「score」の検索結果

Outbound SDR (Sales Development Representative) operating system that runs the full top-of-funnel motion across HubSpot (CRM, engagement, and source of truth) and Exa (web research + signals). Use this skill WHENEVER the user is doing outbound sales development, building target/prospect lists, defining or refining an ICP, enriching leads, researching accounts or people before outreach, writing cold emails or multi-touch sequences, qualifying or scoring leads, logging activity to CRM, handing meetings off to AEs, or reporting on pipeline. Trigger it even when the user only says things like "find me 50 leads", "research this account", "write a cold email to this person", "score these leads", "log this in HubSpot", or "build a sequence"; these are all SDR tasks this skill governs. Do not wait for the user to say "SDR" or "prospecting" explicitly.

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Codified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships. Informed by transportation managers with 15+ years experience. Includes scorecarding frameworks, RFP processes, market intelligence, and compliance vetting. Use when managing carriers, negotiating rates, evaluating carrier performance, or building freight strategies.

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Systematic QA testing for web applications, desktop apps, and workflows. Three tiers: QUICK (critical/high severity only — 5 min), STANDARD (+ medium severity — 15 min), EXHAUSTIVE (+ cosmetic — 30+ min). Tests user flows, finds bugs, reports with severity classification, and optionally auto-fixes with atomic commits. Produces before/after health scores and ship-readiness summary. Use when user says "test this", "QA", "find bugs", "does this work?", or "is it ready to ship?". Voice triggers: "quality check", "run QA", "test the app". For report-only (no fixes), use with --report-only flag.

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Analyzes single pattern implementation, calculates 4 scores (compliance, completeness, quality, implementation), identifies gaps. Invoked by ln-640 or standalone.

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Audits architectural patterns against best practices. Maintains patterns catalog, calculates 4 scores per pattern. Output: patterns_catalog.md.

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Checks DRY/KISS/YAGNI/architecture compliance with quantitative Code Quality Score. Validates decisions via MCP Ref. Reports with SEC-/PERF-/MNT-/ARCH-/BP-/OPT- prefixes.

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Score a repository's agentic legibility from repo-visible evidence only. Use when Codex needs to audit how easy a codebase is for coding agents to discover, bootstrap, validate, and navigate, especially for harness-engineering reviews, developer-experience audits, repo cleanup, or before/after comparisons after improving docs, tooling, or architectural constraints.

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This skill should be used when the user asks to "implement a DiD regression", "run a staggered difference-in-differences", "set up an event study", "implement IV / 2SLS", "run a regression discontinuity design", "build synthetic control", "do propensity score matching", "test parallel trends", "run Honest DiD", "wild cluster bootstrap", "Callaway-Sant'Anna", "Sun-Abraham", "Bacon decomposition", "double machine learning", "debiased machine learning", "DML / DoubleML", "cross-fitting", "causal forest", "heterogeneous treatment effects", "ATE with ML controls", "which causal design fits my data", or needs causal-inference code, diagnostics, or identification writing in Python (StatsPAI), R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape, with runnable validated templates, ML-based causal inference patterns, and Journal of Finance applications.

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Non-interactive content-production toolkit: mine quotable moments from podcast RSS feeds and meeting notes, discover clip-worthy moments in video transcripts, repurpose long-form source into platform-native drafts (X, LinkedIn, YouTube Shorts, newsletter), and batch-score/gate those drafts before publish. Use when asked to "mine quotes from this podcast", "find clips in this video", "repurpose this into a thread / LinkedIn post / Short", "turn this transcript into posts", "extract viral moments", or "gate this batch of drafts". Runs Python scripts end to end. For interactive expert-panel scoring of a single artifact see content-ops.

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Autonomous growth experimentation framework: creates experiments with hypotheses, logs data points, runs statistical analysis (bootstrap CI + Mann-Whitney U), auto-promotes winners to a living playbook, and suggests next experiments. Use when asked to run growth experiments, analyze A/B tests, build experiment scorecards, or generate pacing alerts. For SEO-specific experiments see seo-ops; for outbound experiments see outbound-engine.

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Drive Chromium from standard Playwright APIs with a real-device fingerprint applied in the kernel, one persistent isolated profile per identity, and a per-profile proxy whose exit IP sets timezone and WebRTC - JavaScript (npm 'anti-detect-browser') or Python (PyPI 'antibrow'). Use when sessions must stay logged in across runs and stay separate, when a scraper or agent is blocked by an incoherent headless fingerprint, when checking ads or pricing from another region, when a page must be reached as a phone rather than a desktop, when automation mints a profile per task, when running several of your own accounts from one machine, or when testing how your own bot detection scores a real device. Also for 'antibrow', 'fingerprint browser', 'multi-account browser', '防关联', '多账号', '安卓模拟', 'Android profile', 'mobile fingerprint', 'temporary profile', 'CreepJS', 'residential proxy', 'browser-use', 'crawl4ai'. MCP control is browser-mcp-agent; isolation is multi-account-isolation.

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Identify customers most likely to churn before they leave. Uses behavioral signals, usage patterns, and engagement data to score accounts and recommend retention actions.

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Run a structured AI readiness audit for any organization. Scores 8 dimensions, identifies gaps, produces a prioritized 90-day action plan with budget ranges.

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Score, monitor, and troubleshoot AI agent fleets in production. Built for ops teams running 1-100+ agents.

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Search, score, and compare flights with jetlag impact 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。

Prepare for the ACT with adaptive practice, score tracking, weak area analysis, and college targeting.

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Code auditor that grades software projects and the agents/developers who built them. Produces structured JSON + Markdown reports with a standardized rubric. Evaluates architecture, correctness, testing, authorship cohesiveness, ghost code, and more. Optionally ingests tmux session logs for process evidence and interrogates agents about specific problems found. Use when asked to "audit", "grade", "evaluate", "review code quality", "rate this project", "score the agents", or "assess the codebase". Especially useful after Marcus multi-agent experiments to grade agent output and track quality over time.

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Perform comprehensive technical reviews of Ruby on Rails applications. Runs automated analysis tools (RubyCritic, Brakeman, bundler-audit, Gitleaks, Debride, linters, SimpleCov, Rails stats, Rails ERD), analyzes code for architecture, security, authorization (Pundit/CanCanCan), dead code, and design issues, and produces a structured markdown report with prioritized findings and a 0-10 score. Use when the user requests a tech review, code audit, project assessment, or quality analysis of a Rails application.

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。

Run linters/typecheckers to calculate health score.

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。

Systematically QA-tests a web app and fixes bugs: test, then iteratively fix, re-verify, commit each fix. Tiers: Quick, Standard, Exhaustive. Produces before/after health scores and fix evidence.

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。

Experimental/manual harness canary procedure. It records benchmark manifests and scores but does not provide a fully automated runner.

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 refining a draft user prompt before sending it to a coding agent. Decomposes goal/context/constraints/acceptance/output-format, detects ambiguity and model-coupled framing, then emits a model-agnostic rewrite. Supports --analyze, --score, and --deep. NOT for prompts from scratch (mk:brainstorming), plans/reviews (mk:elicit), implementation plans (mk:plan-creator), or general codebase scouting (mk:scout).

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。

Experimental/manual harness canary procedure. It records benchmark manifests and scores but does not provide a fully automated runner.

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。

Pre-routing ambiguity analysis — scores request clarity and asks clarifying questions when needed (inspired by ouroboros)

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。