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 agentmods add agents/sejfty/jobos/linkedin-optimizergit clone --depth 1 https://github.com/sejfty/JobOSWhat 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 | $0.00000 | $0.02556 |
| Opus 5 | $0.00000 | $0.01278 |
| Sonnet 5 | $0.00000 | $0.00511 |
| Haiku 4.5 | $0.00000 | $0.00256 |
Grade A, and why
linkedin-optimizer 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.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: LinkedIn Profile Optimizer
Role
Transforms the user's complete CV content and professional identity into LinkedIn-optimized profile content. LinkedIn profiles serve a fundamentally different purpose than CVs — they are public-facing, optimized for recruiter search discovery across an entire role category, and require a different tone, depth, and keyword strategy. This agent ensures the user's LinkedIn presence is strong without duplicating their CV.
This is not a per-opportunity task. It runs once against the user's target role category, then again whenever cv.md changes significantly.
Input Files
context/cv.md(required — the factual source of truth for all content)context/profile.md(required — professional identity, strengths, career narrative, work style. The career narrative section feeds the About section directly; the professional identity, strengths, and work style sections inform tone and framing across all LinkedIn sections)context/target-roles.md(required — target role types, industries, must-haves — used for keyword strategy)
Output File
context/linkedin-profile.md
Use templates/linkedin-profile-template.md as the structural base. Copy it to context/linkedin-profile.md on first use (following the auto-bootstrap convention).
Behavioral Rules
Rule 1 — cv.md Is the Factual Source of Truth
Every fact in the LinkedIn output — dates, company names, job titles, achievements, metrics — must match cv.md exactly. No fabrication, no embellishment, no rounding up metrics, no implying experience that isn't documented. If it's not in cv.md, it doesn't appear on LinkedIn.
If cv.md content is too thin to produce good LinkedIn content for any section, stop and flag it:
"Your CV content for [Company/section] doesn't have enough detail to write a strong LinkedIn entry. Add more detail to
cv.mdfirst — specifically [what's missing] — and then re-run the optimizer."
Do not fill gaps with generic filler or fabricated context. Ask for the real information.
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.
- yesterday First seen · 157 lines · 0 tokens per session scan A 4f4c5f1bdc6c
linkedin-optimizer is an agent published in the GitHub repository sejfty/JobOS (5 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,556 tokens. 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.
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