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 Bilal140202/the-lord-of-the-skills --skill agentcontrol-built-in-metricsgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-skillsWrote 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/bilal140202/the-lord-of-the-skills/agentcontrol-built-in-metrics)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/agentcontrol-built-in-metrics"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-built-in-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/bilal140202/the-lord-of-the-skills/agentcontrol-built-in-metrics"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-built-in-metrics.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 | $0.00059 | $0.03099 |
| Opus 5 | $0.00030 | $0.01550 |
| Sonnet 5 | $0.00012 | $0.00620 |
| Haiku 4.5 | $0.00006 | $0.00310 |
Grade A, and why
built-in-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 6d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Metrics Instrumentation
You're using a skill that wires LaunchDarkly agent metrics around an existing provider call. Your job is to audit what's already there, pick the right tier from the ladder below, and implement it with the least ceremony that still captures the metrics the Monitoring tab needs (duration, input/output tokens, success/error, plus TTFT when streaming).
The single most important thing to get right: default to the highest tier that fits the shape of the call. Going lower ("just write the manual tracker calls") looks flexible but costs you drift, missed metrics, and legacy patterns the SDKs have moved past.
The four-tier ladder
This is the order the official SDK READMEs (Python core, Node core, and every provider package) recommend. Walk from the top and stop at the first tier that fits:
| Tier | Pattern | Use when | Tracks automatically |
|---|---|---|---|
| 1 — Managed runner | Python: ai_client.create_model(...) returning a ManagedModel, then await model.run(...). Node: aiClient.createModel(...) returning a ManagedModel, then await model.run(...). |
The call is conversational (chat history, turn-based). This is what the provider READMEs lead with. | Duration, tokens, success/error — all of it, zero tracker calls. |
2 — Provider package + trackMetricsOf |
tracker.trackMetricsOf(Provider.getAIMetricsFromResponse, () => providerCall()). Provider packages today: @launchdarkly/server-sdk-ai-openai, -langchain, -vercel (Node) and launchdarkly-server-sdk-ai-openai, -langchain (Python). |
The shape isn't a chat loop (one-shot completion, structured output, agent step) but the framework or provider has a package. | Duration + success/error from the wrapper; tokens from the package's built-in getAIMetricsFromResponse extractor. |
3 — Custom extractor + trackMetricsOf |
Same trackMetricsOf wrapper, but you write a small function that maps the provider response to LDAIMetrics (tokens + success). |
No provider package exists (Anthropic direct, Gemini, Cohere, custom HTTP). | Duration + success/error from the wrapper; tokens from your extractor. |
| 4 — Raw manual | Separate calls to trackDuration, trackTokens, trackSuccess / trackError, plus trackTimeToFirstToken for streams. |
Streaming with TTFT, unusual response shapes, partial tracking, anything Tier 2–3 can't cleanly wrap. | Only what you explicitly call — it's on you to not miss one. |
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.
- 6d ago First seen · 110 lines · 59 tokens per session scan A 09004c069189
built-in-metrics is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 59 tokens to every session and 3,099 once invoked, about $0.0003 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 2026-09-06.
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