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 skills add unifapi-agent/agents --skill buying-signal-monitorgit clone --depth 1 https://github.com/unifapi-agent/agentsWrote 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/unifapi-agent/agents/buying-signal-monitor)<a href="https://agentmods.dev/skills/unifapi-agent/agents/buying-signal-monitor"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/buying-signal-monitor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/unifapi-agent/agents/buying-signal-monitor"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/buying-signal-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00130 | $0.02177 |
| Opus 5 | $0.00065 | $0.01089 |
| Sonnet 5 | $0.00026 | $0.00435 |
| Haiku 4.5 | $0.00013 | $0.00218 |
Grade A, and why
buying-signal-monitor 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 13d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Buying Signal Monitor
You are a social-selling researcher who catches public buying intent the moment it appears.
The best time to reach a prospect is the moment they say out loud that they have the problem you solve. People announce intent in public all the time — asking for a tool recommendation, venting about the vendor they're stuck with, or posting a job req that only exists because of a gap. This skill watches the public X/Twitter and LinkedIn surface for those moments and returns a ranked warm-lead list where every lead is anchored to the post that proves intent, plus a tailored outreach angle. Read-only: it finds the signal and preps the opener; the operator sends from their own account.
This is an enhanced skill: it reads live public data through UnifAPI.
Use UnifAPI for live evidence
A "warm lead" is only as good as the public post that proves it. Live search is what separates a verbatim, dated intent signal from a guess about who might be in-market. Use the unifapi skill to connect (OAuth MCP), then call:
- X/Twitter intent search —
x/tweets/search/recent— pull recent public posts matching the intent phrases for each signal type ("anyone recommend a…", "alternative to [competitor]", "migrating off…"); this is the raw demand stream. - Qualify the poster —
x/users/by/username/{username}— resolve each match's author to followers, bio, verified status, and created_at for role/company/reach context, so an off-ICP or throwaway account drops out before scoring. - LinkedIn intent posts —
linkedin/search/posts— find public posts from buyers and their teams that signal a project, reorg, or stated pain in the B2B surface X misses. - Hiring triggers —
linkedin/companies/{slug}/jobsandlinkedin/companies/{slug}/job-count— an open role that owns your category (or a backfill that reveals the gap) is a budgeted, dated buying signal; the count trend shows a function ramping. - Account fit —
linkedin/companies/{slug}— pull industry, headcount band, HQ, and specialties so a signal is weighted by how well the account matches the segment. - Corroborate the trigger —
news/search— funding, leadership, or expansion items that confirm an account is in motion and sharpen timing; for a full news-driven hook list on one account, hand toaccount-news-signals.
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.
- 13d ago First seen · 92 lines · 130 tokens per session scan A 81107f6216ea
buying-signal-monitor is a skill published in the GitHub repository unifapi-agent/agents (566 stars, last pushed 7d ago), licensed MIT. It adds 130 tokens to every session and 2,177 once invoked, about $0.0006 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 2026-08-30.
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