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-online-evalsgit 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-online-evals)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/agentcontrol-online-evals"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-online-evals/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-online-evals"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-online-evals.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 | $0.00032 | $0.03853 |
| Opus 5.5 | $0.00013 | $0.01541 |
| Sonnet 5.5 | $0.00006 | $0.00771 |
| Haiku 4.5 | $0.00003 | $0.00385 |
Grade B, and why
online-evals scanned grade B with 2 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 1mo 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
2. **Check MCP config** - Claude: `~/.claude/config.json` -> `mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs" \ How it starts
The opening of the file, as written. The whole thing — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Config Online Evaluations
Attach judges to config variations for automatic quality scoring using LLM-as-a-judge methodology. Judges evaluate responses and return scores between 0.0 and 1.0.
Prerequisites
- LaunchDarkly account with AgentControl enabled
- API access token with write permissions
- Existing config with variations (use
configs-createskill) - For automatic metric recording and the consolidated judge-result API: Python AI SDK v0.20.0+ or Node.js AI SDK v0.20.0+
API Key Detection
- Check environment variables -
LAUNCHDARKLY_API_KEY,LAUNCHDARKLY_API_TOKEN,LD_API_KEY - Check MCP config - Claude:
~/.claude/config.json->mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY - Prompt user - Only if detection fails
Core Concepts
What Are Judges?
Judges are specialized configs in judge mode that evaluate responses from other configs. They use an LLM to score outputs and return structured results:
{
"score": 0.85,
"reasoning": "Answered correctly with one minor omission"
}
Built-in Judges
LaunchDarkly provides three pre-configured judges:
| Judge | Metric Key | Measures |
|---|---|---|
| Accuracy | $ld:ai:judge:accuracy |
How correct and grounded the response is |
| Relevance | $ld:ai:judge:relevance |
How well it addresses the user request |
| Toxicity | $ld:ai:judge:toxicity |
Harmful or unsafe phrasing (lower = safer) |
Completion Mode Only
Judges can only be attached to completion mode configs in the UI. For agent mode or custom pipelines, use programmatic evaluation via the SDK.
Restrictions
- Cannot attach judges to judges (no recursion)
- Cannot attach multiple judges with the same metric key to a single variation
- Cannot view/edit model parameters or tools on judge variations
Workflow
Step 1: Create Custom Judges (Optional)
For domain-specific evaluation, create judge configs:
# Create judge config
curl -X POST "https://app.launchdarkly.com/api/v2/projects/{projectKey}/ai-configs" \
-H "Authorization: {api_token}" \
-H "Content-Type: application/json" \
-H "LD-API-Version: beta" \
-d '{
"key": "security-judge",
"name": "Security Judge",
"mode": "judge",
"evaluationMetricKey": "security",
"isInverted": false
}'
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.
- 1mo ago First seen · 456 lines · 32 tokens per session scan B 4c83e0e91c6f
online-evals is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 3,853 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 2 findings (reads agent configuration directories, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-06.
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