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-objection-handlernpx skills add styfinity/linkedin-engine --skill linkedin-objection-handlergit 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 | $0.00051 | $0.00513 |
| Opus 5 | $0.00026 | $0.00257 |
| Sonnet 5 | $0.00010 | $0.00103 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
linkedin-objection-handler 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.
What it actually says
LinkedIn Objection Handler
An objection is a question wearing a no. This skill answers it directly, reframes to the cost of staying still, and moves the thread one step toward a call.
Inputs
- The objection as the prospect wrote it, plus your honest answer to it: $ARGUMENTS
- The brief (offer, pains, proof, voice profile) loads automatically.
Do this
- Name the real concern underneath the words. Most objections are one of five: price, timing, trust, fit, or "we already do this". Say which one this is and why.
- Answer it first and directly. No dodging, no pivot before the answer lands. Use the operator's honest answer from the input as the spine.
- Reframe to the cost of the status quo. What does doing nothing keep costing them, in their own metric (leads, pipeline, hours, missed revenue)?
- Advance to a call. Offer a small, specific next step (a short call, a look at their numbers). Keep it conditional and low-pressure.
- Match the tone to the thread's warmth: cold gets shorter and lighter, warm can carry more substance.
Output
The reframe message, ready to paste, tier-matched to the thread's warmth. Then a one-line note stating the concern you actually addressed (price / timing / trust / fit / already-do-this).
Rules
- Answer-first, always. Never evade the objection or change the subject before it is handled.
- Never discount to win. Price objections get reframed against the cost of the status quo, not knocked down.
- Advance to a call, do not hard-close in the text. Sending happens later, through the connected CLI/MCP layer, capped at 20 actions/day for new accounts.
- Draft only. Claude drafts, the operator approves, and only then sends.
- No em-dashes. Reads like a peer who respects the prospect's pushback.
- If the reply has run hot and is ready to book, hand it to /linkedin-reply-triager. Run /linkedin-humanizer on the draft before sending.
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 · 32 lines · 51 tokens per session scan A d7b9820a203b
linkedin-objection-handler is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 513 once invoked, about $0.0003 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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