「benchmark」の検索結果

Assertion-first AI eval framework aligned to Anthropic's 'Demystifying evals for AI agents' — typed deterministic asserts + a forced-structured LLM judge over an input→assert case schema, pass^k/pass@k, capability vs regression suites, subscription-billed. USE WHEN eval, evaluate, benchmark, regression test, assertion, assert, llm-rubric, judge, pass@k, pass^k, grade output, compare prompts/models, test agent. NOT FOR scientific-method framing (use Science), property/mutation testing of code (use Hardening), or live UI verification (use Interceptor).

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This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.

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Workflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says \"robotics idea discovery\", \"机器人找idea\", \"embodied AI idea\", \"机器人方向探索\", \"sim2real 选题\", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.

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Profile a target (script, process, GPU, memory, interconnect) for performance analysis. Use when user says "profile", "benchmark", "bottleneck", or wants performance analysis.

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。

Workflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says "robotics idea discovery", "机器人找idea", "embodied AI idea", "机器人方向探索", "sim2real 选题", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.

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。

MNN OpenCL 后端 kernel 性能优化与新特性集成。覆盖 benchmark 基线、kernel 优化迭代、集成验证全流程，以及新 OpenCL 特性适配、.cl + codegen 双轨、kernel 选路、packed weight 设计、tune 机制、Android 真机验证等参考知识。

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。

MNN Vulkan 后端 kernel/算子性能优化与新特性集成。覆盖 benchmark 基线、kernel 优化迭代、集成验证全流程，以及 GLSL .comp + makeshader 双轨、conv1x1/attention dispatcher 多路径（coopMat/subgroup/nosubgroup）、cooperative matrix、packed weight 设计、pipeline cache、CPU 侧调度瓶颈、Adreno/Mali/Apple 多 vendor 真机验证等参考知识。

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Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).

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。

Implements concurrent Go patterns using goroutines and channels, designs and builds microservices with gRPC or REST, optimizes Go application performance with pprof, and enforces idiomatic Go with generics, interfaces, and robust error handling. Use when building Go applications requiring concurrent programming, microservices architecture, or high-performance systems. Invoke for goroutines, channels, Go generics, gRPC integration, CLI tools, benchmarks, or table-driven testing.

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Refresh Claude Ads platform, API, policy, regulation, benchmark, issue, pull-request, fork, and repository evidence. Use for ads research refresh, expired refresh_due dates, stale API or platform claims, reverify-or-demote decisions, release-current claim validation, ecosystem review, current platform changes, or updating paid-media knowledge. When tools or sources are unavailable, still demote the stale claim for the current run and block dependent release-current assertions before requesting access.

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Writes, reviews, and debugs property-based tests — Hypothesis, fast-check, proptest, jqwik, rapid, and Echidna or Medusa for Solidity invariants. Use whenever tests should cover a whole input domain instead of a hand-picked list of examples: encode/decode and serialize/deserialize pairs, parsers, canonicalizers and normalizers, validators, numeric and Decimal types, comparators and sort order, data structures, and smart-contract state invariants. Also use when adding cases to an existing @given, fast-check, or proptest suite, when judging whether existing property tests assert anything real, and when a generator has shrunk a counterexample and you need to tell a wrong property from a genuine bug. Not for coverage-guided binary fuzzing (libFuzzer, AFL), mutation-testing campaigns, static analysis, benchmarking, or end-to-end UI 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。

Evaluate a local Codex skill in engineer-friendly terms. Use when the user says "evaluate this skill", "give me an analysis of the game dev skill", "audit this skill", "why did this score that way", "what should I fix first", or asks for a skill-specific report before benchmarking it.

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 local Codex plugin in engineer-friendly language. Use when the user says "evaluate this plugin", "audit this plugin", "why did this score that way", "what should I fix first", "help me benchmark this plugin", or asks for a plugin-wide report before comparing versions.

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。

Help engineers evaluate a local skill or plugin, explain why it scored that way, show what to fix first, measure real token usage, benchmark starter scenarios, or decide what to run next. Use when the user says things like "evaluate this skill", "give me an analysis of the game dev skill", "why did this score that way", "what should I fix first", "measure the real token usage of this skill", or "what should I run next?".

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。

HyperFrames CLI tool — hyperframes init, lint, inspect, preview, render, transcribe, tts, doctor, browser, info, upgrade, compositions, docs, benchmark. Use when scaffolding a project, linting, validating, inspecting visual layout in compositions, previewing in the studio, rendering to video, transcribing audio, generating TTS, or troubleshooting the HyperFrames environment.

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。

Scores how completely an implementation fulfills a PRD/spec, case by case, and produces a single comparable final grade. Invoke only when explicitly named (e.g. run spec-driven-eval); do not auto-trigger. Use when benchmarking spec-driven implementations, grading acceptance criteria, evaluating whether a feature was 100% implemented, comparing multiple implementations of the same PRD, or auditing implementation and test coverage (unit and e2e) against product requirements. Do NOT use for planning or building features (use tlc-spec-driven), writing PRDs, or general code review unrelated to a spec.

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。

Apply a prompt-agnostic execution and verification loop to any substantial task while preserving the original request. Use when the user asks to fan out work, use subagents or independent reviewers, loop until done, benchmark against references, apply a harsh critic, compare candidates blind, improve an existing prompt with verification, or continue until explicit quality gates pass.

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 DeepChat's bundled CLI control plane for model inference, image/video/speech generation, transcription, OCR, artifact inspection, public configuration, Skills, and MCP operations. Activate when a user asks to invoke DeepChat capabilities that are not already exposed as a more specific tool, compare models, run a benchmark, inspect DeepChat runtime state, or manage DeepChat through the CLI.

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 and repair LoopX control-plane drift or agent behavior drift. Use when a LoopX task makes unexpectedly small progress, follows a stale or contradictory recommended_action, ignores a higher-priority blocked item while doing fallback work, reports vague owner/user gates, loses todo projection, misaligns benchmark treatment with the real product path, mixes temporary artifacts into commits, or when the user asks for root-cause analysis, self-repair, or why the harness/agent behaved unexpectedly.

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Use when a connected LoopX project is asked to read, remember, record, index, register, or use a durable project material such as a Lark/wiki/design doc, research note, SOP, owner packet, migration report, benchmark paper, or external material source. Use even when the user does not mention LoopX or doc registry.

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。

Compare baseline and current Pinot JMH benchmark results using an isolated baseline worktree.

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。

Review Apache Pinot performance when hot paths change or a PR makes benchmark or efficiency claims.

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。

This skill helps users extract YouTube video transcripts and perform deep competitive analysis on the content. Agent should proactively apply this skill when users express needs like analyze YouTube video content strategy, perform competitive video content analysis, extract and analyze YouTube subtitles for marketing insights, understand competitor value propositions from their videos, identify target audience from YouTube video content, analyze pain points and needs mentioned in YouTube videos, evaluate competitor CTA strategies in video content, find content gaps in competitor YouTube videos, analyze video narrative structure and hooks, extract key messaging and positioning from YouTube content, benchmark competitor video content quality, research competitor marketing angles through video analysis, identify audience signals and terminology level in videos, analyze emotional tone and persuasion techniques in YouTube content.

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Etsy category page scraper: given an Etsy category URL (e.g. https://www.etsy.com/c/jewelry) and optional page number, returns paginated product listings with listingId, shopId, title, url, image, salePrice, originalPrice, currency, rating, reviewCount, shopName, isAd, freeShipping, badge from category and subcategory pages. Use when user mentions Etsy category, Etsy c/ URL, etsy.com category, browse Etsy categories, scrape Etsy category, Etsy category listing, extract Etsy category products, Etsy subcategory, Etsy jewelry, Etsy clothing, Etsy home decor, Etsy wedding, Etsy accessories, Etsy craft supplies, Etsy vintage category, Etsy category page scraper, Etsy taxonomy, get all products from an Etsy category, bulk category export from Etsy, Etsy niche scraping. Also applies to trend research within a specific Etsy category, competitor benchmarking within a category, sourcing suppliers by category, seasonal product monitoring, and paginated bulk collection driven by a category URL.

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。