「evaluate」の検索結果

AIエージェントの成果物を、正確さ・網羅性・明瞭さ・実行しやすさ・簡潔さの5軸で自己評価し、根拠付きの点数と優先順位を付けた改善案をまとめます。

• 複数ファイルの実装後の自己評価
• 複数工程を終えた成果物の点検
• 設計文書や分析レポートの評価

大規模言語モデルの知識・推論・コード生成を共通の課題で評価するスキル。複数モデルの比較表や学習途中の成績推移を作り、評価結果と個別の回答を保存します。

• MMLU・GSM8Kでのモデル評価
• 複数モデルの比較表作成
• 学習途中のモデルの成績追跡

重みが公開されたLLMの回答拒否に関わる仕組みを分析し、再学習なしで重みを変更する実験を支援。計算環境に合うモデルや手法の選択、変更前後の品質評価まで扱います。

• LLMの回答拒否の仕組みを調べたいとき
• 再学習せず重み変更を実験したいとき
• 計算環境に合うモデルと手法を選びたいとき

コード実装や不具合修正、PRレビューをBlackbox AIに委任します。複数モデルの結果を評価して選び、長時間の作業の監視や保存地点からの再開も扱うスキル。

• 機能実装や不具合修正の委任
• PRの不具合とセキュリティ確認
• 複数AIモデルの実装比較

目標画像と実装した3Dシーンのスクリーンショットを比較し、構図・照明・質感を段階的に改善します。見た目の再現度を採点し、描画速度も確認しながら修正を重ねるスキルです。

• 目標画像に近い3Dシーンを作りたいとき
• ブラウザゲームの照明や質感を磨きたいとき
• 既存の3D作品の見た目を改善したいとき

Use when asked to brainstorm, evaluate whether an idea is worth building, run office hours, or think through a new product idea or design direction before any code is written.

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。

QA-test a website or web app and return a 1-5 quality score (5 = flawless, 1 = broken) with evidence. Use when the user wants to test, QA, evaluate, score, or "check how good" a site, page, flow, or app — including a local dev server (e.g. "qa test localhost:5173", "does the checkout work?", "rate this landing page"). Drives a real Browser Use cloud browser, tunneling localhost automatically.

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。

Turn a Caveman optimization observation into an operator-chosen candidate with a paired baseline evaluation. Use when asked to inspect or evaluate a Caveman optimization report. Needs explicit approval.

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。

Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.

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 PR's title and description against its actual implementation, then iteratively suggest and apply updates. Use when the user asks to check, fix, or update a PR title or description.

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 and carry out non-trivial software-system changes from first principles. Use when assessing RFCs, issues, designs, features, refactors, migrations, dependency changes, or proposed fields, events, APIs, modules, and services whose need, consumers, system fit, validation, or rollback require scrutiny. Read the actual system, identify the concrete problem and named semantic consumers, choose the smallest sufficient solution, reject pseudo-requirements and speculative abstractions, and require evidence proportional to risk. Do not use for mechanical edits, source-code explanation, or a dedicated review of an already-complete diff.

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。

Search, score, scan, and import agent skills from GitHub repositories that contain SKILL.md, CLAUDE.md, .cursorrules, and similar agent skill files. Discover community skills across many tool and provider categories, evaluate relevance with heuristic scoring, check for malware or hardcoded secrets, and install into Hermes, Claude Code, Gemini CLI, or OpenCode agent directories.

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 reviewing sign-in, sign-up, MFA, CAPTCHA, recovery, and re-auth flows. Evaluate the full authentication path, including error handling and backup methods, not just the primary login form.

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。

AI job search command center -- evaluate offers, generate CVs, scan portals, track applications. Use when the user pastes a job URL or JD, asks to scan portals, generate a CV/PDF, track applications, prepare for interviews, draft outreach/emails, or run any career-ops mode.

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 for any product or shopping question: find, reverse image search, shopping Instagram/Marketplace links, buy, compare, or evaluate real products with prices, images, and product page URLs, including buying or browsing Facebook Marketplace listings. Use when presenting shopping search results from any source. For shopping intent, load this skill first before any other skills.

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 an Agent Skill bundle for structural integrity, trigger quality, artifact improvement, script correctness, safety, installed-tree integrity, and target-host portability before release.

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。

Focused interactive tutor for the Agent Skills Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to create, discover, invoke, secure, evaluate, package, or port Agent Skills. Teaches one lesson per invocation and records evidence in AGENT-SKILLS-LEARNING.md.

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。

When the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," "free resource," "ROI calculator," "grader tool," "audit tool," "should I build a free tool," or "tools for lead gen." Use this whenever someone wants to build something useful and give it away to attract leads or earn links. For downloadable content lead magnets (ebooks, checklists, templates), see lead-magnets.

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。

Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.

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。

Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.

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。

Builds, evaluates, and audits right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.

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。

Evaluates scientific claims and evidence quality. Applies to experimental design validity, biases and confounders, statistical interpretation, evidence grading frameworks (GRADE, Cochrane Risk of Bias), and teaching critical analysis. Supports evidence appraisal and identifying flaws; formal peer review writing belongs to peer-review.

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。

Discovers and evaluates scientific datasets, models, methodology posts, and Spaces through the Hugging Science catalog. Used when selecting scientific ML resources in biology, chemistry, genomics, materials, climate, physics, astronomy, medicine, mathematics, protein design, single-cell analysis, or PDE modeling, and when checking their actual datasets, Transformers, native-runtime, Inference Providers, or Gradio interfaces.

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。

Runs Cantera homogeneous chemical reactors and evaluates ignition delay with mechanism provenance, conservation checks, and numerical refinement. Use for combustion kinetics, closed adiabatic ideal-gas constant-volume or constant-pressure ignition, temperature histories, or mechanism-specific ignition-delay comparisons.

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。