「replay」の検索結果

Lock an ideation run into a single-source-of-truth brainstorm brief (`output/trace/IDEA_BRIEF.md`) and a replayable multi-query plan (`queries.md`).

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Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

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Use only for MSBuild failure investigation, build-performance analysis, or review of existing build XML/extension contracts. A plan to convert a working legacy/non-SDK project to SDK style or migrate packages.config is a project-system migration, NOT a build-file review: do not activate, even to check scope. Framework/API upgrades are also excluded. Activate for advisory Import/Exists hook and NuGet packed-import questions even without a checkout or failed build; .csproj/.fsproj/.vbproj/.props/.targets conditions; custom-target inputs/outputs; .binlog capture/replay; failing dotnet build/restore/pack/publish or MSBuild-based test builds; slow evaluation, compilation, copying, or no-change builds; and controlled timing comparisons. Exclude SDK installation alone, C# refactoring, runtime profiling, assertions after successful builds, and non-MSBuild builds.

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Use when an app, service, or user-mode driver host TTD recording is available and earlier calls, writes, or lifetimes matter. Not for kernel replay or history from a normal dump.

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Use when the target has a non-Wi-Fi radio attack surface — Bluetooth/BLE (GATT, pairing, KNOB/BIAS/BleedingTooth), Zigbee/Thread/Matter & Z-Wave mesh (Touchlink, S0 downgrade), LoRaWAN/Sub-GHz LPWAN, SDR capture/replay/rolljam, smart locks/medical/IoT radios. For Wi-Fi/WPA/evil-twin see network-attack.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Testing strategies, patterns, and tools for robotics software. Use this skill when writing unit tests, integration tests, simulation tests, or hardware-in-the-loop tests for robot systems. Trigger whenever the user mentions testing ROS nodes, pytest with ROS, launch_testing, simulation testing, CI/CD for robotics, test fixtures for sensors, mock hardware, deterministic replay, regression testing for robot behaviors, or validating perception/planning/control pipelines. Also covers property-based testing for kinematics, fuzz testing for message handlers, and golden-file testing for trajectories.

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。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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 Diffusion Policy for robot imitation-learning configs, zarr replay data, policy/model interfaces, training/evaluation workflows, and safety-gated real robot 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。

Use DreamerV2 2.2.0 for TensorFlow world-model reinforcement-learning training, custom Gym integration, typed experiment configuration, replay/checkpoint management, and JSONL/TensorBoard evaluation.

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 BoxMOT for multi-object tracking, benchmark replay, ReID model lifecycle workflows, and native C++ tracker backends.

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。

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

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。

Drive Burp Suite over its MCP server as an AI triage + attack layer - review proxy history for signals, replay via Repeater/send, OOB-gate blind bugs with Collaborator, fuzz via Intruder (RoE-safe), then hand off to the matching vuln-class hunt. Wiki-first, FIND schema output.

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Capture a Burp Suite Repeater request/response as a PoC image (targets/<eng>/poc/) by driving the Burp MCP + the Kali GUI. Replays a request in a Repeater tab, sends it, and grabs the request+response panes - a Burp-native PoC (client report / CTF writeup). Use when you want the evidence to come from Burp rather than a curl/terminal card, or whenever you drive a target through Burp and need the images. Pairs with hunt-burp.

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Turn long videos & livestream VODs into viral vertical shorts, 100% locally — on-device transcription, LLM highlight detection, 9:16 reframe with karaoke captions, and a per-clip render-QA report. Use when the user asks to clip / cut / 切片 / 剪 a long video, podcast or stream replay into short clips, find highlights / 爆点 in a video, or transcribe a media file. Footage never leaves the machine.

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Start and carry an explicitly invoked openai-guardrails-python implementation through a fresh isolated worktree and a local PR-ready handoff. Fetch the latest origin/main, keep task changes uncommitted, replay them onto the latest main before final review, run applicable verification and $implementation-final-review, use $pr-draft-summary to generate the complete PR draft and branch name, then create one clean local commit with takeover provenance when applicable. Use only when the user explicitly invokes this skill; never push, open a PR, or mutate GitHub.

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Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.

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How TestDriver learns your app and caches what it discovers for instant, deterministic replays

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。

Diagnose failing tests with screenshots, replays, and logs

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Use when implementing or modifying behavior that must stay consistent across TUI and Web (mode/input/tool/replay/order). Require canonical semantics first, then TUI/Web adapters, then renderer-specific UI.

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Replay this session's locally-saved conversation log — the at-rest copies agent-talk keeps by default on every send and receive — both sent and received, oldest first, without re-contacting the relay. Use to review the conversation with a peer. `<user>` is this session's user directory (absolute path; from init).

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Decision framework for isolating every external dependency in a test suite: when to use in-process mocks, HTTP stubs (MSW, WireMock), record-replay, fault injection (Toxiproxy), or ephemeral real services (Testcontainers), and how to enforce that no real calls escape in CI. Use when: "mock service," "stub API," "fake service," "WireMock," "MSW," "Toxiproxy," "test isolation," "dependency management," "stub an external API in CI." Not for: consumer-driven contract verification (Pact, broker) — use contract-testing; standing up a full Docker Compose env or seed data — use test-environments; broad resilience/game-day fault campaigns — use chaos-engineering. Related: contract-testing, test-environments, api-testing, test-data-management, chaos-engineering.

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Event sourcing and CQRS expert for AI memory systemsUse when "event sourcing, event store, cqrs, nats jetstream, kafka events, event projection, replay events, event schema, event-sourcing, cqrs, nats, kafka, projections, event-driven, memory-architecture, ml-memory" mentioned.

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。

Expert in designing and implementing visceral, satisfying combat systems. Masters hitbox/hurtbox design, frame data, combo systems, enemy archetypes, damage feedback, and the invisible craft that makes players feel powerful. Draws from fighting games, character action games (DMC, Bayonetta), and Souls-like design to create combat that is readable, responsive, and endlessly replayable. Use when "combat system, combat design, hitbox, hurtbox, frame data, hitstop, screen shake, input buffer, coyote time, i-frames, invincibility frames, combo system, attack cancel, recovery frames, punishment window, souls-like combat, action game combat, fighting game, damage feedback, enemy design, boss design, attack telegraph, parry system, stamina system, poise system, weapon feel, game feel combat, melee combat, combat juice, combat, action-game, fighting-game, hitbox, frame-data, game-feel, souls-like, character-action, melee, enemy-design, boss-design, combo, parry, i-frames" mentioned.

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。