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-reply-triagernpx skills add styfinity/linkedin-engine --skill linkedin-reply-triagergit 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 | $0.00048 | $0.00553 |
| Opus 5 | $0.00024 | $0.00277 |
| Sonnet 5 | $0.00010 | $0.00111 |
| Haiku 4.5 | $0.00005 | $0.00055 |
Grade A, and why
linkedin-reply-triager 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 2d 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.
What it actually says
LinkedIn Reply Triager
The inbox sorter. Replies are not equal. This skill reads each one, names what bucket it belongs in, and hands it to the right next skill so nothing rots and nothing gets a sales pitch it did not ask for.
Inputs
- One reply or a batch of replies, pasted in (include who said what if it is a thread): $ARGUMENTS
- The brief (persona, offer, pains) loads automatically.
Do this
For each reply, in order:
- Read the actual words, not the timing. A reply that came in slow can still be HOT; a fast one can be a brush-off.
- Classify into exactly one bucket: HOT, WARM, COLD, OBJECTION, WRONG-PERSON, or HOSTILE.
- Name the tell. Quote the one phrase or signal that put it in that bucket. One line.
- Route it:
- HOT (buying signal, asking how it works, wants a call) to /linkedin-sell-by-chat, or /linkedin-booking-closer if they are ready for a time.
- WARM (interested but not now, soft yes, "keep me posted") to /linkedin-followup-adapter.
- COLD (one-word, flat, polite-but-dead) to /linkedin-cold-reviver.
- OBJECTION (price, timing, trust, "already have someone") to /linkedin-objection-handler.
- WRONG-PERSON (not their call, not their remit) ask for the right contact, warmly.
- HOSTILE (annoyed, "stop messaging me") stop the sequence, no further outreach.
- Write a one-line suggested next message in the operator's voice.
Output
Per reply: the class, the tell (quoted), the route (the slash-skill or the action), and a one-line suggested next message. Label each clearly. Batch input gets one block per reply.
Rules
- Route every reply. No reply is left unclassified.
- Read the words, not a timer. The bucket comes from what they said.
- Nothing sends here. This skill only sorts and suggests; the routed skill drafts, the operator approves.
- No em-dashes. Use a comma, a period, or " - ".
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.
- 2d ago First seen · 37 lines · 48 tokens per session scan A 3e7852b5a1e7
linkedin-reply-triager is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 553 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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