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 skills add malue-ai/dazee-small --skill job-application-optimizergit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote 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.
[](https://agentmods.dev/skills/malue-ai/dazee-small/job-application-optimizer)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/job-application-optimizer"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/job-application-optimizer/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.
<a href="https://agentmods.dev/skills/malue-ai/dazee-small/job-application-optimizer"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/job-application-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.00625 |
| Opus 5 | $0.00016 | $0.00313 |
| Sonnet 5 | $0.00006 | $0.00125 |
| Haiku 4.5 | $0.00003 | $0.00063 |
Grade A, and why
job-application-optimizer 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 8d 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.
What it actually says
求职申请优化
全流程求职辅助：分析职位描述、优化简历、生成求职信、准备面试问题、模拟面试。
使用场景
- 用户说「帮我分析这个职位」「优化我的简历」「帮我准备面试」
- 用户要投递简历，需要针对职位描述调整
- 用户即将面试，需要准备常见问题和模拟练习
执行方式
直接使用 LLM 能力完成，无需额外工具。
1. JD 分析
分析职位描述，提取关键信息：
- 核心要求：必备技能、经验年限、学历
- 加分项：优先但非必须的技能
- 隐含要求：从描述语气和措辞推断的团队文化、工作风格
- 关键词：ATS（简历筛选系统）可能匹配的关键词
2. 简历优化
根据 JD 分析结果，优化简历：
- 关键词匹配：确保简历包含 JD 中的核心关键词
- 经验重排：将最相关的经验放在最前面
- 量化成果：将模糊描述改为具体数据（「提升了效率」→「效率提升 30%」）
- 删减无关内容：去掉与目标职位无关的经验
3. 求职信生成
根据 JD 和简历，生成针对性求职信：
- 开头抓住注意力（不要「我看到贵公司招聘…」）
- 用 1-2 个具体案例展示匹配度
- 结尾表达热情但不谄媚
4. 面试准备
生成可能的面试问题及参考回答：
- 行为面试题：STAR 法则回答模板
- 专业题：基于 JD 要求的技术/业务问题
- 反问环节：给面试官的高质量提问
5. 模拟面试
以面试官角色进行模拟面试：
- 逐个提问，等用户回答
- 给出即时反馈（亮点和改进建议）
- 模拟完后给出总结评分
输出规范
- JD 分析用表格展示，一目了然
- 简历修改用对比格式（原文 → 优化后）
- 面试问题按难度分级
- 模拟面试保持自然对话节奏，不要一次性输出所有问题
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.
- 8d ago First seen · 74 lines · 32 tokens per session scan A 67f3fb730e72
job-application-optimizer is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 32 tokens to every session and 625 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…