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-configs-variationsgit 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-configs-variations)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-variations"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-variations/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-configs-variations"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/agentcontrol-configs-variations.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 | $0.00033 | $0.01243 |
| Opus 5 | $0.00016 | $0.00622 |
| Sonnet 5 | $0.00007 | $0.00249 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
configs-variations 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Config Variations
You're using a skill that will guide you through testing and optimizing configs through variations. Your job is to design experiments, create variations, and systematically find what works best.
Prerequisites
This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.
Primary MCP tool:
clone-ai-config-variation-- clone a baseline variation with selective overrides (recommended for experimentation)
Alternative MCP tools (for more control):
get-ai-config-- review existing variations before adding new onescreate-ai-config-variation-- create new variations from scratch
Optional MCP tools:
update-ai-config-variation-- refine a variation after creationdelete-ai-config-variation-- remove variations that didn't work out
Core Principles
- Test One Thing at a Time: Change model OR prompt OR parameters, not all at once
- Have a Hypothesis: Know what you're trying to improve
- Measure Results: Use metrics to compare variations
- Verify via Tool: The agent fetches the config to confirm variations exist
Workflow
Step 1: Identify What to Optimize
What's the problem? Cost, quality, speed, accuracy? How will you measure success?
Step 2: Design the Experiment
| Goal | What to Vary |
|---|---|
| Reduce cost | Cheaper model (e.g., gpt-4o-mini) |
| Improve quality | Better model or more detailed prompt |
| Reduce latency | Faster model, lower max_tokens |
| Increase accuracy | Different model family (Claude vs GPT-4) |
Step 3: Create Variations (Recommended: Clone with Overrides)
Use clone-ai-config-variation to duplicate the baseline and override only what you're testing. The tool reads the source variation, merges your overrides, and creates the new variation. Everything you don't pass is inherited from the source automatically.
Required fields:
sourceVariationKey-- the baseline to clone fromkeyandname-- identifiers for the new variation (e.g.,gpt4o-mini-cost-test)
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 · 111 lines · 33 tokens per session scan A 20ee03e0af5a
configs-variations is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 33 tokens to every session and 1,243 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 2026-09-06.
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