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/dashboardgit 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 | $0.00030 | $0.05470 |
| Opus 5 | $0.00015 | $0.02735 |
| Sonnet 5 | $0.00006 | $0.01094 |
| Haiku 4.5 | $0.00003 | $0.00547 |
Grade A, and why
dashboard 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 — 701 lines — stays where its author put it; the contents beside it link to each section on GitHub.
分析仪表板
生成全面的可视化分析报告，帮助你了解求职进度和改进效果。
使用方法
生成基本仪表板
# 默认格式（Markdown）
/analytics/dashboard
# 指定输出格式
/analytics/dashboard html
/analytics/dashboard markdown
/analytics/dashboard pdf
# 指定时间范围
/analytics/dashboard --week
/analytics/dashboard --month
/analytics/dashboard --all
生成特定类型报告
# 只显示应用漏斗
/analytics/dashboard --funnel
# 只显示技能分析
/analytics/dashboard --skills
# 只显示面试表现
/analytics/dashboard --interviews
# 只显示进步趋势
/analytics/dashboard --trends
# 完整仪表板（所有部分）
/analytics/dashboard --full
导出报告
# 导出到文件
/analytics/dashboard --output exports/analytics/report-2024-01-15.html
# 在浏览器中打开（HTML格式）
/analytics/dashboard html --open
# 生成PDF报告
/analytics/dashboard pdf --output exports/analytics/report.pdf
仪表板组成部分
1. 应用漏斗可视化
## 📊 Application Funnel
应用进度追踪：
Researching (100)
▼
Applied (45) [45% conversion]
▼
Screening (12) [27% conversion]
▼
Interviews (5) [42% conversion]
▼
Offers (2) [40% conversion]
Overall Conversion Rate: 4.4%
2. 技能差距热图
## 🔥 Skill Gap Heatmap
技能对比矩阵：
Your Skills vs. Job Requirements
┌─────────────────────┬────────┬────────┬────────┬────────┐
│ Category │ Your │ Market │ Avg │ Gap │
│ │ Level │ Demand │ Req │ Score │
├─────────────────────┼────────┼────────┼────────┼────────┤
│ Python │ Expert │ 85% │ Inter │ +25 │
│ JavaScript │ Advan │ 78% │ Expert │ -15 │
│ Distributed Systems │ Inter │ 65% │ Expert │ -35 │
│ System Design │ Inter │ 82% │ Advan │ -20 │
│ Cloud (AWS/GCP) │ Advan │ 90% │ Expert │ -10 │
│ Machine Learning │ Basic │ 45% │ Inter │ -25 │
│ Kubernetes │ Basic │ 72% │ Inter │ -40 │
│ Go │ None │ 38% │ Basic │ -50 │
└─────────────────────┴────────┴────────┴────────┴────────┘
Legend:
+20+: Above requirements ✅
+0 to +20: Meets requirements 🟢
-0 to -20: Slight gap 🟡
-20 to -40: Moderate gap 🔴
-40+: Critical gap ⚠️
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 · 701 lines · 0 tokens per session scan A 5869c28147b3
dashboard is a command published in the GitHub repository huifer/claude-code-interview (23 stars, last pushed 7mo ago), licensed MIT. It adds 30 tokens to every session and 5,470 once invoked, about $0.0002 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.