284 件 ・ 関連度順
概要と使いどころ
Create, schedule, duplicate, inspect, cancel, archive, and troubleshoot Vapi outbound Campaigns. Use for persistent multi-contact calling, CSV or API contact personalization, campaign concurrency, campaign webhooks, pre-dial eligibility, contact outcomes, or rerunning an audience. Do not use for a single call or a simple one-off /call batch.
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。
Audits fielded survey-response data for registered elements, data quality, bot and AI-automation screening, and sample integrity, and produces an appendix-ready quality report. Use at soft launch before full field, after fielding before outcomes are analyzed, or when asked what a study did about bots and AI respondents.
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。
Audit fielded survey response data for registered elements, data quality, bot and AI-automation screening, and sample integrity, and emit an appendix-ready quality report. Use at soft launch or after fielding, before outcomes are analyzed.
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。
Self-evolution workflow for the agent. Before substantive work, recall past outcomes from evolution memory; while editing, detect improvement signals; at task end, record the outcome; when reusable, distill or search the EvoMap network for proven genes/capsules. Use when the user starts non-trivial work (a feature, a fix, a refactor) or asks the agent to "evolve", "learn from this", or "remember how this went".
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 and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Claude, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.
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 prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.
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。
Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.
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 repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.
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 repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for parser differentials, HTTP normalization gaps, ambiguous headers, path decoding drift, transfer-framing mismatches, and request smuggling routes. Use when the user asks to trace proxy and backend parse differences, conflicting path normalization, Host or forwarded-header ambiguity, CL/TE issues, or routing outcomes that differ across hops. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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 repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.
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。
Strategic Customer Success leadership guidance for CS org design, customer segmentation and tiering (tech touch, low touch, high touch), success metrics and KPIs (NRR, GRR, NPS, CSAT, CES), playbook development, executive stakeholder management, CS technology stack strategy, value realization frameworks, and customer journey mapping. Use when building CS teams, defining customer segments, designing playbooks, measuring success outcomes, or implementing CS platforms.
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 probability-weighted valuation models with multiple exit scenarios, timing assumptions, and risk-adjusted returns. Use when building growth equity valuations, modeling scenario-weighted outcomes, or analyzing risk-adjusted returns.
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 growth equity return models with minority/majority economics, participation rights, and preference stack analysis. Use when modeling growth equity returns, projecting minority investment outcomes, or analyzing preference structures.
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。
Assesses litigation outcome impact with settlement probability, damage range estimation, and stock price sensitivity analysis. Use when evaluating litigation catalysts, modeling legal outcomes, or analyzing litigation-driven opportunities.
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。
Constructs venture return models with entry valuation, follow-on reserve, multiple scenario exits, and portfolio-level fund math. Use when modeling VC returns, calculating fund economics, or projecting portfolio outcomes.
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 SPAC de-SPAC transaction economics with sponsor dilution, redemption risk, PIPE analysis, and warrant impact. Use when analyzing SPAC deals, evaluating sponsor economics, or modeling de-SPAC outcomes.
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。
Constructs accretion/dilution analysis with pro forma financials, synergy phasing, and purchase price allocation. Use when modeling merger outcomes, calculating EPS accretion, or analyzing deal structures.
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 master and special servicer performance with delinquency management, modification outcomes, and loss mitigation effectiveness. Use when assessing servicer quality, monitoring servicing metrics, or evaluating servicer transfers.
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 regulatory decision impact with approval probability, timeline analysis, and outcome scenario modeling for event-driven positions. Use when analyzing regulatory events, evaluating FDA/FCC/DOJ decisions, or modeling regulatory outcomes.
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 proxy fight mechanics with shareholder base analysis, ISS/Glass Lewis recommendations, and vote probability modeling. Use when analyzing proxy contests, assessing vote outcomes, or evaluating director nomination campaigns.
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 distressed debt opportunities with recovery analysis, liquidity assessment, and potential restructuring outcomes. Use when screening distressed situations, analyzing stressed credits, or evaluating workout scenarios.
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。
Improve router-facing pipeline and reflection narration to reduce noisy status churn and make Step 0/Reflection outcomes explicit. Use when updating Router output contract, reflection reminder wording, or post-pipeline notification batching.
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。
Feed actual task results back into agent memory for calibration. Compares predicted vs actual outcomes, records accuracy scores, and tracks estimation quality, prediction quality, and decision quality over time to improve future agent performance.
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。