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 twitter-researchgit 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/twitter-research)<a href="https://agentmods.dev/skills/selat-ai/selat-skills/twitter-research"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/twitter-research/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/twitter-research"><img src="https://agentmods.dev/badge/skills/selat-ai/selat-skills/twitter-research.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 | $0.00167 | $0.01875 |
| Opus 5 | $0.00084 | $0.00937 |
| Sonnet 5 | $0.00033 | $0.00375 |
| Haiku 4.5 | $0.00017 | $0.00187 |
Grade A, and why
twitter-research 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
twitter-research
A read-only research toolkit for Twitter/X, backed by SELAT's own first-party
Twitter API (catalog.selat.ai). The manifest lists 9 curated GET endpoints
as a priced menu (~$0.001 each); the agent picks the ones a given request
actually needs. No API keys, keyless pay-per-call.
When To Use
Use for read/lookup tasks on X: profiling an account, pulling someone's recent posts, seeing who mentions or follows them, analyzing how a specific tweet landed (replies + retweeters), searching for a topic/keyword, or checking regional trends. Read-only — it does not post, like, or follow, and cannot see protected/private accounts.
Workflow
This is a menu, not a pipeline. Do NOT run all 9 steps. Select only the endpoint(s) the request needs, tell the user the cost, then invoke just those. A blind
selat skill runwould pay for all 9 (~$0.009) and return mostly irrelevant data — always drive selection from the intent below.
Route the request to the smallest set of endpoints, then report back in plain language. Confirm cost before spending — "this is N SELAT-native Twitter reads, about $0.00N — go ahead?"
| The user wants… | Run | Params |
|---|---|---|
| Who is @X / their profile & authority | user/info |
handle |
| What X has posted lately | user/last_tweets |
handle |
| Who's talking to/about X (reach) | user/mentions |
handle |
| X's audience / follower sample | user/followers |
handle |
| A profile deep-dive | user/info + user/last_tweets (+ user/mentions) |
handle |
| What people are saying about a topic | tweet/advanced_search |
query |
| Is a topic trending / regional trends | trends |
woeid |
| Topic monitor (chatter + trending) | tweet/advanced_search + trends |
query, woeid |
| A specific tweet's content & counts | tweets |
tweetId |
| How a tweet was received | tweets + tweet/replies + tweet/retweeters |
tweetId |
- Escalate only as needed: start with the one endpoint that answers the ask; add
a second (e.g.
user/mentionsafteruser/info) only if the user wants more. advanced_searchaccepts the full X operator surface inquery(from:,to:,#tag,$CASHTAG,min_faves:,since:/until:,lang:en).- Then synthesize for the user in plain language — the answer, not the JSON. Keep endpoint URLs, wallet addresses, and tweet IDs out of what you relay; lead with the finding and the dollar cost.
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 · 120 lines · 167 tokens per session scan A e38c6ee9c12f
twitter-research is a skill published in the GitHub repository SELAT-AI/selat-skills (2 stars, last pushed today), licensed Apache-2.0. It adds 167 tokens to every session and 1,875 once invoked, about $0.0008 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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