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 social-intelgit 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/social-intel)<a href="https://agentmods.dev/skills/selat-ai/selat-skills/social-intel"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/social-intel/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/social-intel"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/social-intel.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 | $0.00114 | $0.01259 |
| Opus 5 | $0.00057 | $0.00629 |
| Sonnet 5 | $0.00023 | $0.00252 |
| Haiku 4.5 | $0.00011 | $0.00126 |
Grade A, and why
social-intel 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 11d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
social-intel
Grounded web-context intelligence on any topic, brand, or account. The skill runs two independent web searches — Exa (neural, MPP on Tempo) and Tavily (advanced, x402 on Base) via the SELAT Router (x402) — and the agent fuses them into a single cited brief, cross-checking the two sources and flagging claims only one of them makes.
When To Use
Use when the user wants a grounded, corroborated web read on a topic, brand, or product — not a single-source lookup. The value is in cross-checking two distinct retrieval methods (Exa's neural/semantic search vs Tavily's aggregation) so the brief is corroborated rather than dependent on one engine. Every API call is a paid x402 service; the agent does the ranking, sentiment read, and synthesis around the paid data.
Rails
Both steps are native x402, via the SELAT Router through the SELAT Router (rail: via the SELAT Router):
- x402 on Base: Exa web search (
api.exa.ai) — resolves asx402 on Base. - x402 on Base: Tavily web search (
x402.tavily.com) — resolves asx402 on Base.
The selat CLI auto-detects each step's protocol at call time.
Workflow
- Install:
selat skill install social-intel - Run end-to-end:
selat skill run social-intel --topic "<topic>" - The CLI compiles each step into a
selat-paycall and prints each result.
Recommended agent procedure:
- Ground the topic on the web — Exa
POST /search(x402 on Base, ~$0.007); returns ranked results with page text. - Corroborate the web read — Tavily
POST /search(x402 on Base, ~$0.011),search_depth: advanced. Cross-reference against Exa; flag claims only one source makes, and prefer sources both engines surface.
Then synthesize: the dominant themes, the strongest sources, and where the two engines agree or diverge — with source URLs.
Inputs And Outputs
| Param | Required | Default | Description |
|---|---|---|---|
topic |
yes | agent payments |
Keyword/topic to search the web for (both engines). |
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.
- 11d ago First seen · 95 lines · 114 tokens per session scan A 89aac91ee2e5
social-intel is a skill published in the GitHub repository SELAT-AI/selat-skills (2 stars, last pushed today), licensed Apache-2.0. It adds 114 tokens to every session and 1,259 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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