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 vlinx-io/VelaTerm --skill vaskgit clone --depth 1 https://github.com/vlinx-io/VelaTermWrote 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/vlinx-io/velaterm/vask)<a href="https://agentmods.dev/skills/vlinx-io/velaterm/vask"><img src="https://agentmods.dev/badge/skills/vlinx-io/velaterm/vask/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/vlinx-io/velaterm/vask"><img src="https://agentmods.dev/badge/skills/vlinx-io/velaterm/vask.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 | $0.00091 | $0.00478 |
| Opus 5.5 | $0.00036 | $0.00191 |
| Sonnet 5.5 | $0.00018 | $0.00096 |
| Haiku 4.5 | $0.00009 | $0.00048 |
Grade A, and why
vask 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 1mo 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.
What it actually says
vask
Use vask as the focused-question form of vrefer --ask. There is no separate shell command: invoke
the existing read-only command with the session reference and the user's question.
vrefer <session> --ask "<question>"
If the target is not known, search first:
vsearch <distinctive words...>
vrefer <id-from-search> --ask "<question>"
<session> accepts a full id, an id prefix of at least eight characters, an exact session name, or a
unique substring. Quote names containing spaces. If several sessions match, select an id from the
candidate list and retry rather than guessing.
By default the answering agent receives the complete transcript. If the user enabled Summarize first
in Settings → Agents, VelaTerm compresses the transcript with the one globally selected Agent, model,
and reasoning effort, adds relevant original full-text search excerpts from the same session, and then
asks the answering agent. This setting is automatic; the skill command and --ask syntax do not change.
Use --with <kind> only when the user explicitly wants a particular answering agent. It does not
change the configured pre-summary Agent. Use --timeout <seconds> when the default 120-second overall
limit is unsuitable.
The returned text is an agent's reading of another session, not a verbatim transcript. When exact wording
or broader context matters, follow up with vrefer <session> or vrefer <session> --range A:B.
If answering fails, vrefer explains the reason on stderr and returns the transcript as its fallback.
Check stderr before treating stdout as a completed answer.
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.
- 1mo ago First seen · 46 lines · 91 tokens per session scan A 24267164d39d
vask is a skill published in the GitHub repository vlinx-io/VelaTerm (301 stars, last pushed 2d ago), licensed MIT. It adds 91 tokens to every session and 478 once invoked, about $0.0004 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 2026-09-14.
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