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 SELAT-AI/selat-skills --skill find-twitter-influencersgit clone --depth 1 https://github.com/SELAT-AI/selat-skillsWrote 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/selat-ai/selat-skills/find-twitter-influencers)<a href="https://agentmods.dev/skills/selat-ai/selat-skills/find-twitter-influencers"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/find-twitter-influencers/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/selat-ai/selat-skills/find-twitter-influencers"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/find-twitter-influencers.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00140 | $0.02206 |
| Opus 5.5 | $0.00056 | $0.00882 |
| Sonnet 5.5 | $0.00028 | $0.00441 |
| Haiku 4.5 | $0.00014 | $0.00221 |
Grade A, and why
find-twitter-influencers 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 22d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
find-twitter-influencers
When To Use
Use this skill to discover and rank public Twitter/X influencer candidates for one known company and one defined niche. It combines:
- company context from a name and matching domain;
- independent web roundups that mention relevant creators;
- Twitter account search for profile-level candidates; and
- current Twitter topic search for active authors and observed engagement.
The result is a research shortlist, not an outreach list. It does not purchase email addresses or phone numbers, message anyone, follow accounts, or post on X. If the user later selects a candidate and has a legitimate need for business contact enrichment, treat that as a separate workflow with a fresh quote, privacy review, and explicit approval.
Do not use this five-call bundle for one known account or one simple Twitter
search. Use twitter-research or a smaller discovered endpoint instead.
Workflow
-
Install the vetted recipe:
selat skill install find-twitter-influencers -
Collect all five inputs and confirm they describe one coherent campaign:
company: company or brand name;domain: bare domain for the same company;webQuery: independent roundup/listicle search for the niche;userQuery: concise Twitter account-search keywords; andtweetQuery: current topic query, optionally using X search operators.
The queries should target the same audience and niche as the company. Do not silently reuse placeholder fintech, AI, or company data for another request.
-
Probe all five payment challenges for free:
selat skill verify ~/.config/selat/skills/find-twitter-influencers \ --company "Acme Pay" \ --domain acme.com \ --webQuery "best fintech payments Twitter X creators to follow" \ --userQuery "fintech payments" \ --tweetQuery "(fintech OR payments) min_faves:50 lang:en" \ --live-probe -
Show every live quote, the expected cumulative total, and the sum of the per-step caps. Propose a cumulative session budget no higher than that cap sum and wait for explicit approval. Verification without
--payis free.
What ships with it
3 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.
- 22d ago Changed · +114 lines · +10 tokens per session 3a9c996442d3
- 1mo ago Changed de5c20cd5b4c
- 1mo ago First seen · 87 lines · 130 tokens per session scan A ea8c2a17bc20
find-twitter-influencers is a skill published in the GitHub repository SELAT-AI/selat-skills (2 stars, last pushed today), licensed Apache-2.0. It adds 140 tokens to every session and 2,206 once invoked, about $0.0006 per session on Opus 5.5. 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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