Doca Bare Metal Deployment
NVIDIA/skills
A skill your agent uses for launching, supervising, debugging, OR platform lifecycle on a BlueField — BFB install, RShim/TMFIFO, host PF rebind, post-BFB recovery — taking a DOCA-linked binary to a…
A skill your agent uses when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and…
Install Socc Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-experiments --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/SoCC-Skills/skills/socc-experiments .claude/skills/socc-experiments && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "socc-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-experiments into .claude/skills/socc-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-experiments", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-experimentsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/SoCC-Skills/skills/socc-experiments .agents/skills/socc-experiments && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "socc-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-experiments into .agents/skills/socc-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-experiments", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/SoCC-Skills/skills/socc-experiments .cursor/skills/socc-experiments && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "socc-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-experiments into .cursor/skills/socc-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-experiments", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path SoCC-Skills/skills/socc-experiments--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/SoCC-Skills/skills/socc-experiments .gemini/skills/socc-experiments && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "socc-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-experiments into .gemini/skills/socc-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-experiments", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-experimentsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/SoCC-Skills/skills/socc-experiments .github/skills/socc-experiments && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "socc-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-experiments into .github/skills/socc-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-experiments", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills socc-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/SoCC-Skills/skills/socc-experiments .opencode/skills/socc-experiments && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "socc-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SoCC-Skills/skills/socc-experiments into .opencode/skills/socc-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "socc-experiments", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
socc-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 560 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and…
- Auditing ACM SoCC evaluations
- SKILL.md covers Evaluation audit, Claim-to-evidence design table, Tail, cost, and scale floor and Workload and trace provenance…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Realistic deployments over simulation
What it does
Socc Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Multi-tenancy and Deployment. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Auditing ACM SoCC evaluations
- Realistic deployments over simulation
- Representative workloads and traces
- Tail-latency and cost as first-class metrics
Example prompts
- “/socc-experiments”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Socc Experiments loads about 1.3k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 560 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 560 words, ~1,328 tokens.
Download SKILL.mdSave it as
.claude/skills/socc-experiments/SKILL.md (or your agent's skills folder).- name
- socc-experiments
- description
- Use when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim.
SoCC Experiments
Use this before submission when the evaluation is not yet locked. SoCC reviewers come from both SIGMOD and SIGOPS, and the evaluation is where a good cloud idea is won or lost. The organizing principle is measured evidence proportional to the claim — the study must test the thing the paper asserts, on a system and workloads a skeptic from either community would accept, with tail latency and cost reported, not just the mean.
Evaluation audit
- Prefer a real or realistic deployment over simulation. A cloud claim about a system needs the system running on a testbed or cluster; simulation alone invites the "does it hold on real hardware?" reject. Where full scale is impossible, run a faithful scaled deployment and say so.
- Use production or representative workloads/traces, with stated provenance and selection, and release the replay harness. Synthetic-only microbenchmarks invite the "does this reflect real load?" objection.
- Report tail and cost, not just averages. p95/p99 (p99.9 where it bites), the latency distribution, and a concrete cost model (instance-seconds or $) are the outcomes operators are billed against.
- Choose fair baselines, including the strongest prior system and a simple-but-reasonable alternative, each tuned with a documented, equal budget. An untuned baseline is a scored weakness.
- Show scale and multi-tenancy behavior: how the result holds as nodes, tenants, or load grow, and where the bottleneck appears — SoCC papers live at scale.
- Make the measurement pipeline reproducible (see the code block) so the evaluation reproduces rather than re-samples.
- Design limits in, not on: know before you run which provider, region, and regime limits the study will have, and instrument to bound them.
Claim-to-evidence design table
| Cloud claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "System raises throughput at scale" | Throughput across node counts on a real testbed vs. tuned baseline | "Simulated / small-scale only" |
| "Holds tail latency under bursts" | p99 distribution under bursty replayed load | "Only mean latency reported" |
| "Cuts cost" | Instance-seconds or $ under a stated pricing model | "Cost asserted, never measured" |
| "Fair under multi-tenancy" | Per-tenant SLO attainment with contending workloads | "Single-tenant microbenchmark" |
| "The new mechanism adds the value" | Ablation isolating the mechanism vs. a simpler policy | "Mechanism's marginal value never isolated" |
Show full SKILL.md (204 more words)Show less
Tail, cost, and scale floor
[Tail] report percentiles and the distribution, not just the mean; state the SLO target
[Cost] define the pricing model; report the cost metric so a reader can recompute the saving
[Scale] sweep nodes/tenants/load; identify the bottleneck where the result degrades
[Runs] report the number of runs and the variance for any measured metric
[Compute] state the testbed actually used (nodes, instance types, kernel), not vague feasibilityWorkload and trace provenance floor
- Pin commit SHAs and record trace extraction dates; archive the replayed trace or a faithful generator, not just a pointer or a query.
- State inclusion/exclusion criteria and the resulting workload, with the filtering/replay scripts in the artifact.
- Report how the trace maps onto the testbed (scaling factors, time compression) so a reader can judge realism.
Vignette: evaluating a storage QoS mechanism
Suppose the paper claims a scheduler enforces latency SLOs on shared storage better than the prior system. The matching plan: run both on a real storage testbed with a mix of latency-sensitive and batch tenants replayed from a representative workload; tune both with an equal, documented budget; report per-tenant p99 attainment and aggregate throughput with variance; show behavior as tenants and load scale and name the bottleneck; report the provisioning cost of each; and state external-validity limits (storage backend, workload mix) — every number traceable to a logged run in the artifact.
Statistical and measurement reporting floor
- Latency distributions and tail percentiles for every comparison, not point estimates.
- Number of runs and the source of variance for any measured metric.
- A stated cost model behind any economic claim; the compute actually consumed.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: testbed/workload/metric incl. tail+cost>
[Deployment realism] <real testbed / scaled deployment / simulation-only>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Tail+cost+scale] <percentiles + cost model + scaling behavior reported? yes/no>
[Provenance] <trace SHAs+dates, replay harness, testbed described? yes/no>
[Decision-critical next run] <one experiment or measurement extension>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in SoCC-Skills/skills/socc-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Socc Experiments next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Socc Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Doca Bare Metal DeploymentNVIDIA/skills | 3.5k | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| Letta Fleet Managementletta-ai/skills | 147 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Navan Deploy Integrationjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~901 | Automated safety check: Pass | MIT | |
| KubeSphere Multi-Tenant Managementkubesphere/kubesphere | 17k | 1 repos | ~3.1k | Automated safety check: Pass | Custom licence | |
| Os Usagegoinfinite/os | 361 | — | ~4.8k | Automated safety check: Notes | GPL-2.0 |
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Questions about Socc Experiments
What does Socc Experiments do?
A skill your agent uses when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and…. Socc Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ACM SoCC evaluations, covering real or realistic deployments over simulation, production or representative workloads and traces, tail-latency and cost as first-class metrics, fair tuned baselines, scale and multi-tenancy behavior, reproducible measurement pipelines, and matching evidence to the shape of each cloud claim.
When should I use Socc Experiments?
Socc Experiments fits situations like: auditing ACM SoCC evaluations; realistic deployments over simulation; representative workloads and traces; tail-latency and cost as first-class metrics.
How do I install Socc Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a claude-code`. Or copy the skill folder (SoCC-Skills/skills/socc-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/socc-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Socc Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a codex`. Or copy the skill folder (SoCC-Skills/skills/socc-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/socc-experiments in your project. Codex loads it when a task matches its description.
Can I use Socc Experiments in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-experiments -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/socc-experiments, .gemini/skills/socc-experiments, .github/skills/socc-experiments and .opencode/skills/socc-experiments in your project.
What does Socc Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Socc Experiments is instructions for the agent only.
Does Socc Experiments access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Socc Experiments safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Socc Experiments use?
Socc Experiments is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Socc Experiments use?
About 1.3k tokens (SKILL.md is roughly 5.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Socc Experiments?
Skills that share tags, products or a category with Socc Experiments: Doca Bare Metal Deployment (NVIDIA/skills, 3.5k stars), Letta Fleet Management (letta-ai/skills, 147 stars), Navan Deploy Integration (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and KubeSphere Multi-Tenant Management (kubesphere/kubesphere, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Socc Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.