claude-code-interview: Command for Claude Code

.claude/commands/weakness/plan.md

plan is a command for Claude Code from huifer/claude-code-interview. It costs 30 tokens per session (3,773 once invoked), scanned A, original, MIT.

A command for turning interview feedback and self-review into a structured plan to improve weak areas.

In plain words
What is it for?
Use it to plan improvement in technical skills, system design, behavior, or communication after an interview.
Why use it?
It organizes scattered feedback into specific learning resources, exercises, timelines, and success checks.

Command for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

This is huifer/claude-code-interview's own configuration. It tells Claude Code how to work on claude-code-interview itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything claude-code-interview configures →

Reuse

Borrowing it

Nothing to install: this file belongs to huifer/claude-code-interview. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/huifer/claude-code-interview/main/.claude/commands/weakness/plan.md
Clone the repo
git clone --depth 1 https://github.com/huifer/claude-code-interview

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for plan

README.md
[![agentmods](https://agentmods.dev/badge/commands/huifer/claude-code-interview/plan/github.svg)](https://agentmods.dev/commands/huifer/claude-code-interview/plan)
Your own site
<a href="https://agentmods.dev/commands/huifer/claude-code-interview/plan"><img src="https://agentmods.dev/badge/commands/huifer/claude-code-interview/plan/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for plan

Your own site · 80×15
<a href="https://agentmods.dev/commands/huifer/claude-code-interview/plan"><img src="https://agentmods.dev/badge/commands/huifer/claude-code-interview/plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,773 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00030 $0.03773
Opus 5.5 $0.00012 $0.01509
Sonnet 5.5 $0.00006 $0.00755
Haiku 4.5 $0.00003 $0.00377

Measured 2mo ago against content hash bc8671f9028b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.claude/commands/weakness/plan.md · 586 lines

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"
      ]
    }
  }
}

Read the full file on GitHub · 586 lines

Changes

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.

  1. 2mo ago First seen · 586 lines · 0 tokens per session scan A bc8671f9028b

Subscribe to this mod's changes

plan is a command published in the GitHub repository huifer/claude-code-interview (27 stars, last pushed 10mo ago), licensed MIT. It adds 30 tokens to every session and 3,773 once invoked, about $0.0001 per session on Opus 5.5. 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.

Related

plan compared with commands from other repositories

plan next to 6 commands from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
planhuifer/claude-code-interviewCommand2710mo agoMITA30Claude Code
learn-story-flowIntai/story-flowCommand162d agoMITA10Claude Code
start-10-1minicoohei/ai-agent-campCommand3471mo agononeA2Claude Code
start-0-8minicoohei/ai-agent-campCommand3471mo agononeA2Claude Code
setup-m365cli.esminicoohei/ai-agent-campCommand3471mo agononeA8Claude Code
prep-rolefourleafai/clover-publicCommand81mo agoMITA0Claude Code
setup-vercel.esminicoohei/ai-agent-campCommand3471mo agononeB2Claude Code