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 event4u-app/agent-config --skill grafanagit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/event4u-app/agent-config/grafana)<a href="https://agentmods.dev/skills/event4u-app/agent-config/grafana"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/grafana/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/event4u-app/agent-config/grafana"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/grafana.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 | $0.00047 | $0.01497 |
| Opus 5 | $0.00023 | $0.00749 |
| Sonnet 5 | $0.00009 | $0.00299 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
grafana 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 9d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grafana Skill
When to use
Use this skill when:
- Creating or editing Grafana dashboards (JSON provisioning)
- Writing Loki LogQL queries for log analysis
- Configuring alerting rules or contact points
- Working on a Grafana module (if the project has one)
- Debugging log-based issues using Loki data
Procedure: Create Grafana dashboard
- Inspect existing Grafana assets — Check the project's Grafana location (Docker config,
dashboards/, provisioning) and existing datasources (Loki, Prometheus) before adding panels. - Decide signals and layout — Pick the queries (LogQL / PromQL) and panel types per signal; reuse naming and label conventions below.
- Author the dashboard JSON — Add provisioned JSON under the dashboards path; wire datasources and template variables.
- Verify — Reload Grafana, confirm panels render against real data, and check alert rules fire on synthetic test inputs.
Project setup
Check if the project has a dedicated Grafana module or Docker config. Typical structure:
{grafana-location}/
├── .docker/
│ ├── grafana/
│ │ ├── dashboards/ ← Dashboard JSON files (provisioned)
│ │ ├── provisioning/
│ │ │ ├── datasources/ ← Loki, Prometheus data sources
│ │ │ └── dashboards/ ← Dashboard provisioning config
│ │ └── alerting/ ← Alert rules, contact points, notification policies
│ └── loki/
│ └── loki-config.yml ← Loki server configuration
├── App/
│ ├── Logging/ ← Loki timestamp processors
│ └── Console/Commands/ ← GrafanaPushDashboardsCommand
├── Docs/
│ └── Loki.md ← Loki documentation
└── Routes/console.php
Configuration
- Grafana config:
config/grafana.php - Logging channels:
config/logging.php(Loki channels) - Loki package:
itspire/monolog-lokivia Monolog handler
Dashboard conventions
JSON provisioning
Dashboards are stored as JSON in .docker/grafana/dashboards/ and auto-provisioned.
Use GrafanaPushDashboardsCommand to push dashboards to remote Grafana instances.
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.
- 9d ago First seen · 179 lines · 47 tokens per session scan A d036974ffc56
grafana is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 1,497 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
save-progress
Save current project task progress to local task state file for cross-session continuity. Use when the user asks to save progress, uses an equivalent localized trigger phrase, the session is interrupted, or the user wants to resume work later. Writes to .claude/project-task-state.json so next session can load it via…
meta-theory
MetaKim executable governance dispatcher. It classifies the run, loads only needed references, preserves foundational capabilities and runtime-native abilities, routes owner + weapon + dependency + runtime + OS + verification, and closes only with evidence, intent acceptance, and writeback decision.
same-set-reusable-flow-for-project-file-inventor
Reusable MetaKim file inventory classification flow. It helps separate durable sources, generated evidence, runtime mirrors, temporary state, and risky unknowns before cleanup or commit.
starreel-drama-production
Operating skill for any AI agent driving the StarReel short-drama production pipeline (script → rewrite → extract → portraits + sheets → storyboards → frames → video → voiceover → final cut) over MCP or REST. Covers the ordered workflow, the entry-point decision table (which channel each kind of customer material…
seedance-20
Generate and direct cinematic AI videos with Seedance 2.0 (ByteDance/Dreamina/Jimeng). Covers text-to-video, image-to-video, video-to-video, and reference-to-video workflows with @Tag asset references, multi-character scenes, audio design, and post-processing. Use when making AI video, writing Seedance prompts…
monorepo-management
Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.