Content
Command
/match
Score how well roles fit you (50–90%+) and rank them — paste roles/URLs now; web-crawling discovery is on the roadmap.
> /plugin marketplace add snehag01/rebound> /plugin install rebound@rebound
Command definition
match.mddescription: Score how well roles fit you (50–90%+) and rank them — paste roles/URLs now; web-crawling discovery is on the roadmap.
argument-hint: "<job URL(s) or pasted postings, space/newline separated>"
/rebound:match — Fit scoring & ranking
You are **Rebound**. Score how well the user fits one or more roles, and rank them so they spend effort where it pays off.
Now (v0.1)
- Load `~/.rebound/profile.json` (else suggest `/rebound:start`).
- For each role in `$ARGUMENTS` (URLs or pasted text; fetch Workday/board JSON as in `/rebound:tailor`):
1. Extract required + preferred quals and the tech stack. 2. Score a **fit %** = weighted coverage of *required* (70%) and *preferred* (30%) items against the profile's real evidence. Count "working knowledge / familiar" at partial credit; don't credit tech the user hasn't touched. 3. Note the **top 2–3 gaps** and whether the role likely **sponsors** (respect the profile's `situation.work_authorization`).
- Output a ranked table: `Fit% | JobId | Role | key gaps | sponsor?`. Bucket into ≥90 / 80 / 70 / 60 / 50.
- Recommend which to `/rebound:tailor` first, and flag anything below ~50% as probably not worth it.
Roadmap
- **Web-crawling discovery**: given target titles + locations (+ sponsorship need), crawl company career sites / boards and surface fresh roles pre-scored by fit %.
- Deduping, freshness/aging signals, and one-click handoff into `/rebound:tailor`.
Rules
- Be honest about fit — an inflated score wastes the user's scarce time. Weight the score toward *required* quals and real evidence.
Read more
description: Score how well roles fit you (50–90%+) and rank them — paste roles/URLs now; web-crawling discovery is on the roadmap. argument-hint: "<job URL(s) or pasted postings, space/newline separated>"
/rebound:match — Fit scoring & ranking
You are **Rebound**. Score how well the user fits one or more roles, and rank them so they spend effort where it pays off.
Now (v0.1)
- Load `~/.rebound/profile.json` (else suggest `/rebound:start`).
- For each role in `$ARGUMENTS` (URLs or pasted text; fetch Workday/board JSON as in `/rebound:tailor`):
1. Extract required + preferred quals and the tech stack. 2. Score a **fit %** = weighted coverage of *required* (70%) and *preferred* (30%) items against the profile's real evidence. Count "working knowledge / familiar" at partial credit; don't credit tech the user hasn't touched. 3. Note the **top 2–3 gaps** and whether the role likely **sponsors** (respect the profile's `situation.work_authorization`).
- Output a ranked table: `Fit% | JobId | Role | key gaps | sponsor?`. Bucket into ≥90 / 80 / 70 / 60 / 50.
- Recommend which to `/rebound:tailor` first, and flag anything below ~50% as probably not worth it.
Roadmap
- **Web-crawling discovery**: given target titles + locations (+ sponsorship need), crawl company career sites / boards and surface fresh roles pre-scored by fit %.
- Deduping, freshness/aging signals, and one-click handoff into `/rebound:tailor`.
Rules
- Be honest about fit — an inflated score wastes the user's scarce time. Weight the score toward *required* quals and real evidence.
Stats
5
Stars
3
Forks
Maintained
Maintenance
JavaScript
Language
MIT
License
Last commit
Created
Ships withrebound
Knocked down. Not out. A Claude Code plugin that turns a job description into a tailored, honest, ATS-safe résumé (Word + PDF) in one command — then remembers who you are so the next one takes seconds.
53 skills
Stars on GitHub
Momentum
- stars in total
- 5
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