Individual skill

osdi-experiments

Use when designing or auditing the evaluation of an OSDI submission — choosing mature baselines and realistic workloads, structuring the section around research questions, measuring scalability and tail behavior, quantifying the design's costs, and fitting the evidence into the 12-page reviewed body.

OSDI-Skills/skills/osdi-experiments/SKILL.md

Read pinned source

Source commit 932eb23331721b16e967c59f093781be1928b498

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
Yes
Observed behavior
Network or broad shell
Last checked
Nov 9, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
1,401
Repository last updated
Sep 27, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# OSDI Experiments

Design the evaluation as the paper's proof obligation. The page constraints referenced
here are OSDI '26 rules (12 reviewed pages, no appendices at submission — verified
2026-07-08); the evidence standards are the durable expectations of systems PCs.

## Research questions first

Write the evaluation's research questions before running anything, and derive the
experiment set from them. Every OSDI evaluation ultimately answers versions of:

1. **Does the idea work end to end?** — the headline comparison on a realistic
   workload against the strongest baseline.
2. **Where does the benefit come from?** — component breakdown attributing the win to
Back to top