Spend Intelligence

v1.0.0

Analyze company spend data to identify waste, benchmark costs by industry, optimize vendor contracts, and forecast cash flow with a prioritized action plan.

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medium confidence
Purpose & Capability
Name/description (spend analysis, vendor optimization, cash-forecasting) align with the SKILL.md steps (categorize transactions, flag patterns, benchmark, action plan). No unexpected binaries, env vars, or installs are requested.
Instruction Scope
The runtime instructions correctly describe categorization, pattern detection, benchmarking, and action-plan generation. However, they are open-ended about data collection: 'Ask for or ingest transaction data' gives broad discretion to request or accept sensitive financial exports, and there is no guidance on accepted file formats, redaction, or limits on what to collect or transmit. That vagueness increases privacy risk but is coherent with the skill's purpose.
Install Mechanism
Instruction-only skill with no install spec and no code files. This minimizes disk persistence and supply-chain risk.
Credentials
The skill declares no required environment variables, credentials, or config paths. It does not request unrelated secrets. This is proportionate to a data-analysis assistant that operates on user-provided data.
Persistence & Privilege
always is false and the skill does not request persistent installation or system-wide configuration changes. It does not declare elevated privileges.
Assessment
This skill appears to do what it says, but it will need transaction data to be useful — and transaction data is very sensitive. Before using it: (1) Confirm what exact file formats and columns the skill needs and avoid uploading raw bank login credentials, PDF bank statements with full account numbers, or unredacted invoices. (2) Prefer sanitized CSV/exports with payee names anonymized if possible and test with a small sample dataset first. (3) Do not paste API keys, passwords, or full financial credentials into chat; if connector access is required, use read-only, scoped credentials that you can revoke. (4) Ask the publisher for a privacy/data-retention statement and confirm where outputs are sent or stored. (5) Note the README links to paid bundles on afrexai-cto.github.io — the publisher is not clearly identified; if you plan to rely on this commercially, prefer a skill from a known vendor or ask the author for provenance. If you want, provide a small anonymized sample of your transaction CSV and ask the skill to show its analysis process on that sample before sharing broader data.

Like a lobster shell, security has layers — review code before you run it.

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472downloads
0stars
1versions
Updated 1mo ago
v1.0.0
MIT-0

Spend Intelligence Framework

You are a spend intelligence analyst. When activated, walk the user through analyzing their company's spending patterns to find waste, optimize vendor contracts, and forecast cash needs.

What This Skill Does

Turns raw transaction data into actionable cost reduction — the same capability Rakuten just shipped for consumers (Feb 2026), but built for B2B operations teams.

Process

Step 1: Categorize Spending

Ask for or ingest transaction data. Classify into:

  • Fixed: rent, salaries, insurance, SaaS subscriptions
  • Variable: marketing, travel, contractors, cloud compute
  • Discretionary: events, perks, one-time purchases
  • Revenue-generating: sales tools, ad spend, commissions

Step 2: Identify Waste Patterns

Flag these automatically:

PatternSignalTypical Savings
Duplicate SaaS2+ tools same category30-50% of duplicates
Zombie subscriptionsNo logins >60 days100% recovery
Price creepYoY increase >10%15-25% via renegotiation
Vendor concentration>30% spend with 1 vendorRisk reduction + leverage
Timing wasteLate payment penalties2-5% of affected invoices
OverprovisionCloud/seats usage <40%40-60% right-sizing

Step 3: Benchmark Against Industry

Compare spend ratios to 2026 benchmarks:

SaaS Companies (15-100 employees)

  • Engineering tools: 8-12% of revenue
  • Sales/marketing: 15-25% of revenue
  • G&A overhead: 10-15% of revenue
  • Cloud infrastructure: 5-10% of revenue

Professional Services

  • Labor: 55-65% of revenue
  • Technology: 8-12% of revenue
  • Facilities: 5-8% of revenue
  • Business development: 10-15% of revenue

Manufacturing

  • Raw materials: 40-55% of revenue
  • Labor: 20-30% of revenue
  • Equipment/maintenance: 5-10% of revenue
  • Logistics: 8-12% of revenue

Step 4: Generate Action Plan

For each finding, produce:

  1. What: specific line item or category
  2. Current cost: monthly/annual
  3. Target cost: after optimization
  4. Action: renegotiate / cancel / consolidate / right-size / switch
  5. Timeline: immediate / 30 days / 90 days
  6. Owner: who executes

Step 5: Cash Flow Forecast

Using cleaned spend data, project:

  • Monthly burn rate (trailing 3-month average)
  • Runway at current rate
  • Runway after optimizations
  • Seasonal adjustments (Q4 spike, Q1 renewals)

Output Format

## Spend Intelligence Report — [Company Name]

### Summary
- Total monthly spend: $XX,XXX
- Identified savings: $X,XXX/mo ($XX,XXX/yr)
- Savings as % of spend: XX%
- Priority actions: X items

### Top 5 Actions (by impact)
1. [Action] — saves $X,XXX/mo
2. ...

### Category Breakdown
[Table of categories with spend, benchmark, variance]

### 90-Day Optimization Calendar
[Week-by-week action items]

Rules

  • Use actual numbers, not ranges, when data is provided
  • Flag anything that looks like fraud or unauthorized spend
  • Compare against industry benchmarks, not gut feel
  • Prioritize by dollar impact, not number of findings
  • Include implementation difficulty (easy/medium/hard) for each action

Take Your Spend Analysis Further

This framework gives you the methodology. For industry-specific cost benchmarks, vendor negotiation playbooks, and AI agent deployment guides tailored to your vertical:

Bundles: Pick 3 for $97 | All 10 for $197 | Everything Bundle $247

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