Results for “kendall-tau”

Evaluates automated metrics and vision-language models on identifying granular errors in detailed image descriptions and ranking paired descriptions against human judgments, using macro F1, pairwise accuracy, Spearman rank ρ, and Kendall's τ.

Orchestrate token-heavy research, coding, and testing by delegating bounded tasks to cheaper subagents while reserving Claude Fable for architecture, synthesis, and final review.

Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.

Seven-role depersonalised panel review of framework artefacts (KUD, criterion bank, LT definition, crosswalk, scope-and-sequence) in sequential-isolation mode. Gate rule mean>=88 AND no role<70.

選択肢(A/B/C)の深掘り比較→淘汰→推奨で判断負担を下げ判断の質を上げるスキル。5段階（L1固定3点/L1.5案拡張Diverge・自動/L2評価軸マトリクス/L3複数LLM弁証論/L4過去判断照合）。 「比較して」「深掘りして」「メリデメ教えて」「お勧めは？」「徹底的に」「過去の判断と照合」「前にどう決めたっけ」「/sentaku」等で発火。teian(浅)の深掘り要求を受け取り、brainstorming(深:設計全体)と棲み分け。

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics

Train, evaluate, distill, quantize, export, and run inference for RT-DETR object detection models using NVIDIA TAO.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

Documents the warm, editorial design system of Claude.com, including color palette, typography, spacing, and surface modes for consistent brand implementation.

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics

Provides guidance on using Anthropic's Claude API, covering message formats, context windows, tool use, and best practices for building conversational AI applications.

Tags harness-decomposed curriculum items (KUDs, LTs, criteria) with a school's developmental band metadata while preserving source voice and labels. Supply the band schema; the skill derives mapping rules from it.

Hand the current conversation off to a fresh background agent that picks up the work immediately.

Package an in-progress task on this cluster so a Claude agent on another cluster (MMM Young, CX3, HX1, Isambard) can pick it up, or resume a task handed off to this cluster. Use when the user says hand off / migrate / continue this on <cluster>, or asks you to pick up an existing handoff.

Use DuDuClaw — a self-hosted AI-employee platform — for cross-session memory, team-shared wiki knowledge, task boards, and messaging humans over LINE/Telegram/Discord/Slack. Applies when the user mentions DuDuClaw, asks their agent to remember things durably, or wants to reach people on messaging channels from an agent.

Sets up and audits the Claude Code harness for a project: CLAUDE.md, .claude/rules, skills, subagents, settings.json permissions, hooks, verification loop. Commands: kickoff (scaffold new setup), check-playbook (score an existing one), scouting-report (last scorecard), adjust (fix bloat/misplaced instructions), drill (turn advisory prose into real hooks/permissions/CI), decline (record an accepted risk), gameplan (work order with acceptance criteria before building), watch-film (check a diff against that order for scope creep/weakened tests/false claims). Use for setting up Claude Code, or on: "Claude ignores my CLAUDE.md", "it's huge and still misses things", "it said done but ran nothing", "it changed files I didn't ask about", "it weakened a test to pass", "rule, skill, or hook?", "is my setup any good". Not for code quality (code-audit), test coverage (test-assessment), one-off prompt wording (genie-proof-prompts), new skill authoring (skill-creator), or compacting a conversation (handoff).

Auto-routes Claude Code subtasks to the cheapest capable model (Haiku/Sonnet/Opus), logs token costs, and A/B tests tiers to validate savings against real workloads.

Compares coding agents head-to-head on reproducible tasks, measuring pass rate, cost, time, and consistency.

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics

Deliberative consultation with another Claude model via Task tool. Opus consults Sonnet, Sonnet consults Opus. Ask, evaluate, critique, iterate until workable agreement.

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics

Quantifies the minimum accuracy gap needed between two models for a sampled micro-benchmark to reliably preserve their ranking, using the MDAD metric from Yauney et al. (2025).