Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/huifer/claude-code-interview/marketgit clone --depth 1 https://github.com/huifer/claude-code-interviewWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00023 | $0.06346 |
| Opus 5 | $0.00012 | $0.03173 |
| Sonnet 5 | $0.00005 | $0.01269 |
| Haiku 4.5 | $0.00002 | $0.00635 |
Grade A, and why
market scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 773 lines — stays where its author put it; the contents beside it link to each section on GitHub.
市场情报分析
提供全面的市场情报，帮助你做出数据驱动的求职决策。
使用方法
生成市场分析报告
# 综合市场分析
/analytics/market
# 技能需求分析
/analytics/market --skills
# 薪资分析
/analytics/market --salary
# 竞争定位
/analytics/market --positioning
# 趋势分析
/analytics/market --trends
# 地区市场分析
/analytics/market --location "San Francisco"
/analytics/market --location "New York"
/analytics/market --location "Beijing"
对比分析
# 对比多个地区
/analytics/market --compare "San Francisco" "New York" "Remote"
# 对比不同公司级别
/analytics/market --compare-levels Junior Senior Staff
# 对比不同时间段
/analytics/market --compare-months 2024-01 2024-02
导出报告
# 导出为报告
/analytics/market --output exports/market/report-2024-01-15.md
# 生成可分享版本
/analytics/market --share
分析维度
1. 技能需求趋势分析
## 📈 Skill Demand Trends
基于所有导入JD的技能需求分析
### 🔥 Top 20 Most In-Demand Skills
┌──────────────────────┬────────┬────────┬────────┬────────┐
│ Skill │ Demand │ Growth │ Supply│ Gap │
├──────────────────────┼────────┼────────┼────────┼────────┤
│ 1. Python │ 92% │ +12% │ High │ 30% │
│ 2. JavaScript │ 88% │ +8% │ High │ 25% │
│ 3. React │ 82% │ +15% │ Med │ 40% │
│ 4. AWS │ 79% │ +18% │ Med │ 45% │
│ 5. Kubernetes │ 75% │ +25% │ Low │ 65% │
│ 6. TypeScript │ 74% │ +20% │ Med │ 35% │
│ 7. Go │ 68% │ +22% │ Low │ 58% │
│ 8. Distributed Systs │ 65% │ +10% │ Low │ 70% │
│ 9. Machine Learning │ 62% │ +28% │ Low │ 75% │
│ 10. GraphQL │ 58% │ +16% │ Med │ 30% │
│ 11. Docker │ 85% │ +5% │ High │ 20% │
│ 12. SQL │ 80% │ +3% │ High │ 15% │
│ 13. System Design │ 90% │ +8% │ Low │ 80% │
│ 14. CI/CD │ 72% │ +12% │ Med │ 35% │
│ 15. Microservices │ 68% │ +15% │ Med │ 40% │
│ 16. REST APIs │ 86% │ +2% │ High │ 10% │
│ 17. Testing │ 70% │ +6% │ Med │ 25% │
│ 18. Security │ 58% │ +14% │ Low │ 45% │
│ 19. Cloud Native │ 64% │ +20% │ Low │ 55% │
│ 20. Data Engineering │ 55% │ +18% │ Low │ 50% │
└──────────────────────┴────────┴────────┴────────┴────────┘
Demand: % of job postings requiring this skill
Growth: 6-month trend in demand
Supply: Relative availability of candidates
Gap: Demand vs. Supply gap (investment opportunity)
### 📊 Category Analysis
**Frontend**:
- React: 82% demand, +15% growth 🔥
- Vue.js: 35% demand, +8% growth
- Angular: 28% demand, -3% decline 📉
**Backend**:
- Python: 92% demand, +12% growth 🔥
- Node.js: 75% demand, +6% growth
- Go: 68% demand, +22% growth 🚀
- Java: 55% demand, -5% decline 📉
**Cloud**:
- AWS: 79% demand, +18% growth 🔥
- GCP: 52% demand, +25% growth 🚀
- Azure: 48% demand, +15% growth
**Data**:
- ML/AI: 62% demand, +28% growth 🚀
- Data Engineering: 55% demand, +18% growth
- Analytics: 48% demand, +12% growth
**DevOps**:
- Kubernetes: 75% demand, +25% growth 🚀
- Docker: 85% demand, +5% growth
- CI/CD: 72% demand, +12% growth
### 💡 Skill Investment Recommendations
**High ROI Skills** (High demand + High growth + Low supply):
1. 🚀 Kubernetes (+25% growth, 65% gap)
2. 🚀 Machine Learning (+28% growth, 75% gap)
3. 🚀 Go (+22% growth, 58% gap)
4. 🚀 GCP (+25% growth, 55% gap)
5. 🔥 React (+15% growth, 40% gap)
**Saturated Skills** (High demand + High supply):
⚠️ JavaScript, Python, SQL, Docker
→ Good to have, but competitive
**Declining Skills**:
📉 Angular (-3%), Java (-5%), jQuery (-15%)
→ Consider deprioritizing
### 🎯 Your Skill Positioning
Your Top Skills vs Market:
┌───────────────────┬────────┬────────┬────────┐
│ Your Skill │ Your │ Market │ Compet-│
│ │ Level │ Demand │ itive │
├───────────────────┼────────┼────────┼────────┤
│ Python │ Expert │ 92% │ ✅ │
│ Distributed Sys │ Advan │ 65% │ ✅ │
│ AWS │ Advan │ 79% │ ✅ │
│ System Design │ Inter │ 90% │ Gap ⚠️│
│ Kubernetes │ Basic │ 75% │ Gap ⚠️│
│ Go │ None │ 68% │ Gap 🔴│
│ Machine Learning │ Basic │ 62% │ Gap 🟡│
└───────────────────┴────────┴────────┴────────┘
Competitive Advantage:
✅ Strong in high-demand areas (Python, AWS)
✅ Differentiated in distributed systems
⚠️ Gap in Kubernetes (critical)
🔴 Missing Go (significant opportunity)
Quick Wins (1-2 months):
1. Kubernetes: Intermediate → Advanced
2. Machine Learning: Basic → Intermediate
Medium-term (3-6 months):
1. Go: None → Intermediate
2. System Design: Intermediate → Advanced
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 773 lines · 0 tokens per session scan A e1bfb5e90dde
market is a command published in the GitHub repository huifer/claude-code-interview (23 stars, last pushed 7mo ago), licensed MIT. It adds 23 tokens to every session and 6,346 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
feature
Post a feature to the Features Board on production and write tweets.
seo-geo
SEO/GEO end-to-end along the SITE loop: survey demand and competitors, implement content, tune quality/tech/on-page, and evaluate authority/rankings/reports/memory (--phase survey|implement|tune|evaluate). Not sure? Use /aaron-marketing:auto.
email-audit
Full-spectrum email audit — technical rendering issues (Phase 1, Email Designer) and copy/strategy critique (Phase 2, Email Copywriter). Accepts pasted HTML or a description of an existing email.
competitive-landscape-analyzer
Comprehensive competitive analysis combining deep research with ad platform data. Includes competitor profiling, positioning maps, spend estimation, creative analysis, and strategic recommendations. Integrates Google Ads auction insights and Meta Ad Library research. (requires Pro subscription).
review-video
Make a " Reviews" video — a fast, faceless VO montage of REAL, verified competitor reviews that names the recurring complaints and positions YOUR business as the alternative, then hands off to your own customer testimonials.
client-report
Produce the full client-facing first-scan pitch report for a prospect, end to end — live scan, research pass, branded PDF.