「score」の検索結果

言語モデルに指示への応答や好ましい回答を学習させるため、TRLによる追加学習を案内するスキル。データ準備、学習方式の選択、報酬モデルの学習や評価を扱います。

• 回答例で指示に従うモデルを学習
• 回答の好みをDPOで学習したいとき
• 報酬モデルを使うRLHFの構築

プロンプト、正規表現、SQL、小さなコードを、採点基準に沿って繰り返し改良します。AIが候補を作り、失敗例を手がかりによりよい案を探すスキルです。

• 正解例に沿ってプロンプトを改善したいとき
• 合格率で正規表現を調整したいとき
• テストでSQLやコードを改善したいとき

Show ponytail measured impact as a scoreboard: less code, less cost, more speed, from the agentic benchmark averages. One-shot display.

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。

Read-only review of Caveman Cloud evidence: cost, Cave Score, workflows, traces, latency, errors, routing, savings. Use when asked what Caveman found or where LLM spend goes.

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。

Five-dimension design quality review — score the artifact against craft, brand, accessibility, and copy, then fix what falls short before handing it over.

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-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score 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。

Retrieve probabilistic forecasts and their scorecard context. Use when the user asks what World Monitor is forecasting, how probabilities shifted, or how calibrated the forecasts are.

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。

Retrieve recent earthquakes (USGS) with magnitude, depth, location, and a concern score that flags proximity to nuclear test sites. Use when the user asks about recent seismic activity or whether an earthquake was natural.

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 run evaluation suites, watch live benchmark progress, view scorecards, compare model performance, and integrate eval runs with CI workflows from 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。

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。

Extract MIDI/score from an existing production

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。

Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction

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。

Inspect and audit GEPA genomes via the `@metaharness/darwin/gepa` library entry (darwin 0.8.0) — load/validate a genome (default is the shipped cand-6 promotion), render the system prompt a genome compiles to, or classify failure modes in a run transcript. The `gepaOptimize` loop itself is library-only (bring your own evaluator) and not surfaced here — use `harness-evolve` for sandbox-scored evolution. Degrades gracefully when @metaharness/darwin is absent.

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。

7-section repo readiness report from `metaharness genome {path}`. Returns repo_type / agent_topology / risk_score / mcp_surface / test_confidence / publish_readiness. Pure-read; degrades gracefully (ADR-150).

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。

Manage `@metaharness/darwin` bench suites — `bench create {repo}` scaffolds a JSON suite from a repo's test corpus; `bench verify {suite.json}` checks suite well-formedness. Bench suites are the fixed evaluation corpora that `harness-evolve --bench {suite.json}` scores variants against, decoupling evolution from the repo's natural tests. Degrades gracefully when @metaharness/darwin is absent.

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。

MAD-based outlier detection on session spend. Robust to the very outliers it hunts (unlike mean+sigma). Surfaces specific anomalous sessions with modified-z scores; optional --alert-on-outliers exit code for CI gates. Distinct from cost-burn (aggregate trend) — this answers "which INDIVIDUAL session is the outlier?".

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-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score 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。

Use when reviewing or improving a Front-End Checklist rule MDX file to raise its quality score, fix stub prompts, add missing fields, or enrich content with real code examples.

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 auditing URL structures for word separator format, generating slugs from titles, or planning a URL migration from underscores or spaces to hyphens with appropriate redirects.

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。

Drives a real browser through the omowright library from the js eval kernel: sites the user is already signed into, forms and clicks, JS-rendered pages, screenshots, web QA, extension popups, a human handoff for login, CAPTCHA or OTP, and a browser you own for scraping, bot-scored targets, network capture and QA traces. Use for any interactive browser task; not for a plain search or an unblocked static fetch.

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。

Reaches web pages the user's browser cannot open: WAF-blocked and bot-scored hosts, platform-native readers (Chinese and social platforms), archive snapshots with provenance, and a browser you own for stealth or throwaway profiles. Not for plain search, an unblocked static fetch, or a page the user's browser can open.

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 this for a dashboard, metrics page, KPI tracker, scorecard, data visualization or recurring report: a page or scroll story of charts, numbers and tables of data, whatever the data and wherever it comes from. Read it when creating, filling, refreshing or revising one. You write datasets (a live query on a connector, or an attached CSV/TSV/JSON file), then the page as plain HTML, in the dashboard's own store.

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-native tutor and onboarding workflow for the four independent Claude certification tracks in AI Engineering from Scratch. Use when a learner wants to choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path, learn the next lesson interactively, run and verify practical labs, build scored artifacts, take a diagnostic or mock exam, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.

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。

Score how well organized the MemPalace palace is, then run a guided repair session — one decision at a time.

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。