Install
$ agentstack add skill-takusaotome-claude-skills-library-ai-bpo-proposal-generator ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
AI-Powered BPO Proposal Generator
Overview
Generate comprehensive BPO (Business Process Outsourcing) service proposals tailored for Japanese companies operating in the US market. This skill creates professional proposals that combine AI-powered service offerings with traditional BPO services, including ROI analysis, implementation roadmaps, and bilingual documentation. The output targets "在米日系企業向けAI実装" market positioning.
When to Use
- Creating BPO service proposals for Japanese subsidiaries in the US
- Developing AI-enhanced outsourcing offerings for traditional business processes
- Estimating ROI for AI implementation in back-office operations
- Building implementation roadmaps for phased AI adoption
- Generating bilingual (Japanese/English) proposal documents
- Positioning AI services for the 在米日系企業 market segment
Prerequisites
- Python 3.9+
- No API keys required (calculations are local)
- Dependencies: pandas, jinja2 (for template rendering)
Workflow
Step 1: Gather Client Requirements
Collect client information using the intake questionnaire:
- Company profile (industry, size, US operation scale)
- Current pain points (manual processes, error rates, processing volumes)
- Budget range and timeline expectations
- Language requirements (primary communication language)
- Compliance requirements (SOX, J-SOX, data residency)
Reference the client intake template:
cat references/client-intake-template.md
Step 2: Select Applicable Service Modules
Review the AI-BPO service catalog and select relevant modules:
python3 scripts/select_services.py \
--industry "manufacturing" \
--pain-points "invoice_processing,expense_reporting" \
--output proposal_services.json
Service categories include:
- Finance & Accounting: AP/AR automation, expense management, reconciliation
- HR & Payroll: Onboarding, time tracking, benefits administration
- Customer Support: Ticket routing, FAQ automation, sentiment analysis
- Data Processing: Document digitization, data entry, validation
- Procurement: Vendor management, PO processing, contract analysis
Step 3: Calculate ROI Estimation
Generate ROI projections based on selected services and client volumes:
python3 scripts/calculate_roi.py \
--services proposal_services.json \
--volumes '{"invoices_per_month": 5000, "employees": 200}' \
--output roi_analysis.json
The ROI calculation includes:
- Current state cost analysis (FTE, error rates, processing time)
- Future state projections (automation rates, accuracy improvements)
- Implementation costs (setup, training, integration)
- Payback period and 3-year NPV
Step 4: Generate Implementation Roadmap
Create a phased implementation plan:
python3 scripts/generate_roadmap.py \
--services proposal_services.json \
--start-date "2025-04-01" \
--output roadmap.json
Standard phases:
- Discovery & Assessment (2-4 weeks): Process mapping, data audit
- Pilot Implementation (4-6 weeks): Single process, limited scope
- Phased Rollout (8-12 weeks): Expand to additional processes
- Optimization (Ongoing): Model tuning, process refinement
Step 5: Generate Proposal Document
Create the final bilingual proposal document:
python3 scripts/generate_proposal.py \
--client-name "ABC Corporation" \
--services proposal_services.json \
--roi roi_analysis.json \
--roadmap roadmap.json \
--language "bilingual" \
--output proposal_ABC_Corporation.md
Output Format
JSON Service Selection
{
"schema_version": "1.0",
"generated_at": "2025-04-15T10:30:00Z",
"client_industry": "manufacturing",
"selected_services": [
{
"service_id": "fin-001",
"service_name": "Invoice Processing Automation",
"category": "finance_accounting",
"ai_components": ["document_extraction", "validation", "approval_routing"],
"estimated_automation_rate": 0.85,
"monthly_fee_usd": 5000
}
]
}
JSON ROI Analysis
{
"schema_version": "1.0",
"analysis_date": "2025-04-15",
"current_state": {
"annual_cost_usd": 250000,
"fte_equivalent": 3.5,
"error_rate_pct": 4.2
},
"future_state": {
"annual_cost_usd": 120000,
"fte_equivalent": 1.0,
"error_rate_pct": 0.5
},
"implementation_cost_usd": 75000,
"annual_savings_usd": 130000,
"payback_months": 7,
"three_year_npv_usd": 285000,
"roi_percentage": 173
}
Markdown Proposal Structure
# AI-BPO Service Proposal / AI-BPOサービス提案書
## Executive Summary / エグゼクティブサマリー
## Company Understanding / 貴社の理解
## Proposed Solution / ご提案ソリューション
## Service Details / サービス詳細
## ROI Analysis / ROI分析
## Implementation Roadmap / 導入ロードマップ
## Pricing / 価格
## Terms & Conditions / 契約条件
## Next Steps / 次のステップ
Resources
scripts/select_services.py-- Service module selection based on industry and pain pointsscripts/calculate_roi.py-- ROI calculation with NPV, payback periodscripts/generate_roadmap.py-- Implementation roadmap generatorscripts/generate_proposal.py-- Bilingual proposal document generatorreferences/service-catalog.md-- Complete AI-BPO service catalog with pricingreferences/client-intake-template.md-- Client requirements questionnairereferences/roi-methodology.md-- ROI calculation methodology and benchmarks
Key Principles
- Bilingual by Default: All client-facing documents include both Japanese and English
- Conservative ROI Estimates: Use industry-standard automation rates, not best-case scenarios
- Phased Implementation: Recommend pilot-first approach to minimize risk
- Compliance-Aware: Consider J-SOX, SOX, and data residency requirements
- Cultural Sensitivity: Understand Japanese business practices and decision-making processes
Industry Benchmarks
| Process | Typical Automation Rate | Error Reduction | Cost Savings | |---------|------------------------|-----------------|--------------| | Invoice Processing | 80-90% | 85-95% | 50-70% | | Expense Reporting | 70-85% | 80-90% | 40-60% | | Employee Onboarding | 60-75% | 70-85% | 35-50% | | Customer Ticket Routing | 85-95% | N/A | 60-75% | | Data Entry | 90-98% | 95-99% | 70-85% |
Pricing Tiers
| Tier | Monthly Volume | Base Fee (USD) | Per-Transaction | |------|---------------|----------------|-----------------| | Starter | < 1,000 | $3,000 | $1.50 | | Growth | 1,000 - 5,000 | $8,000 | $0.80 | | Enterprise | 5,000+ | $15,000 | $0.40 |
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: takusaotome
- Source: takusaotome/claude-skills-library
- License: MIT
- Homepage: https://takusaotome.github.io/claude-skills-library/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.