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 varrdinc/varrd --skill varrd-balancegit clone --depth 1 https://github.com/varrdinc/varrdWrote 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/varrdinc/varrd/varrd-balance)<a href="https://agentmods.dev/skills/varrdinc/varrd/varrd-balance"><img src="https://agentmods.dev/badge/skills/varrdinc/varrd/varrd-balance/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/varrdinc/varrd/varrd-balance"><img src="https://agentmods.dev/badge/skills/varrdinc/varrd/varrd-balance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00431 |
| Opus 5.5 | $0.00013 | $0.00172 |
| Sonnet 5.5 | $0.00007 | $0.00086 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
varrd-balance 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
VARRD Balance — Check Credits
Use this skill to check how many credits the user has before running paid operations.
Command
varrd balance
How It Works
Returns the current credit balance in cents, whether billing enforcement is enabled, and available credit packs for purchase.
When to Check
- Before running
varrd researchorvarrd discover(these cost credits) - After getting a "credits depleted" error
varrd scan,varrd search,varrd hypothesis, andvarrd resetare free — no need to check first
Cost Reference
| Operation | Approximate Cost |
|---|---|
| scan, search, hypothesis, balance, reset | Free |
| Full research workflow | ~20-30 cents |
| ELROND expert council | ~40-60 cents |
| Multi-market testing (3+ markets) | ~$1 |
If Credits Are Low
Direct the user to purchase more at app.varrd.com. Credit packs: $5 / $20 / $50.
Signing up at app.varrd.com grants $2.00 in free credits (~10-20 research queries) — link your agent to that account to use them. Agent accounts created directly via MCP/CLI start at $0.
Cost
Free. No credits consumed.
Python SDK Equivalent
from varrd import VARRD
v = VARRD()
b = v.balance()
print(f"Balance: ${b.balance_cents / 100:.2f}")
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 · 57 lines · 33 tokens per session scan A c8e270eb1977
varrd-balance is a skill published in the GitHub repository varrdinc/varrd (24 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 431 once invoked, about $0.0001 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 2026-08-30.
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