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 agentmods add skills/xobotyi/cc-foundry/statsdnpx skills add xobotyi/cc-foundry --skill statsdgit clone --depth 1 https://github.com/xobotyi/cc-foundryWrote 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/xobotyi/cc-foundry/statsd)<a href="https://agentmods.dev/skills/xobotyi/cc-foundry/statsd"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/statsd.svg" alt="Measured on agentmods" 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 | $0.00074 | $0.02092 |
| Opus 5 | $0.00037 | $0.01046 |
| Sonnet 5 | $0.00015 | $0.00418 |
| Haiku 4.5 | $0.00007 | $0.00209 |
Grade A, and why
statsd 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
StatsD
Choose the right metric type, name it with dot-delimited hierarchy, tag dimensions instead of encoding them in names. StatsD is fire-and-forget: UDP means zero latency impact, but wrong metric types or bad naming corrupt your data silently.
References
- Metric types — [
${CLAUDE_SKILL_DIR}/references/metric-types.md]: Wire format details, type comparison, sampling correction - Naming — [
${CLAUDE_SKILL_DIR}/references/naming.md]: Graphite namespace mapping, character rules, naming examples - DogStatsD — [
${CLAUDE_SKILL_DIR}/references/dogstatsd.md]: Events format, service checks, protocol versions, distributions vs histograms - Aggregation — [
${CLAUDE_SKILL_DIR}/references/aggregation.md]: Flush mechanics, Graphite downsampling, DogStatsD aggregation, timestamps - Client patterns — [
${CLAUDE_SKILL_DIR}/references/client-patterns.md]: High-throughput tuning, error handling, K8s deployment, UDS configuration - Backends — [
${CLAUDE_SKILL_DIR}/references/backends.md]: statsd_exporter config, Telegraf setup, migration guides
Metric Types
Wire format: <metric_name>:<value>|<type>[|@<sample_rate>][|#<tags>]
Decision Matrix
| Question | Type |
|---|---|
| How many times did X happen? | Counter (c) |
| What is X right now? | Gauge (g) |
| How long did X take? | Timer (ms) |
| What is the distribution of X? | Histogram (h) |
| How many unique X occurred? | Set (s) |
| What is the global distribution of X? | Distribution (d, DogStatsD only) |
Wrong metric type = wrong math at the server. A gauge used as a counter loses data between flushes; a counter used as a gauge produces meaningless rates.
Counter (|c)
What ships with it
7 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 First seen · 215 lines · 74 tokens per session scan A 093b99660999
statsd is a skill published in the GitHub repository xobotyi/cc-foundry (20 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 2,092 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…