Claude Code Agent Monitor is a dashboard that records and displays activity from Claude Code and Codex coding-agent sessions, including tool use and subagent coordination. It is for people who need live visibility into agent work through a web interface or native desktop app. The catalogue entries extend Claude Code with skills, commands, agents, plugins, instructions, hooks, and settings related to the monitor.
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 cache-efficiencygit 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/cache-efficiency)<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/cache-efficiency"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/cache-efficiency/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/cache-efficiency"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/cache-efficiency.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 | $0.00100 | $0.00933 |
| Opus 5.5 | $0.00040 | $0.00373 |
| Sonnet 5.5 | $0.00020 | $0.00187 |
| Haiku 4.5 | $0.00010 | $0.00093 |
Grade A, and why
cache-efficiency 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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cache Efficiency
Diagnose whether prompt caching is actually saving money, and where it is not.
Input
The user provides: $ARGUMENTS
This may be: empty (analyze the whole fleet), "today" / "this week" / a date range, a session ID to scope the analysis, or a target like "hit rate > 80%". When empty, analyze all data from /api/analytics.
Data Sources
| Endpoint | Returns |
|---|---|
GET /api/analytics |
tokens.total_input, tokens.total_output, tokens.total_cache_read, tokens.total_cache_write (baselines pre-summed), plus daily_sessions |
GET /api/sessions?limit=200 |
Session list — each has model, cwd, started_at, ended_at, inline cost, metadata (JSON: usage_extras with cache token detail) |
GET /api/sessions/{id} |
Full session detail with nested agents and events, for drill-down on a flagged session |
GET /api/pricing/cost |
{ total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] } — used to price cache read vs write spend |
How cache economics work
cache_hit_rate = total_cache_read / (total_cache_read + total_input)
cache_reuse = total_cache_read / total_cache_write
cache_read_cost = (cache_read_tokens / 1M) × cache_read_per_mtok
cache_write_cost = (cache_write_tokens / 1M) × cache_write_per_mtok
Cache writes cost more per token than cache reads (e.g. Sonnet $3.75 write vs $0.30 read per Mtok), and writes are billed even if the cached block is never reused. The payoff only arrives on subsequent reads — so a healthy fleet shows cache_read_tokens far exceeding cache_write_tokens. When cache_reuse < 1, you are paying to cache context you barely re-read.
Token counts are effective totals = current + baseline (baselines preserve pre-compaction tokens).
Report Sections
1. Fleet Cache Hit Rate
From /api/analytics: compute cache_hit_rate × 100. State raw total_cache_read and total_input. Benchmark: >70% strong, 40–70% moderate, <40% weak prompt-cache utilization.
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.
- 1mo ago First seen · 67 lines · 100 tokens per session scan A 6082e3cddc82
cache-efficiency is a skill published in the GitHub repository hoangsonww/Claude-Code-Agent-Monitor (1,124 stars, last pushed 4d ago), licensed MIT. It adds 100 tokens to every session and 933 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 2026-08-30.
Other skills, from other repositories
code-remediate
Collect and resolve selected findings in one pass, then verify; bare PR targets use online items, while PR +review authorizes matching review evidence and any needed review step.
nodejs-expert
Expert-level Node.js backend development with Express, async patterns, streams, performance optimization, and production best practices. Use when the user mentions JavaScript, backend, Express, or async, or when the task involves Modern Node.js Features, Express Framework, File System Operations, or HTTP Requests.
optimize
Optimize a measurable metric with bounded iterations, resumable numeric campaigns, isolated ablations, and descriptive retrospectives.
react-tanstack-router
Use when implementing routing in a React app (NOT Next.js) with TanStack Router — file-based routes, loaders, search params.
laravel-blade
Create Blade templates with components, slots, layouts, and directives. Use when building views, reusable components, or templating.
laravel-eloquent
Use when working with database models — Eloquent ORM, PHP attributes, relationships, queries, observers, or factories in Laravel 13.