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/plangit 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 | $0.03773 |
| Opus 5 | $0.00015 | $0.01886 |
| Sonnet 5 | $0.00006 | $0.00755 |
| Haiku 4.5 | $0.00003 | $0.00377 |
Grade A, and why
plan 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 — 586 lines — stays where its author put it; the contents beside it link to each section on GitHub.
生成弱点改进计划
分析面试表现和技能差距，生成系统化的改进计划。
使用方法
基于最近面试生成计划
/weakness/plan
# 将使用最近一次面试的数据
基于特定面试生成计划
/weakness/plan google-round1
# 使用特定面试的反馈数据
聚焦特定领域
/weakness/plan --technical
# 只关注技术弱点
/weakness/plan --behavioral
# 只关注行为面试弱点
/weakness/plan --communication
# 只关注沟通弱点
分析维度
1. 技术弱点分析
基于面试反馈分析：
算法和数据结构
{
"weakness": {
"category": "Algorithms",
"issues": [
{
"problem": "DP问题优化思路不清晰",
"frequency": "出现3次",
"severity": "major",
"impact": "未能通过 coding round"
}
],
"root_cause": "缺乏系统练习，DP模式掌握不足",
"improvement_plan": {
"resources": [
"LeetCode DP专题 - 50题",
"《算法导论》动态规划章节",
"TechLead DP模式视频"
],
"practice_plan": [
"Week 1-2: 基础DP模式 (Fibonacci, Climbing Stairs)",
"Week 3-4: 背包问题系列",
"Week 5-6: LCS, LIS系列",
"Week 7-8: 综合问题"
],
"success_criteria": "连续10道DP题 >80%正确率"
}
}
}
系统设计
{
"weakness": {
"category": "System Design",
"issues": [
{
"problem": "Scalability考虑不足",
"examples": ["未考虑数据分片", "缺少缓存策略"],
"severity": "critical",
"improvement_needed": "深入学习大规模系统设计"
}
],
"resources": [
"DDIA - Designing Data-Intensive Applications",
"System Design Primer",
"Google SRE书籍"
]
}
}
2. 沟通弱点分析
清晰度
{
"communication": {
"clarity": {
"current_level": "中等",
"issues": [
"解释问题时思路跳跃",
"缺少结构化表达",
"术语使用不一致"
],
"improvement_plan": {
"techniques": [
"使用STAR方法组织答案",
"先给出概述再深入细节",
"使用First, Next, Finally结构"
],
"practice": [
"每天口头解释一个算法问题",
"录制自己的回答并回听",
"与练习伙伴mock interview"
],
"timeline": "2-3周可见改善"
}
}
}
}
主动沟通
{
"communication": {
"proactive": {
"issues": [
"等待interviewer提示",
"不主动clarify需求",
"缺少中间checkpoints"
],
"best_practices": [
"开始前确认问题理解",
"每5分钟口头说明思路",
"遇到歧义立即提问",
"主动讨论trade-offs"
]
}
}
}
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 · 586 lines · 0 tokens per session scan A bc8671f9028b
plan 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 3,773 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.