本文へ移動
cccskills
無料GitHub で公開

saas-replacement-planner

Evaluates which SaaS tools can be replaced with AI agents. Takes a list of current SaaS subscriptions with costs, assesses replacement feasibility, estimates build vs buy economics, identifies Claude+MCP alternatives, and generates a comprehensive replacement plan with priority matrix, ROI analysis, implementation timeline, and risk assessment.

インストール方法を見る

含まれるファイル(5)

  • SKILL.md3.2 KB
  • references/analysis-framework.md6.6 KB
  • references/analysis-guidelines.md2.6 KB
  • references/output-template.md5.3 KB
  • references/patterns.md3.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

SaaS Replacement Planner

Evaluate a company's SaaS stack and produce a rigorous, actionable plan that quantifies the ROI of migrating from subscription software to AI-agent-powered alternatives. Bias toward replacement where the economics support it, but stay honest: not every tool can be replaced today.

Contents

  • references/analysis-framework.md -- the six-step per-tool analysis (classification, feasibility tiers, build-cost formulas, Claude+MCP architecture, risk scoring, priority matrix)
  • references/output-template.md -- full saas-replacement-plan.md document structure
  • references/patterns.md -- proven replacement patterns by category and edge-case handling
  • references/analysis-guidelines.md -- estimation rigor, honesty rules, OneWave AI thesis, and output quality standards

Workflow

  1. Gather input. Collect the SaaS tool list. Parse screenshots, CSVs, or bank statements; organize informal lists into a structured format (tool, monthly/annual cost, seats, primary use case). Ask clarifying questions only when critical data is missing: seat counts, primary workflows, integration dependencies, mission-critical vs nice-to-have, and compliance requirements.
  2. Research current pricing. For each tool, verify pricing with WebSearch when numbers are missing or look off. Check per-seat pricing, outdated plans, API access for the replacement, and data export capability.
  3. Analyze each tool. Run the complete six-step framework in references/analysis-framework.md for every tool. Do not skip tools or give superficial analysis. Apply matching patterns from references/patterns.md.
  4. Build the priority matrix. Plot all tools on the Impact vs Effort axes. Sequence replacements and group tools that share infrastructure (e.g., all needing Supabase) to reduce incremental build cost.
  5. Generate the timeline. Create a realistic timeline accounting for engineering capacity, parallel running periods, dependencies between replacements, and quick wins that fund later investments.
  6. Write the plan. Generate saas-replacement-plan.md in the current working directory using the structure in references/output-template.md. Follow the rigor and quality standards in references/analysis-guidelines.md.
  7. Present key findings. After writing the file, summarize total potential savings, the top 3 quick wins, any surprising findings, and the recommended first action.

Core Principle

Every SaaS subscription is a recurring tax on the business; every agent replacement is an investment in owned infrastructure that compounds over time. Make the numbers speak clearly and let the ROI make the argument. See references/analysis-guidelines.md for the full OneWave AI thesis and how to frame the analysis.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.

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。

OneWave-AI/claude-skills3392026年10月2日 更新

Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

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。

OneWave-AI/claude-skills3392026年10月2日 更新

Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.

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。

OneWave-AI/claude-skills3392026年10月2日 更新

Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.

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。

OneWave-AI/claude-skills3392026年10月2日 更新

Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.

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。

OneWave-AI/claude-skills3392026年10月2日 更新

Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.

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。

OneWave-AI/claude-skills3392026年10月2日 更新

OneWave-AI のスキルをすべて見る

このスキルの問題を報告する