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 hoangsonww/Claude-Code-Agent-Monitor --skill budget-setgit clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-MonitorWrote 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/hoangsonww/claude-code-agent-monitor/budget-set)<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/budget-set"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/budget-set/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/hoangsonww/claude-code-agent-monitor/budget-set"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/budget-set.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 71 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00079 | $0.01007 |
| Opus 5 | $0.00039 | $0.00504 |
| Sonnet 5 | $0.00016 | $0.00201 |
| Haiku 4.5 | $0.00008 | $0.00101 |
Grade A, and why
budget-set scanned grade A with 1 finding 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 13d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST http://localhost:4820/api/alerts/rules \ How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Budget Set
Help the user define a spend budget and turn it into a live cost guardrail on the Agent Monitor dashboard.
Input
The user provides: $ARGUMENTS
This is the budget to set — e.g. "$50/month", "$10/week", or "200000 tokens".
If a period is omitted, treat it as a monthly budget and say so. If no number is
given, read current spend first and propose a target.
Data Sources
| Endpoint | Returns |
|---|---|
GET /api/pricing/cost |
{ total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] } — current spend, used to size the budget and compute the blended $/token rate |
GET /api/alerts/rules |
{ rules: [{ id, name, rule_type, config, enabled, cooldown_seconds }] } — existing rules, so you don't create a duplicate guardrail |
POST /api/alerts/rules |
Create a rule. Body: { name, rule_type, config, enabled?, cooldown_seconds? }. Returns { rule } |
How a budget becomes a rule
The dashboard's alerting engine fires on tokens, not dollars. The spend-relevant
rule type is token_threshold, whose config is { total_tokens } — it fires when
a session's cumulative tokens (input + output + cache_read + cache_write) cross the
threshold. To turn a dollar budget into a token ceiling:
blended_rate_per_token = total_cost / total_tokens # from /api/pricing/cost
token_ceiling = budget_dollars / blended_rate_per_token
Compute total_tokens by summing the four token columns across the cost breakdown.
Rule fields explained
| Field | Meaning |
|---|---|
name |
Human label shown in the alert feed (e.g. "Monthly $50 budget"). Required, non-empty. |
rule_type |
"token_threshold" for a spend guardrail. (Other types: event_pattern, inactivity, status_duration — not spend-related.) |
config.total_tokens |
Positive integer token ceiling. A session crossing it fires the alert. Derive from the dollar budget as above. |
enabled |
true to arm immediately (default), false to stage it. |
cooldown_seconds |
Minimum seconds between re-fires for the same scope. Default 300. Raise it (e.g. 3600) so a single overspending session doesn't spam the feed. |
What ships with it
1 file 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.
- 13d ago First seen · 82 lines · 79 tokens per session scan A ae1a04f085ae
budget-set is a skill published in the GitHub repository hoangsonww/Claude-Code-Agent-Monitor (991 stars, last pushed 4d ago), licensed MIT. It adds 79 tokens to every session and 1,007 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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