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 skills/styfinity/linkedin-engine/linkedin-voice-profilernpx skills add styfinity/linkedin-engine --skill linkedin-voice-profilergit clone --depth 1 https://github.com/styfinity/linkedin-engineWhat 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 | $0.00044 | $0.00546 |
| Opus 5 | $0.00022 | $0.00273 |
| Sonnet 5 | $0.00009 | $0.00109 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
linkedin-voice-profiler 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 3d 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Voice Profiler
Every skill in this engine writes in the user's voice. This skill builds that voice profile from their real words, not a guessed persona, so the rest of the motion sounds like them and not like generic AI.
Inputs
- A paste of 5 to 15 of the user's real posts and DMs, or a path to a file holding them: $ARGUMENTS
- The brief loads automatically. This skill writes the "## Voice profile" block back into it.
Do this
Read only the supplied samples. Extract, from the real text:
- Sentence rhythm. Short and punchy, long and rolling, or mixed. Note typical length and how lines break.
- Favourite verbs and recurring phrases. The words they reach for again and again.
- Contraction habits ("do not" vs "don't"), emoji habits (which, how often, or never), and swearing tolerance.
- Opening patterns and closing patterns. How they hook a post and how they sign off a DM.
- Register tiers. If the samples show it, separate the cold-outreach voice from the warm, replying-to-a-peer voice.
- Banned tells. The phrases and tics they never use, so other skills avoid them. Then compress all of it into a DO list, a DON'T list, and 3 verbatim sample lines lifted straight from the input.
Output
A ready-to-paste "## Voice profile" block for linkedin-brief.md: the DO list, the DON'T list, the cold-vs-warm register notes if found, and the 3 verbatim sample lines. End with a one-line note: paste this into linkedin-brief.md and the SessionStart hook will load it into every session after that, so /linkedin-humanizer and the copywriting skills write in this voice automatically.
Rules
- Build the profile from the user's REAL words only. Never invent a persona or borrow a style they did not write.
- If fewer than 5 usable samples are supplied, say so and ask for more rather than guessing.
- Capture register tiers (cold vs warm) only if the samples actually show two registers. Do not fabricate a second voice.
- Quote sample lines verbatim. Do not polish or rewrite them.
- No em-dashes.
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.
- 3d ago First seen · 34 lines · 44 tokens per session scan A 9e21fa8067cc
linkedin-voice-profiler is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 546 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.
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