745 件 ・ 関連度順
概要と使いどころ
Records a running CARLA simulation to a .log file via start_recorder/stop_recorder for later deterministic replay — capturing actor spawns, transforms, traffic-light states and animations (optionally velocities/controls). Use when the user asks to "record the simulation", "start/stop the recorder", "capture this run", or "save a scenario to replay later". Does NOT capture sensor images.
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。
End-to-end test the polly multi-agent coding orchestrator's critical user journeys (CUJs). Two halves — a deterministic mock-LLM driver (polly_cuj.py) that boots a throwaway local server + mock LLM and asserts the substrate (boot, bridged sys_* tool dispatch, the blast_radius / spawn_bounds / headless_subagent_purpose_guard guardrails, fan-out delegation), and a live real-CLI recipe (real claude/codex/pi, real worktrees/PRs) for polly's actual judgment. Load when developing, testing, or debugging examples/polly — its config.yaml, the claude_code/codex/pi sub-agents, the investigate/fanout/cross-review skills, or the omnigent.inner.nessie.policies guardrails — or reproducing a polly orchestration bug.
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。
Write or review Apache SeaTunnel E2E (Testcontainers) and unit tests so they are stable, leak-free, and deterministic. Use when creating a new *IT/*Test class, reviewing a test diff, or refactoring a flaky or leaking 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。
Use Ontoly's deterministic Software Graph and MCP capabilities for architecture review, request tracing, dependency analysis, configuration lookup, and impact analysis before falling back to source-file search.
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。
Automation and verification guide for running Native SDK apps. Use when the user asks to test a running app, inspect runtime state, list windows, wait for readiness, drive widgets, take deterministic screenshots, send bridge commands, debug why automation is not connected, create smoke tests, or verify a Native SDK example in a GUI-capable session.
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。
Create and modify Excel workbooks with mandatory deterministic Python verification of workbook structure, required sheets, populated columns, and sample counts before reporting completion.
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。
Edit Excel workbooks only after anchored preflight audit, then require deterministic Python/openpyxl post-write proof that every requested criterion is satisfied or explicitly reconciled before finalizing.
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。
Create or edit Excel workbooks with openpyxl-specific pitfall handling, anchored preflight audit, and deterministic post-write proof that every criterion is satisfied
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 a deterministic Python/openpyxl inspection pass to verify workbook structure, counts, marked rows, and output-file existence when delegated spreadsheet summaries seem inconsistent.
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。
Patches past the barriers that stop a fuzzer making progress — checksum and hash verification, magic-value validation, time-based seeds, and other non-deterministic global state. Covers locating the blocking check, neutering it behind a fuzzing build flag, and avoiding the false positives a patch can introduce. Use when a fuzzer is stuck at validation, when coverage shows large regions behind a checksum, or when valid inputs are impractical to generate.
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 building public-equity DCF valuation workbooks. Default to the banker formula workbook path for new model builds; use deterministic exports only for controlled support calculations or explicit lightweight runs. Do not use for standalone workbook audits; use model-audit-tieout.
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。
Map GWAS loci to ranked candidate genes using a deterministic multi-skill chain (EFO -> GWAS -> coordinates -> Open Targets L2G/coloc -> eQTL -> burden/coding context), with reproducible tables and optional figures. Use when a user provides a trait/EFO term and/or lead variants and needs locus-to-gene prioritization for downstream biology decisions.
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 a bounded real-world procedure has a deterministic happy path but brownfield, variable, or partially knowable state; when an operation must resume from verified evidence; or when deciding whether agent judgment or ordinary code should own a procedure's control loop.
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。
Feature planning and implementation with 4 adaptive phases (Specify, Design, Tasks, Execute). Auto-sizes depth by complexity. Writes testable requirements in EARS notation, atomic tasks, atomic Conventional Commits, and requirement traceability. Ships deterministic Python validation scripts so structural gates are enforced by code, not memory. Features an independent Verifier (author != verifier, evidence-or-zero), a discrimination sensor, a decision log (STATE.md), a test-coverage matrix, and a self-improving lessons layer. Stack-agnostic and tool-agnostic. Use when (1) planning features, (2) implementing with verification and atomic commits, (3) validating an implementation against a spec. Triggers on "specify feature", "discuss feature", "design", "tasks", "implement", "validate", "verify work", "UAT", "record decision", "pause work", "resume work". Do NOT use for pure architecture decomposition analysis or standalone technical design documents.
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。
Give the AI agent its own EVM wallet with admin-controlled policies the agent CANNOT bypass even under prompt injection. Encrypted keystore (AES-256-GCM, scrypt KDF), policy file the agent has no tool to write, deterministic policy gate on every signing operation, optional local HTTP dashboard. Triggers: agent wallet, give the agent a wallet, agent address, fund the agent, agent autonomy, policy gate, kill switch, agent permissions, bounded autonomy, ERC-4337 alternative, session-key alternative.
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。
Create or redesign SaaS and AI product landing pages where a real workflow, interface, or deterministic demo is the central proof. Use for pale atmospheric shells, product UI in the hero, prompt-to-output stories, audience tabs, compact feature modules, honest pricing comparisons, and FAQ handoffs.
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, implement, tune, or test readable tactical action combat for web games. Use for attack timing, guard and dodge windows, hit contact, posture, lock-on, weapons, boss phases, combat feedback, and deterministic combat tests.
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。
Create, integrate, or audit production-ready rigged 3D characters and monsters with a main model, skeleton, animation library, sockets, collision contracts, separate character equipment, optimized runtime files, catalog media, and an interactive moveset inspector. Use when generating or importing FBX/GLB game actors; defining character-versus-monster asset requirements; separating swords, shields, helmets, armor, or stowed gear; adding idle, walk, run, attack, hit, dodge, block, cast, or death clips; wiring an asset into gameplay; or adding action and equipment controls plus deterministic tests to a game asset catalog.
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 a playable browser game end to end with deterministic fixtures and real browser evidence. Use for gameplay QA, regression testing, controls, accessibility, responsive/mobile testing, save flows, console checks, performance smoke tests, and release verification.
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 or refactor reusable, data-driven enemy archetype and moveset systems for Three.js action games. Use for enemy content schemas, model and rig conventions, combat move timing and contact contracts, runtime state boundaries, placeholder fallbacks, deterministic fixtures, or production playthrough validation.
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, debug, balance, or test combat enemy AI for playable action games. Use for aggro, target selection, navigation, spacing, attack choices, telegraphs, retreats, boss behavior, behavior-state machines, and deterministic AI regression tests.
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。
Author or revise readable, flat-world Three.js game levels. Use for movement and camera routes, collision and navigation, encounter zones, landmarks, objectives, pickups, motivated lighting, visibility, deterministic level data, or desktop and mobile playthrough verification.
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, integrate, audit, or refactor rigged monsters for Three.js and web action games. Use for monster asset contracts, procedural or imported creature rigs, semantic joints and sockets, hurtboxes and attack volumes, combat animation states, enemy-runtime adapters, LODs, deterministic review fixtures, and validating that every monster follows one shared system.
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, tune, debug, or validate soft wall-aware fog of war and gameplay perception in Three.js action games. Use for orthographic or isometric visibility masks, obstacle-aware line of sight, player and enemy vision ranges, hidden-enemy targeting rules, fog shader artifacts such as spokes or seams, mobile ray budgets, lifecycle and menu-state integration, and deterministic fog-of-war tests.
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。