and inserting transitions only when explicitly enabled.

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Use when a `research-brief` workspace has a small paper set plus outline and needs a compact reader-facing briefing instead of a full survey.

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Audit one research Workspace for declared Unit outputs and Pipeline target Artifacts, writing `output/CONTRACT_REPORT.md`; use for mid-Run coverage snapshots or final delivery completeness, not deep provenance integrity.

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Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.

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Implements a single feature in the pi-mono `todotools` builtin extension work. Use for refactoring, continuation runtime, config resolver, prompt builder, test authoring, golden snapshots, CHANGELOG entries, and harness helpers. Does NOT do manual tmux QA.

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Inspects videos, extracts frames, and concatenates video clips stored in Volcengine TOS: read video metadata, capture single snapshots, batch-capture multiple frames by explicit timestamps or interval, concatenate multiple video clips, and save results locally or back to TOS. Use this skill when the user needs to get video duration or codec info, capture a poster image or thumbnail, extract frames at specific timestamps, sample frames at regular intervals, merge or join multiple video clips into one — even if they don't explicitly mention 'video processing' or 'frame extraction'.

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Use when a native app, service, or user-mode driver host allocation fails; distinguish VA exhaustion, fragmentation, and commit pressure. Not for managed .NET heap growth, proving a leak from one snapshot, or corruption.

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Direct and produce Chinese-first cinematic motion videos with HyperFrames. Use this skill when the user wants a rendered promo film, article-to-video piece, product launch film, website-to-video piece, keynote reveal, kinetic typography sequence, text/icon transition promo, music-synced motion graphic, HTML/CSS/SVG/GSAP video, short-form vertical video, or a landing page/product story turned into motion. Default to Simplified Chinese, vertical 9:16, and 1080x1920 unless the user or platform clearly requires another format. The skill enforces a two-phase workflow: brief first, confirmation second, then assets, composition, validation, snapshots, render, and review report.

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Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.

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Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

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Turn a game-level brief or existing scene snapshot into a reviewable spatial plan, then apply approved changes with coordinate, navigation and scene checks. Use for layouts, encounter placement and terrain plans.

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Web browser automation with AI-optimized snapshots for claude-flow agents

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Use Playwright CLI for ad hoc browser automation and interactive verification of Core UI: opening local routes, navigating, inspecting snapshots, using element refs, checking console/network activity, capturing evidence, tracing, and reproducing flows. Do not use it as a substitute for committed `@playwright/test` coverage or for non-browser unit tests.

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Build or adapt a local browser/CDP harness to drive and inspect a web, IDE, or Electron UI. Use for local UI verification, screenshots, accessibility snapshots, perf profiles, visual diffs, or reproducing UI bugs.

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Find chats by name, read or search WeChat messages with the local GreenBubbles CLI. Use for bounded live or snapshot queries; route setup problems to greenbubbles-setup and maintained notes to greenbubbles-personal-memory.

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Compare scoped quantity snapshots and explain matches, missing rows and conflicts. Use to reconcile drawing, report or schedule counts; not to extract source documents or update adopted records.

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Web browser automation with AI-optimized snapshots for claude-flow agents

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Provides AWS RDS (Relational Database Service) management patterns using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.

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Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.

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Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL, including Auto CDC from event streams or periodic complete snapshots. Invoke BEFORE starting implementation.

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Bring up a REAL, visible, interactive chromium on the Kali VM that the operator logs into (Smart-ID / Mobile-ID / any manual auth or MFA/CAPTCHA), while the agent drives and observes it live through the chrome-devtools MCP (navigate, DOM snapshot, network capture, screenshots, console, evaluate). Use whenever a target needs a MANUAL login the agent cannot complete headlessly, when you need to capture an authenticated session / the real API calls a page makes, or to confirm/screenshot a DOM-XSS. Triggers - "open a browser", "log in manually", "smart-id / mobile-id / national id login", "mfa / 2fa login", "solve the captcha", "drive the browser", "capture the authenticated session / network".

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60-Second Stock Snapshot — fast assessment with signal, key factors, and levels without launching subagents

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。