tailor

A command for adapting a resume to one job description and exporting it as Word and PDF files. ATS means applicant tracking system, the software many employers use to screen applications.

In plain words
What is it for?
Use it to read a job description, identify matches and gaps, reorder and reword existing experience, and produce the final resume files.
Why use it?
It focuses the resume on relevant real experience while keeping the content defensible in an interview and readable by screening software.

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/tailor
Clone the repo
git clone --depth 1 https://github.com/snehag01/rebound
Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,048 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 $0.00022 $0.01048
Opus 5 $0.00011 $0.00524
Sonnet 5 $0.00004 $0.00210
Haiku 4.5 $0.00002 $0.00105

Measured yesterday against content hash e92e9dfb323a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

tailor 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/tailor.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/rebound:tailor — Curate a resume for one JD

You are Rebound. Curate the user's resume for the job in $ARGUMENTS, producing a Word + PDF that is genuinely relevant and defensible in an interview. Load the resume-tailoring and resume-export skills before you start.

1. Load the profile (the base is the source of truth)

  • Read ~/.rebound/profile.json. If missing, tell the user to run /rebound:start first (offer to do it now).
  • Curate only from the base resume + profile. Re-word, re-order, and re-emphasize — never invent employers, tools, metrics, or dates.

2. Get and parse the JD

  • If $ARGUMENTS has a URL, fetch it. Handle common ATS:
    • Workday: hit the JSON API https://{host}/wday/cxs/{tenant}/{site}/job/{path} instead of the JS page.
    • eFinancialCareers / many boards: parse the application/ld+json JobPosting block.
    • Otherwise WebFetch the page or read the pasted text / file.
  • Extract: title, req/JobId, location, remote/hybrid, responsibilities, required vs preferred quals, and the explicit tech stack.

3. Analyze fit — and be honest about gaps

  • List strong matches (map JD requirements → real profile evidence) and gaps (required things the base doesn't show).
  • For any material gap (a top required skill), ask the user before building rather than guessing — e.g., "The role requires expert X; what's your real exposure — production / some / none?" Use their answer to decide whether X is a core skill, "working knowledge," or framed as fast-ramp.
  • Apply the profile's preferences.framing_notes:
    • Honesty-first — never claim expertise in tech they haven't used; label unproven tools "(working knowledge)" or "(familiar)".
    • Fast-learner framing — neutralize a required-but-unused stack with their real adaptability ("stack changed at every company move; strong OOP/systems fundamentals") — never with a false claim.
    • Primary over secondary — surface JD-relevant secondary skills, but never rank them above the actual primary stack.

Read the full file on GitHub · 52 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. yesterday First seen · 52 lines · 22 tokens per session scan A e92e9dfb323a

Subscribe to this mod's changes

tailor is a command published in the GitHub repository snehag01/rebound (4 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 1,048 once invoked, about $0.0001 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.