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 snehag01/rebound --skill profile-memorygit clone --depth 1 https://github.com/snehag01/reboundWrote 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/snehag01/rebound/profile-memory)<a href="https://agentmods.dev/skills/snehag01/rebound/profile-memory"><img src="https://agentmods.dev/badge/skills/snehag01/rebound/profile-memory/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/snehag01/rebound/profile-memory"><img src="https://agentmods.dev/badge/skills/snehag01/rebound/profile-memory.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 | $0.00045 | $0.00631 |
| Opus 5.5 | $0.00018 | $0.00252 |
| Sonnet 5.5 | $0.00009 | $0.00126 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
profile-memory 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 1mo 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.
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.
Profile & Memory
Rebound persists one profile per machine so the user never re-enters their details. It is the reusable base for every /rebound:tailor, /rebound:match, and /rebound:rise.
Location & format
~/.rebound/profile.json— machine-readable (authoritative).~/.rebound/profile.md— human-readable mirror (keep in sync on every write).~/.rebound/pylibs/— bundled python deps (python-docx).
Create ~/.rebound/ if missing. On updates, rewrite both files and confirm.
Schema
{
"name": "", "contact": "", "current_title": "", "years_experience": "",
"base_resume_path": "",
"primary_stack": [], "secondary_stack": [], "differentiators": [],
"roles": [{"title": "", "org": "", "location": "", "dates": "", "bullets": []}],
"education": [], "certifications": [],
"situation": {
"work_authorization": null, // high-level only: "no sponsorship needed" | "will need sponsorship" | null
"timeline": null, // absolute dates + runway, e.g. "authorization/sponsorship deadline ~2026-08-15; ~60 days"
"target_roles": [], "locations": [], "work_mode": null, "notes": null
},
"preferences": {
"framing_notes": [
"fast learner across stacks; strong OOP/systems fundamentals",
"surface secondary skills but never above primary stack",
"honesty-first: never claim expertise in unused tech"
]
}
}
The private "situation" — handle with care
This block is sensitive and personal. Rules:
- Local only. Never transmit it to any external service or include it in a resume/PDF.
- Don't echo the values unless the user explicitly asks to see them.
- Use it to serve the user: set urgency from
timeline; respectwork_authorizationwhen judging whether a role must sponsor; prioritize accordingly. - Be supportive — this is often stressful, time-boxed information. Never pressure or judge.
Reasoning with the profile
primary_stackranks first in every tailored resume;secondary_stackis surfaced only when a JD needs it.differentiatorslead in summaries and referral notes.framing_notesare defaults applied during tailoring; the user can edit them via/rebound:profile.- Keep the raw
roles[].bulletsverbatim as the truth source; tailoring re-words copies of them, never the stored originals.
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.
- 1mo ago First seen · 52 lines · 45 tokens per session scan A b8d85765ef7c
profile-memory is a skill published in the GitHub repository snehag01/rebound (5 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 631 once invoked, about $0.0002 per session on Opus 5.5. 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
freehire-job-search
Use when finding jobs for someone — searching or filtering the freehire catalogue by keyword, region, seniority, skills, salary or company; reading a posting or a company's open roles; discovering what values a filter accepts; reading the saved job-search profile before asking someone what they want; handing over a…
freehire-mail-triage
Use when sorting a job seeker's application mail into the freehire tracker from their own mail client (himalaya, mbsync, notmuch, the Gmail API, any IMAP) — pushing a fetched batch, judging what each message is, linking it to an application, advancing a stage from a reply, or draining the two queues the matcher fills…
freehire-tailor-cv
Use when reframing someone's CV toward a specific vacancy through freehire — reading the fit analysis for a tailored copy, fetching or editing the CV document by path, rendering the ATS PDF, or deciding what may and may not be written into a CV. Carries the evidence rule: anything stating what the candidate DID needs…
freehire-market-fit
Use when measuring a set of skills — a CV's stack, or one skill on its own — against live open-vacancy demand on freehire; when telling a candidate which in-demand skills they are missing and what learning each would unlock; or when gauging how much of a filtered market (a role, a region, a seniority) their current…
freehire-track-applications
Use when recording or reviewing what someone applied to on freehire — marking a job applied, bookmarking one for later, moving an application through its stages (applied through offer, or rejected/withdrawn), attaching a note, or listing what they are tracking. Also covers reporting a posting that never answered them…
agent-memory
Repo-local file memory under .scaffolding/ (shared/agent/conversation tiers). TRIGGER when: reading or writing .scaffolding/ memory files, or choosing which file tier an insight belongs in. SKIP: vector memory / MCP search and store (use semantic-memory-mcp); distilling conversations (use distill).