Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add rjmurillo/ai-agents/plugin install project-toolkitWrote 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/rjmurillo/ai-agents/metrics)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/metrics"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/metrics/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/rjmurillo/ai-agents/metrics"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/metrics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Prompt Injection · line 224 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Excessive Agency · line 7 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00063 | $0.03138 |
| Opus 5.5 | $0.00025 | $0.01255 |
| Sonnet 5.5 | $0.00013 | $0.00628 |
| Haiku 4.5 | $0.00006 | $0.00314 |
Grade A, and why
metrics 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 4d 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Metrics Collection Utility
Purpose
This utility collects and reports metrics on agent usage from git history. It collects 4 of the metrics defined in docs/agent-metrics.md (Invocation Rate, Coverage, Infrastructure Review, Usage Distribution) for measuring agent system health, effectiveness, and adoption.
Triggers
| Trigger Phrase | Operation |
|---|---|
collect agent metrics |
Run collect_metrics.py with default 30-day window |
generate metrics dashboard |
Run with markdown output for reporting |
check agent adoption rate |
Run and highlight Metric 2 (agent coverage) |
weekly metrics report |
Run with 7-day window, markdown output |
export metrics as JSON |
Run with JSON output for automation |
When to Use
Use this skill when:
- Measuring agent system health or adoption trends
- Producing periodic dashboards or reports
- Evaluating whether agent usage is balanced across types
- Checking infrastructure review coverage
Use manual git log inspection instead when:
- Investigating a single commit's agent attribution
- Debugging a specific CI run's metrics workflow
Process
- Run the metrics collection script for the desired time range
- Review generated reports for agent usage patterns
- Identify trends and anomalies in adoption metrics
Anti-Patterns
| Avoid | Why | Instead |
|---|---|---|
| Running without specifying time window | Default 30 days may not match your intent | Use --since with explicit day count |
| Comparing metrics across different time windows | Misleading trends | Normalize to same window size |
| Ignoring zero agent coverage | Indicates broken detection patterns | Verify commit message conventions match patterns |
| Manual commit counting | Error-prone, misses patterns | Use the script for consistent detection |
| Storing JSON output without markdown | Loses human-readable context | Generate both formats for archival |
Verification
After execution:
What ships with it
6 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.
- 4d ago Changed · +29 lines 082185db2c90
- 18d ago Changed · +23 lines 0253a6975142
- 22d ago Changed · +6 lines 8a70a75bacf5
- 24d ago Changed 1344cb5363c9
- 1mo ago Changed faf71ba9c360
- 1mo ago First seen · 225 lines · 63 tokens per session scan A 702620038e2f
metrics is a skill published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 3,138 once invoked, about $0.0003 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 2026-09-03.
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