match

A command for scoring and ranking job roles against a person's profile using required and preferred qualifications. Fit scoring compares the role's stated needs with documented experience.

In plain words
What is it for?
Use it with pasted job descriptions or URLs to extract requirements, calculate a fit percentage, identify gaps, assess likely sponsorship, and choose which role to tailor first.
Why use it?
It helps prioritize applications and shows the main gaps instead of treating every job as equally suitable.

Command

Install

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.

agentmods
npx agentmods add commands/snehag01/rebound/match
Clone the repo
git clone --depth 1 https://github.com/snehag01/rebound
Per session 32 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 415 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00032 $0.00415
Opus 5 $0.00016 $0.00208
Sonnet 5 $0.00006 $0.00083
Haiku 4.5 $0.00003 $0.00042

Measured yesterday against content hash 7da5f8fae13c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

match 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 yesterday.

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.

commands/match.md · 25 lines

What it actually says

/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.
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. yesterday First seen · 25 lines · 32 tokens per session scan A 7da5f8fae13c

Subscribe to this mod's changes

match is a command published in the GitHub repository snehag01/rebound (4 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 415 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.