Hisn00w/ASu-skills

ASu‑skills – a plug‑in suite for Chinese job‑search workflows

What it is – A collection of “skills” (plug‑ins) that can be installed into a handful of AI‑coding assistants (Claude Code, Codex, DeepSeek Harness Desktop, Cursor, TraeWork, OpenCode, WorkBuddy, Qoder, etc.). Each skill provides a self‑contained command‑line‑style entry point (e.g. /contributor, /great‑resume) that helps a user move through a typical tech‑job‑hunting process.

Why it matters – The repo does not train or run AI models. Instead it orchestrates existing LLM‑powered agents to:

1. Find and make verifiable open‑source contributions.
2. Turn AI‑generated code chats into a structured “evidence chain”.
3. Generate project‑learning guides and interview‑style summaries.
4. Refine and format a résumé (HTML, PDF, optional LaTeX) with many ready‑made Chinese templates.
5. Match a résumé against a job description and highlight gaps.
6. Auto‑fill a single job‑application web form via a browser‑bridge (Kimi WebBridge or similar).
7. Conduct interview‑question drills and track which claims have been verified.
8. Keep a small “offer tracker” table that logs each application’s status.

All nine steps are designed to work together, passing a claim‑evidence ledger from one skill to the next so the user can prove that every résumé bullet is backed by a real contribution or measurable result.

How to use it – Install the plug‑in on the supported AI platform, then invoke the desired entry, for example:

/contributor   # find a GitHub issue, fork, submit a PR, and record the diff
/great-resume  # rewrite the experience for a target role, add HR opening line
/make-resume   # output an editable HTML résumé (or LaTeX) and export PDF
/job-apply     # open a recruitment page, fill fields, stop before final submit

The README provides detailed prompts, example dialogues, and a clear file‑structure (registry JSON, skills/ folders, assets, reference docs).

Target audience – Chinese‑speaking software engineers, AI‑application developers, or anyone preparing for technical interviews who wants a systematic, evidence‑based résumé workflow powered by LLM agents.

License – MIT, fully open‑source.

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All information above is taken directly from the repository’s README; no external assumptions have been added.