「impact」の検索結果

Evaluates optimal leverage with WACC minimization, rating impact, and financial flexibility assessment across market conditions. Use when optimizing capital structure, analyzing target leverage, or evaluating rating implications.

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 amendment and waiver requests with consent requirements, fee structures, and modified term impact analysis. Use when analyzing amendments, structuring consent solicitations, or evaluating covenant relief requests.

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。

Git-aware smart revert for tracks, phases, and tasks. Handles rewritten history, finds related commits, and provides safe rollback with multiple confirmation 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。

Detect Claude Code context compaction events in session JSONL logs. Identifies compaction boundaries, measures token delta before/after, reports compaction events with timestamps and token impact.

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。

Increase visual impact, contrast, personality, and compositional confidence when a design feels bland, generic, or too safe. Use when the design needs more energy, confidence, or memorability—not when the real problem is clutter, over-intensity, or micro-detail polish.

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。

Add moments of joy, personality, and surprise through success states, empty states, loading moments, hover details, and celebratory polish once the fundamentals already work. Use when the user wants delight, charm, personality, or memorable micro-interactions—not when the main need is baseline cleanup or stronger visual impact.

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。

Write Bugcrowd-format reports — VRT (Vulnerability Rating Taxonomy) mapping, P1-P5 severity, impact-led writing, and Bugcrowd-specific submission etiquette. Use AFTER triage-validation passes for Bugcrowd programs.

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。

Write production-grade HackerOne reports — title formula, impact-led writing, CVSS 3.1 scoring, complete PoC formatting, attached files, and submission etiquette. Use AFTER triage-validation passes and you're ready to submit on HackerOne.

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。

Commission a project to the Assured (Method 2) SDLC bundle — install plugin, scaffold programs.yaml, set granularity and regulatory context, configure change-impact-gate.

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 and scientifically interpret completed research runs, including negative, null, boundary, confounded, failed, and inconclusive outcomes. Use after execution to assess evidence and claim impact; not to edit methods or schedule experiments.

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。

Design and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.

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。

Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

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。

Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.

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 retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.

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Fetch upcoming economic events and data releases using FMP API. Retrieve scheduled central bank decisions, employment reports, inflation data, GDP releases, and other market-moving economic indicators for specified date ranges (default: next 7 days). Output chronological markdown reports with impact assessment.

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Use when an answer is wanted from one named seat only: breaking, business, career, codebase, human, impact, innovation, moat, rent-seeking, skeptic, or stability. Not for multi-lens synthesis: use prism. Not for rebuilding from primitives: use from-first-principle.

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Use when asked to audit typography across a codebase. Produces a file:line report with concrete fixes ordered by impact and flags unverifiable rules. Not for building a type system or token scale.

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Assess and route one bounded Proto UI dependency drift, advisory, or update question. Use to trace manifest and lockfile identity, consumer impact, compatibility evidence, provenance, update risk, and the exact next update or no-action transition. This leaf remains read-only.

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Use for ANY bug bounty, penetration test, or web/API/mobile security assessment: recon, testing endpoints, analyzing Burp traffic or any bug-bounty platform's MCP (HackerOne, Intigriti, Bugcrowd, YesWeHack, Immunefi), reviewing APKs, bypassing a WAF, enforcing scope, hunting a specific vuln class, validating findings, writing reports, or when you are stuck on a target and about to give up. Activates automatically for all bug-bounty and pentest sessions. Runs an autonomous, never-surrender predator methodology: refuses to conclude "secure", exhausts each vuln class to depth, proves a real attacker-path with impact and no false positives, tracks coverage so nothing is re-tested or skipped, diagnoses failed tooling from full logs, and hunts at maximum effort with intelligent token use until the goal is found. Activates and follows instructions in ANY language the operator writes in (the skill itself is English). Bundles a per-class web-exploitation KB, a writeup library, and PayloadsAllTheThings.

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 skill whenever the user wants to create, improve, rewrite, review, tailor, or ATS-optimize a PRODUCT MANAGER resume or CV. Triggers include any mention of 'PM resume', 'product manager resume', 'product resume', 'PM CV', 'product management CV', plain-language asks like 'create my resume', 'build my CV', 'optimize my resume', 'review my resume', 'make it ATS-friendly', 'tailor my resume to this job description', 'help me get hired', the Spanish terms 'hoja de vida' or 'curriculum vitae', or a request to turn product/PM experience into a resume, quantify product impact, write PM bullet points, or prepare a resume for an APM, PM, Senior, Lead, Group, Director of Product, Head of Product, or CPO application. Also use for AI Product Manager, Growth PM, Platform/Technical PM, and Product Owner resumes. Do NOT use for engineering-only resumes, LinkedIn rewrites, portfolios, or cover letters unless the resume is the main deliverable.

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 the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"

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 surfacing the assumptions underlying an analytical judgment, the user asks "what are we assuming?" / "are these assumptions still valid?", or before publishing a high-impact assessment. Standard SAT applied during major assessments.

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。

Expert error coordinator specializing in distributed error handling, failure recovery, and system resilience. Masters error correlation, cascade prevention, and automated recovery strategies across multi-agent systems with focus on minimizing impact and learning from failures.

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Master coordinator for the founder. Filters noise, owns cross-functional processes, enforces consistency, routes decisions, and positions outputs for impact so the founder can think clearly. Doesn't own any function owns the space between all of them.

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。