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 ElemontCapital/x-algorithm-skills --skill x-experimental-opsgit clone --depth 1 https://github.com/ElemontCapital/x-algorithm-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/elemontcapital/x-algorithm-skills/x-experimental-ops)<a href="https://agentmods.dev/skills/elemontcapital/x-algorithm-skills/x-experimental-ops"><img src="https://agentmods.dev/badge/skills/elemontcapital/x-algorithm-skills/x-experimental-ops.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 5.1 | $0.00038 | $0.00539 |
| Opus 5 | $0.00019 | $0.00269 |
| Sonnet 5 | $0.00008 | $0.00108 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
x-experimental-ops 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 8d 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
X Experimental Ops
Knowledge of X's A/B testing infrastructure (DuckDuckGoose) and the metrics used to measure algorithmic success.
Context
The algorithm is never "finished." It is a living system managed by DuckDuckGoose (DDG), X's internal experimentation platform. Every change to a weight or a filter is first tested on a small percentage of the user base.
What it does
- Explains Bucketing:
- Details the mechanics of DuckDuckGoose, X's internal experimentation platform that uses salt-based consistent hashing to deterministically assign users to "Control" or "Treatment" variants.
- Ensures "sticky" assignments so a user's experience remains consistent across sessions while maintaining statistically sound percentage-based rollouts (e.g., 1%, 5%, or 10% cohorts).
- Decodes Success Metrics:
- Breaks down the "Unregretted User Minutes" (UUM) North Star metric, which prioritizes high-value time spent (replies, likes, and deep reads) over passive scrolling or "clickbait" interactions that lead to user regret.
- Analyzes how experimental changes impact the Multi-Task Learning (MTL) "heads" to ensure a boost in one engagement signal (like Retweets) doesn't negatively correlate with platform health or retention.
- Analyzes Feature Flags:
- Identifies how the system uses Dynamic Configuration and Feature Gates to toggle ranking logic or retrieval sources on and off for specific cohorts in real-time.
- Explains the "Kill Switch" architecture that allows engineers to instantly roll back a new algorithmic feature if it causes a spike in latency or negative feedback without requiring a full code redeployment.
Example Trigger Prompts
- "/run-experiment salt-based hashing for user buckets"
- "/run-experiment Unregretted User Minutes vs dwell time"
- "Trace feature flag logic for latest Grok retrieval test"
- "Show holdout group parameters for current Heavy Ranker A/B"
- "Compare control vs variant metrics for feed engagement test"
- "Explain how a new signal is staged in an experiment pipeline"
What ships with it
2 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.
- 8d ago First seen · 37 lines · 38 tokens per session scan A 5eab31d8d8b8
x-experimental-ops is a skill published in the GitHub repository ElemontCapital/x-algorithm-skills (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 539 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 2026-08-31.
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