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 | $0.00130 | $0.02088 |
| Opus 5 | $0.00065 | $0.01044 |
| Sonnet 5 | $0.00026 | $0.00418 |
| Haiku 4.5 | $0.00013 | $0.00209 |
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 12d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
find-twitter-influencers
When To Use
Use when the user wants a ranked, enriched list of Twitter/X influencers for a company, product, or niche — for partnerships, sponsorships, or outreach. The skill spans the full pipeline: company resolution, candidate discovery, Twitter profile + engagement pull, and contact enrichment. Every API call is a paid service — most MPP on Tempo through the SELAT Router, plus two x402 via Circle Gateway calls via the SELAT Router to SELAT-native for Twitter data; the agent does the parsing, scoring, and ranking around the paid data.
Workflow
- Install:
selat skill install find-twitter-influencers - Run:
selat skill run find-twitter-influencers --company "Acme Corp" [--domain acme.com] [--query "best fintech Twitter accounts to follow"] [--handle somehandle] [--linkedinUrl https://linkedin.com/in/...] [--firstName Jane --lastName Smith --contactDomain janesmithcreative.com] - The CLI compiles each step into a
selat-paycall, pays the per-step price (capped per step), runs the steps in order, and prints a per-step status summary.
Steps (MPP on Tempo via the SELAT Router unless marked x402 via Circle Gateway):
- Step 1 — Apollo
POST /apollo/org-search— resolve the company by name (q_organization_name) to get its Apollo org record, domain, and industry. - Step 2 — Abstract Company Enrichment
POST /abstract-company-enrichment/lookup— resolve by domain when one is supplied (richer firmographic context, industry/SIC fields). - Step 3 — Exa
POST /search— discover curated influencer listicles (requestcontents.textto parse handles from page text; x.com is NOT in Exa's index). - Step 4 — Exa
POST /findSimilar— expand from a strong listicle URL to find more lists. - Step 5 — SELAT-native (x402 via Circle Gateway)
GET /twitter/user/info?userName=— fetch a candidate's Twitter profile and follower counts. - Step 6 — SELAT-native (x402 via Circle Gateway)
GET /twitter/user/last_tweets?userName=— fetch recent tweets with engagement metrics. - Step 7 — Hunter
POST /hunter/email-finder— find an email by name + domain. - Step 8 — Clado
POST /clado/contacts— LinkedIn-URL-to-contact enrichment (email + phone); synchronous, no polling.
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.
- 12d 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 130 tokens to every session and 2,088 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 2026-08-31.
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