「rubric」の検索結果

T+N 天数据回收 + 复盘 + 把实绩观察写入 rubric-memo.md。这是校准循环的反馈环节——不复盘的预测等于占星。触发词："复盘 [path]"/"retro this"/"T+3d 数据来了"/"抓数据 [path]"/"把这篇复盘了"。

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。

给单篇稿子打 rubric 分。**只在控制台输出，不写文件，不预测**。触发词："打分这篇 [path]"/"score this [path]"/"给这稿子打分"/"先打分看看"。是 cheat-predict 之前的轻量探索动作。

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。

查看 cheat-on-content 内容校准工作流的状态、rubric、待复盘与选题池。仅在明确提到该内容工作流或其状态文件时使用；不处理代码构建、项目进度或通用“状态”查询。

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。

用打分、盲预测、发布后复盘和 rubric 进化校准内容判断。适用于视频、文章、播客等可量化内容；触发词包括初始化、打分、预测、发布、复盘、推荐选题、抓热点、状态、找对标。首次使用先运行 cheat-init。

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。

Solves complex, open-ended problems using an evolutionary search mechanism inspired by genetic algorithms. Generates a diverse population of candidate answers, scores them against a rubric, then iteratively applies selection, crossover, and mutation to breed progressively better solutions. Use this skill whenever the user wants to explore a solution space deeply, asks for the "best" answer to a subjective or multi-dimensional problem, wants to evaluate competing approaches and distill the strongest, or uses any of these triggers: "evolutionary search", "genetic algorithm", "evolve an answer", "breed solutions", "population-based search", or phrases like "give me multiple approaches and refine the best one". Also trigger when the problem is complex, open-ended, or has no obvious single correct answer and the user wants a high-quality, well-explored result, not just a quick response.

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。

SKILL.mdを評価するとき、新規Skillを設計するときに読む。

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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 standardized metrics across multiple ego-benchmark-harness runs by recalculating them from tasks.jsonl and session JSONL, never from HTML reports. Covers overall aggregates (success rate/average score), Odysseys per-rubric pass rates, duration distributions (avg/p50/p90/max), turns/tools/cost, error and exception counts, per-task success matrices, helper distributions, and the share of DOM operations. Use whenever users ask to compare data or metrics across runs, calculate aggregate/Odysseys/duration/turn/error/helper metrics, inspect differences among runs, or compare multiple rwb/real-world-bench runs. Use pi-session-analyzer instead for deep forensic analysis of why an individual task was slow or failed.

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。

Prepares a discovery call brief from what the user has on a prospect (CRM record, inbound form, emails, notes, public material the user supplies): what is known with sources, gaps against the qualification dimensions, hypotheses to test, questions in priority order and what to listen for. Returns a DRAFT Markdown brief in the chat. Use when the user asks to "prep me for the discovery call with <prospect>", "what should I ask on this first call", "intro call brief", "questions for the qualification call" or "what do we know before we speak to them". Do not use for scoring leads against a rubric, use lead-qualification-scorer instead; for an existing customer's account review use account-plan-builder. Drafts for human review; never approves, authorises or signs off.

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。

Reviews one deal's notes and timeline (CRM opportunity record, activity log, emails, meeting notes, forecast entries) for risk signals such as a single contact thread, no identified budget owner, slipped or missing dates, silence after a proposal, an undefined decision process or a competitor named late, and returns a DRAFT risk register: each signal with the quoted evidence and its date, a severity on a stated scale, and one proposed next action with an owner role for the user to decide on. Use when the user asks to "review the risks on this deal", "why is this opportunity stalling", "is this deal single-threaded", "deal health check before the forecast call" or "what should we do next on <opportunity>". Do not use for a whole-account plan, use account-plan-builder instead; for scoring early leads against a rubric use lead-qualification-scorer. Drafts for human review; never approves, authorises or signs off.

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 a DRAFT structured interview scorecard from a job description: five to eight competencies traced to it, past-behaviour and situational evidence questions with probes, four-level rating anchors written as observable behaviours, a blank scoring sheet, a panel plan and the items the hiring manager must confirm. Use when the user asks to "build an interview scorecard for this job description", "write competency questions for this role", "draft the interview guide for the panel", "set up a rating scale for the interviewers", "put together a structured interview kit" or "create a hiring rubric from the JD". Do not use for writing or fixing the job description itself, use job-description-drafter instead; for the new hire's first 90 days, use onboarding-plan-builder. Drafts for human review; never approves, authorises or signs off.

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 as an independent design-review pass after rendering a website; evaluates macro, meso, micro craft, applies the Design Quality rubric, identifies root causes, and prioritizes changes without defending the implementation intent.

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。

Compile architecture draft into dated architecture design and engineering standards with rubric-complete quality gates

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。

Orchestrate ambiguity auditing across selected layers with rubric-driven scoring, remediation flow, and next-step gating

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。

Validate ideation completeness through constraints, exhaustion, rubric checks, and output compilation

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。

Execute ambiguity audit document-by-document with implementer simulation, rubric scoring, cross-layer checks, remediation, and verdicting

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 should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise compari

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 automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.

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 improve AI-generated output with explicit rubrics, reflection loops, and stop conditions. Use when building self-critique workflows, evaluator-optimizer pipelines, or acceptance gates for code, docs, analysis, or plans.

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 should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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。

Scaffolds eval.yaml test files for agent skills in the dotnet/skills repository. Use when creating skill tests, writing evaluation scenarios, defining assertions and rubrics, or setting up test fixture files. Handles eval.yaml generation, fixture organization, and overfitting avoidance. Do not use for running or debugging existing tests nor for skills authoring.

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。

Shared foundation for all human-voice writing skills. Load before any profession-specific skill. Contains: burstiness principle, perplexity principle, soul injection techniques, community-discovered AI patterns with sources, additional core patterns, banned AI vocabulary, and scoring rubric.

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 should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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。

Busabase App-in-Skill review queue that scores candidate SME financing deals (revenue-based/RBF-style credit) with a deterministic, fully auditable rule-based rubric — never an LLM or API call. Use when the user invokes $kelly-deal-scorer or /kelly-deal-scorer, wants to review a deal-underwriting queue, score financing candidates, compute a composite score breakdown, see a suggested revenue-share rate range, or record approve/send-back/reject decisions for a private-credit or RBF-style lending pipeline.

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。