技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 328 · 0 current installs · 0 all-time installs
⭐ 0
安装量（当前） 0
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：1kalin/afrexai-ai-spend-audit
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
This is an instruction-only audit framework for AI spend that is internally consistent with its stated purpose and contains no code, installs, or built-in credential requests.
目的
The name and description promise an AI spend audit; the SKILL.md provides a detailed, plausible framework (inventory, scoring, model optimization, vendor consolidation, reporting) that matches that purpose. There are no unrelated dependencies, binaries, or config requirements declared.
说明范围
The instructions are largely advisory and procedural. They do recommend actions that, in practice, require access to billing data, API-based models, and production queries (e.g., 'run 100 production queries through a cheaper model' and mapping API-based tools). The skill itself does not include code to perform these operations nor does it request credentials — implementers will need to supply data and keys. This is scope-appropriate but import…
安装机制
No install spec and no code files are included; nothing is written to disk and there are no downloaded binaries. This is the lowest-risk installation profile.
证书
The skill declares no required environment variables or credentials, which is appropriate for an instruction-only framework. However, several suggested checks implicitly require access to API keys, billing exports, or production queries. Users must provide those credentials or data to perform the audit; the skill does not attempt to obtain them automatically.
持久
The skill does not request persistent presence (always:false), does not modify other skills, and contains no install steps that would alter agent/system configuration. Normal autonomous invocation remains possible but is not elevated by the skill itself.
综合结论
This skill is an advisory playbook (no code), so installing it itself is low-risk. Before executing an audit guided by this skill: 1) Do not paste long-lived API keys, passwords, or raw billing exports into chats—use read-only or scoped/ephemeral keys where possible. 2) Run any model-comparison tests on anonymized or synthetic data or in a staging environment to avoid leaking PII. 3) Provide the agent only the minimum data needed (e.g., aggreg…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Ai Spend Audit」。简介：Audit and optimize your company's AI spending by identifying waste, measuring R…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/1kalin/afrexai-ai-spend-audit/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
# AI Spend Audit
Audit your company's AI spending — find waste, measure ROI, and right-size your tool stack.
## When to Use
- Quarterly AI budget reviews
- Before renewing AI tool subscriptions
- When AI spend exceeds 3% of revenue without clear ROI
- Evaluating build vs buy decisions for AI capabilities
## The Framework
### Step 1: Inventory Every AI Line Item
Map all AI spending across these categories:
| Category | Examples | Typical Waste |
|----------|----------|---------------|
| **Foundation Models** | OpenAI, Anthropic, Google API keys | 40-60% (unused capacity, wrong model tier) |
| **SaaS with AI** | Salesforce Einstein, HubSpot AI, Notion AI | 30-50% (features enabled but unused) |
| **Custom Development** | Internal ML teams, fine-tuning, RAG pipelines | 25-45% (duplicate efforts, over-engineering) |
| **Infrastructure** | GPU instances, vector DBs, embedding compute | 35-55% (over-provisioned, always-on dev instances) |
| **Data & Training** | Labeling services, training data, synthetic data | 20-40% (one-time costs recurring unnecessarily) |
### Step 2: Score Each Tool (0-100)
**Usage Score (0-30)**
- 0: Nobody uses it
- 10: <25% of licensed users active
- 20: 25-75% active
- 30: >75% active, daily use
**ROI Score (0-40)**
- 0: No measurable business impact
- 10: Saves time but no revenue/cost link
- 20: Measurable cost reduction (<2x spend)
- 30: Clear ROI (2-5x spend)
- 40: High ROI (>5x spend)
**Replaceability Score (0-30)**
- 0: Commodity (10+ alternatives at lower cost)
- 10: Some alternatives exist
- 20: Few alternatives, moderate switching cost
- 30: Irreplaceable, deep integration
**Action Thresholds:**
- Score 0-30: **CUT** — cancel immediately
- Score 31-50: **REVIEW** — renegotiate or find alternative
- Score 51-70: **OPTIMIZE** — right-size tier/usage
- Score 71-100: **KEEP** — monitor quarterly
### Step 3: Model Cost Optimization
For every API-based AI tool, check:
1. **Model Selection**: Are you using GPT-4 where GPT-3.5 suffices? Claude Opus where Sonnet works?
- Rule: Use the cheapest model that meets quality threshold
- Test: Run 100 production queries through cheaper model, measure quality delta
2. **Caching**: Are you re-processing identical or similar queries?
- Semantic cache can cut 20-40% of API calls
- Exact-match cache catches another 5-15%
3. **Batch vs Real-time**: Which requests actually need sub-second response?
- Batch processing is 50% cheaper on most providers
- Queue non-urgent requests for batch windows
4. **Token Optimization**:
- Trim system prompts (every token costs money at scale)
- Use structured output to reduce response tokens
- Implement max_tokens limits per use case
### Step 4: Vendor Consolidation
Map overlapping capabilities:
```
Current State → Target State
─────────────────────────────────────────
ChatGPT Teams + Claude Pro + Gemini → Pick ONE primary + ONE backup
Jasper + Copy.ai + ChatGPT for content → Single content tool
3 different vector databases → Consolidate to 1
Internal embeddings + OpenAI embeddings → Standardize on one
```
**Consolidation savings**: Typically 25-40% of total AI spend.
### Step 5: Build the Audit Report
```
AI SPEND AUDIT — [Company Name] — [Quarter/Year]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Total AI Spend: $___/month ($___/year)
AI Spend as % Revenue: ___%
Industry Benchmark: 2-5% (early adopter) / 0.5-2% (mainstream)
WASTE IDENTIFIED
├── Unused licenses: $___/month
├── Over-provisioned infra: $___/month
├── Model tier downgrades: $___/month
├── Vendor consolidation: $___/month
└── TOTAL RECOVERABLE: $___/month ($___/year)
ACTIONS
┌─ CUT (Score 0-30): [list tools]
├─ REVIEW (Score 31-50): [list tools]
├─ OPTIMIZE (Score 51-70): [list tools]
└─ KEEP (Score 71-100): [list tools]
90-DAY PLAN
Week 1-2: Cancel CUT items, begin REVIEW negotiations
Week 3-4: Implement model downgrades and caching
Week 5-8: Vendor consolidation migration
Week 9-12: Measure savings, establish ongoing monitoring
```
## Company Size Benchmarks (2026)
| Company Size | Typical AI Spend | Typical Waste | Recoverable |
|-------------|-----------------|---------------|-------------|
| 10-25 employees | $2K-$8K/mo | 35-50% | $700-$4K/mo |
| 25-50 employees | $8K-$25K/mo | 30-45% | $2.4K-$11K/mo |
| 50-200 employees | $25K-$80K/mo | 25-40% | $6K-$32K/mo |
| 200-500 employees | $80K-$300K/mo | 20-35% | $16K-$105K/mo |
| 500+ employees | $300K-$1M+/mo | 15-30% | $45K-$300K/mo |
## Red Flags
- AI spend growing faster than revenue (unsustainable)
- More than 3 overlapping tools in same category
- No usage tracking on AI SaaS licenses
- GPU instances running 24/7 for dev/test workloads
- Paying for enterprise tiers with startup-level usage
- No A/B testing between model tiers
- "Innovation budget" with no success metrics
## Industry Adjustments
- **SaaS/Tech**: Higher AI spend acceptable (5-8%) if it's in the product
- **Professional Services**: Focus on billable hour impact — $1 AI spend should save $5+ in labor
- **Manufacturing**: AI spend should tie to defect reduction or throughput gains
- **Healthcare**: Compliance costs inflate spend 20-30% — factor in before judging waste
- **Financial Services**: Model risk management adds 15-25% overhead — legitimate cost
- **Ecommerce**: Measure AI spend per order — should decrease as volume scales
---
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