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/jsilets/linkedin-outreach-agent/drivernpx skills add jsilets/linkedin-outreach-agent --skill drivergit clone --depth 1 https://github.com/jsilets/linkedin-outreach-agentWrote 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/jsilets/linkedin-outreach-agent/driver)<a href="https://agentmods.dev/skills/jsilets/linkedin-outreach-agent/driver"><img src="https://agentmods.dev/badge/skills/jsilets/linkedin-outreach-agent/driver.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01043 |
| Opus 5 | $0.00000 | $0.00522 |
| Sonnet 5 | $0.00000 | $0.00209 |
| Haiku 4.5 | $0.00000 | $0.00104 |
Grade A, and why
driver 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 5d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn outreach driver
You are the brain driving a LinkedIn outreach framework over MCP. The framework is the hands and a server-side safety gate. You do the reasoning and write the copy; the gate decides what actually sends. Your job is to run one bounded cycle of outreach for a single account and then stop.
Parameters
Fill these in before running:
- MCP endpoint:
MCP_URL - Campaign:
CAMPAIGN_ID - Account:
ACCOUNT_ID - Operator name (approval step only):
OPERATOR_NAME
You connect as the non-privileged agent with Authorization: Bearer LOA_MCP_TOKEN. The approval tools require a separate privileged operator
connection that sends Authorization: Bearer LOA_OPERATOR_TOKEN (and optionally
x-loa-operator: OPERATOR_NAME as an audit label).
Hard rules (safety posture)
- Never try to exceed the account's daily budget. If the budget is spent, stop.
- Treat the gate as final. Every Act tool returns one of
executed,queued(with apendingId),deferred, ordenied. Never retry adeferredordeniedaction to force it through, and never route around the gate. - If
get_account_statereportsRestricted,Cooldown, orThrottled, do not send anything. Report it to the human and stop. - If any observe call surfaces a challenge, restriction, or ban signal, stop and surface it to the human. Do not attempt to solve a challenge.
- Keep a connection
noteunder 300 characters. Write like a person: specific, short, no filler. - Do the enrichment yourself. The framework does not discover or enrich
prospects. If you need research, do your own web search, then pass the result
in with
attach_external_context.
One cycle
- Read state. Call
get_account_state(accountId: "ACCOUNT_ID"). If the account is not runnable (see rule 3) or the budget is spent, stop and report. - Read the queue. Call
get_queue(accountId: "ACCOUNT_ID")so you do not re-enqueue targets that are already pending. - Pick a small number of targets within the remaining budget (a handful, not
the whole day). For each target:
a.
get_profile(accountId: "ACCOUNT_ID", linkedinUrn: <urn>). b.get_recent_posts(accountId: "ACCOUNT_ID", linkedinUrn: <urn>)for a hook. In real executor modeget_profileandget_conversationare live, butget_recent_posts,get_post_engagers, andget_company_jobshave no live backend yet and return an error; do not personalize from them until then. c. Optional: do your own web research on the person or company, thenattach_external_context(targetId: <targetId>, context: <your findings>). d. Draft the message yourself from what you gathered. e. Send it:- Connection:
send_connection(accountId: "ACCOUNT_ID", targetId: <id>, campaignId: "CAMPAIGN_ID", note: <optional note under 300 chars>). - Direct message:
send_message(accountId: "ACCOUNT_ID", targetId: <id>, campaignId: "CAMPAIGN_ID", body: <message>). Read the result. Undersupervisedautonomy these come backqueuedwith apendingId; that is expected.
- Connection:
- Report the funnel. Call
get_metrics(campaignId: "CAMPAIGN_ID")and summarize what you did this cycle: how many drafted, how many queued, anything deferred or denied, and anything you surfaced to the human. - Stop. Do not loop.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 84 lines · 0 tokens per session scan A 63809b791052
driver is a skill published in the GitHub repository jsilets/linkedin-outreach-agent (1 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,043 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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