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 slbug/claude-ruby-grape-rails --skill skill-monitorgit clone --depth 1 https://github.com/slbug/claude-ruby-grape-railsWrote 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/slbug/claude-ruby-grape-rails/skill-monitor)<a href="https://agentmods.dev/skills/slbug/claude-ruby-grape-rails/skill-monitor"><img src="https://agentmods.dev/badge/skills/slbug/claude-ruby-grape-rails/skill-monitor/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/slbug/claude-ruby-grape-rails/skill-monitor"><img src="https://agentmods.dev/badge/skills/slbug/claude-ruby-grape-rails/skill-monitor.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 | $0.00028 | $0.00794 |
| Opus 5 | $0.00014 | $0.00397 |
| Sonnet 5 | $0.00006 | $0.00159 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
skill-monitor 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 11d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Monitor
Audience: Agents, Not Humans
Imperative-only. Exploratory — does NOT prove a skill is good or bad on its own.
Requirements
.claude/session-metrics/metrics.jsonl (from /session-scan).
Trust Order
Before trusting the dashboard:
claude plugin validate --strict plugins/ruby-grape-railsmake evalormake eval-all/docs-checkif Claude docs drift suspected/skill-monitor— corroborating observational input
Usage
| Command | Behavior |
|---|---|
/skill-monitor |
dashboard for all skills |
/skill-monitor --skill review |
scoped to one skill |
/skill-monitor --improve |
delegates to skill-effectiveness-analyzer |
/skill-monitor --window 30d |
broader window |
/skill-monitor --provider claude-code |
exact provider |
Workflow
1. Parse Arguments
| Flag | Values |
|---|---|
--skill |
NAME |
--improve |
(no value, triggers analyzer mode) |
--window |
7d / 30d / all |
--provider |
NAME |
2. Load Metrics
Read .claude/session-metrics/metrics.jsonl. Extract
skill_effectiveness.
No skill_effectiveness data → tell contributor to rescan with current
scorer.
--provider present → restrict dashboard to that provider.
3. Compute Observational Aggregates
Useful aggregates:
- total invocations
- sessions used in
- weighted action rate
- weighted avg post-errors
- weighted avg post-corrections
- dominant outcomes
Baseline comparison computed → present as heuristic context, NOT causal effect estimate.
4. Display the Dashboard
Show:
- window + provider scope
- sample sizes
- per-skill aggregates
- low-confidence warnings for thin samples
Flagged skills are review candidates, NOT proven regressions.
5. Improvement Mode
--improve requested → delegate to skill-effectiveness-analyzer.
Analyzer requirements:
- use the improvement template
- cite session evidence
- list confounders
- look for corroboration in
lab/eval, docs-check, previous session-analysis reports
What ships with it
2 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.
- 11d ago First seen · 122 lines · 28 tokens per session scan A 9dcc48b191bf
skill-monitor is a skill published in the GitHub repository slbug/claude-ruby-grape-rails (7 stars, last pushed 6d ago), licensed MIT. It adds 28 tokens to every session and 794 once invoked, about $0.0001 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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