Extracting Windows Event Logs Artifacts
mukul975/Anthropic-Cybersecurity-Skills
Extract, parse, and analyze Windows Event Logs (EVTX) using Chainsaw, Hayabusa, and EvtxECmd to detect lateral movement, persistence, and privilege escalation.
Agent skill
Uai Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging code, data, samplers, solvers, and logs for a UAI submission's 50 MB supplementary ZIP or a public post-acceptance release, making probabilistic-inference…
MITAuto-check passed
Install Uai Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-artifact-evaluation --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/UAI-Skills/skills/uai-artifact-evaluation .claude/skills/uai-artifact-evaluation && 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 "uai-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-artifact-evaluation into .claude/skills/uai-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-artifact-evaluation", 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/UAI-Skills/skills/uai-artifact-evaluationType 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 uai-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-artifact-evaluation --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/UAI-Skills/skills/uai-artifact-evaluation .agents/skills/uai-artifact-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "uai-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-artifact-evaluation into .agents/skills/uai-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-artifact-evaluation", 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 uai-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-artifact-evaluation --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/UAI-Skills/skills/uai-artifact-evaluation .cursor/skills/uai-artifact-evaluation && 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 "uai-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-artifact-evaluation into .cursor/skills/uai-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-artifact-evaluation", 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 UAI-Skills/skills/uai-artifact-evaluation--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 uai-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-artifact-evaluation --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/UAI-Skills/skills/uai-artifact-evaluation .gemini/skills/uai-artifact-evaluation && 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 "uai-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-artifact-evaluation into .gemini/skills/uai-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-artifact-evaluation", 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 uai-artifact-evaluationInstalls 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 uai-artifact-evaluation -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/UAI-Skills/skills/uai-artifact-evaluation .github/skills/uai-artifact-evaluation && 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 "uai-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-artifact-evaluation into .github/skills/uai-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-artifact-evaluation", 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 uai-artifact-evaluation -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 uai-artifact-evaluation --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/UAI-Skills/skills/uai-artifact-evaluation .opencode/skills/uai-artifact-evaluation && 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 "uai-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-artifact-evaluation into .opencode/skills/uai-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-artifact-evaluation", 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
uai-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 626 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging code, data, samplers, solvers, and logs for a UAI submission's 50 MB supplementary ZIP or a public post-acceptance release, making probabilistic-inference…
- Logs for a UAI submissions 50 MB supplementary ZIP
- SKILL.md covers Design for the five-minute…, What inference artifacts must…, Anonymity inside the archive and README skeleton for the archive, plus 4 more sections
- Calls git
- A public post-acceptance release
What it does
Uai Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, data, samplers, solvers, and logs for a UAI submission's 50 MB supplementary ZIP or a public post-acceptance release, making probabilistic-inference claims independently runnable while keeping every file double-blind, given that UAI reviewers may open the archive but are not obliged to read it.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
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
- Logs for a UAI submissions 50 MB supplementary ZIP
- A public post-acceptance release
- Making probabilistic-inference claims independently runnable while keeping every file double-blind
- Given that UAI reviewers may open the archive but are not obliged to read it
Example prompts
- “/uai-artifact-evaluation”
Requirements
- Python 3
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
Shell commands in SKILL.md call:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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
Uai Artifact Evaluation loads about 1.6k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 626 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). 626 words, ~1,596 tokens.
Download SKILL.mdSave it as
.claude/skills/uai-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- uai-artifact-evaluation
- description
- Use when packaging code, data, samplers, solvers, and logs for a UAI submission's 50 MB supplementary ZIP or a public post-acceptance release, making probabilistic-inference claims independently runnable while keeping every file double-blind, given that UAI reviewers may open the archive but are not obliged to read it.
UAI Artifact Evaluation
UAI posted no separate artifact-evaluation track or badge system for the 2026 cycle (existence of one in later cycles: 待核实). What it did post is sharper than a badge: code and data release is strongly encouraged, a 50 MB anonymous ZIP rides with the submission, and reviewers judge whether claims are "backed up convincingly." The artifact's job is to convert a skeptical probabilistic-ML reviewer's spot-check into confirmation.
Design for the five-minute skeptic
Because reviewers are explicitly not required to consult supplementary material, assume whoever opens the ZIP gives it five minutes. Optimize for that reader:
- A top-level
READMEthat states, in its first ten lines, what claim each script reproduces and how long it takes. - One command per headline result, each with a small-scale mode that finishes on a laptop: a reviewer who reproduces Figure 2 at n=500 in ninety seconds will extend you trust for the n=50,000 version.
- Pinned dependencies and one canonical entry point. A
Makefileor single driver script beats a directory of loose notebooks. - Expected outputs stored beside the scripts, so "did it work?" needs no judgment.
What inference artifacts must expose
Probabilistic-modeling papers fail reproduction in venue-specific ways. Package accordingly:
| Contribution type | Must be in the artifact | Spot-check the reviewer will try |
|---|---|---|
| MCMC / SMC sampler | Seeds, chain configs, convergence diagnostics code | Re-run short chains; compare R-hat and ESS to reported |
| Variational method | ELBO logging, initialization scheme, optimizer settings | Reproduce the ELBO trace shape, not just the endpoint |
| Causal discovery | Graph-generation scripts, SHD/SID evaluation code | Regenerate synthetic graphs; verify metrics on one seed |
| Calibration / conformal | Score functions, split definitions, coverage computation | Recompute empirical coverage at one α |
| Probabilistic programming / PGM inference | Model source, query set, exact-baseline harness | Run the exact baseline on the smallest instance |
| Theory with simulations | Every constant used to instantiate the bound | Check simulation matches theorem conditions |
Anonymity inside the archive
The double-blind requirement covers all supplementary material explicitly. Archive leaks are quieter than PDF leaks, so build from a clean export:
# Build an anonymous artifact from a clean tree, never from the working repo
git archive --format=tar HEAD | tar -x -C /tmp/uai-artifact
cd /tmp/uai-artifact
grep -rniE 'university|\.edu|author|thanks|grant' --include='*.py' --include='*.md' . | head
find . -name '*.ipynb' -exec grep -l '"authors"' {} \; # notebook metadata
rm -rf .git .github; find . -name '.DS_Store' -delete
zip -r ../supplement.zip . && du -h ../supplement.zip # must be ≤ 50 MB (2026 cap)Watch for: license headers with names, pyproject.toml author fields, conda environment
exports embedding usernames, wandb/MLflow run URLs tied to an account, dataset paths
containing /home/<name>/, and model checkpoints whose training config JSON names a
cluster.
Show full SKILL.md (259 more words)Show less
README skeleton for the archive
The archive's README is the artifact's abstract. A shape that fits the five-minute reader:
# Supplementary code for submission #<OpenReview number>
## Claim → command map (small-scale modes; full-scale flags noted)
| Paper claim | Command | Runtime (laptop) |
|---|---|---|
| Fig. 2 (coverage vs n) | `make fig2-small` | ~2 min |
| Table 1 (SHD, 10 seeds) | `make table1-small` | ~4 min |
| Thm. 3 simulation | `make thm3-check` | ~1 min |
## Environment
- `pip install -r requirements.txt` (pinned versions; Python 3.11)
- No GPU required for small-scale modes.
## Expected outputs
- `expected/` holds reference CSVs; each command prints PASS/FAIL against them.
## Full-scale reproduction
- Flags, hardware used, and total runtime per experiment in `FULL_RUNS.md`.Everything above stays anonymous by construction — no names, no lab conventions in paths, no acknowledgement of infrastructure that identifies an institution.
Data that cannot ship
- Under the 50 MB cap, ship generators, not corpora: the script that produces the synthetic SCMs or the subsampled benchmark, plus checksums for the full data.
- For licensed or private datasets, include the loader, the preprocessing pipeline, the schema, and summary statistics, and state access terms in the README.
- Never bypass a data-use agreement to help reviewers; explain the constraint instead. A stated limitation is a clarity point, a violated license is misconduct under the AUAI code of conduct.
Licensing notes
- The review-time ZIP needs no license drama, but the post-acceptance release does: choose deliberately between permissive (MIT/BSD/Apache-2.0) and copyleft, and check employer policy before the camera-ready deadline forces a rushed default.
- Third-party code vendored into the archive keeps its own license; verify compatibility now, since reviewers occasionally notice a GPL file inside an "MIT" archive and read it as carelessness.
- Data licensing is separate from code licensing; a permissive code license does not launder a restricted dataset.
After acceptance
Convert the anonymous ZIP into a public artifact worth citing: a tagged repository, an archival DOI where your institution supports one, a license chosen deliberately, and the README rewritten from "reviewer instructions" to "user instructions." Link it from the camera-ready — post-acceptance is when the CFP's release encouragement costs you nothing and earns citations.
Output format
[Artifact scope] supplement ZIP / public release / both
[Five-minute path] <command a reviewer runs first, and its runtime>
[Claim coverage] <headline results reproducible / total>
[Anonymity scan] clean / leaks: <files>
[Size] <ZIP size vs current cap>
[Data strategy] shipped / generated / loader-plus-terms© 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 UAI-Skills/skills/uai-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Uai Artifact Evaluation 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 |
|---|---|---|---|---|---|---|
| Uai Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Extracting Windows Event Logs Artifactsmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Artifacts Buildernexu-io/open-design | 100k | — | ~347 | Automated safety check: Pass | Apache-2.0 | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT | |
| Mobisys Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | Automated safety check: Pass | MIT |
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Questions about Uai Artifact Evaluation
What does Uai Artifact Evaluation do?
A skill your agent uses when packaging code, data, samplers, solvers, and logs for a UAI submission's 50 MB supplementary ZIP or a public post-acceptance release, making probabilistic-inference…. Uai Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, data, samplers, solvers, and logs for a UAI submission's 50 MB supplementary ZIP or a public post-acceptance release, making probabilistic-inference claims independently runnable while keeping every file double-blind, given that UAI reviewers may open the archive but are not obliged to read it.
When should I use Uai Artifact Evaluation?
Uai Artifact Evaluation fits situations like: logs for a UAI submissions 50 MB supplementary ZIP; A public post-acceptance release; making probabilistic-inference claims independently runnable while keeping every file double-blind; given that UAI reviewers may open the archive but are not obliged to read it.
How do I install Uai Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-artifact-evaluation -a claude-code`. Or copy the skill folder (UAI-Skills/skills/uai-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/uai-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Uai Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-artifact-evaluation -a codex`. Or copy the skill folder (UAI-Skills/skills/uai-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/uai-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Uai Artifact Evaluation 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 uai-artifact-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/uai-artifact-evaluation, .gemini/skills/uai-artifact-evaluation, .github/skills/uai-artifact-evaluation and .opencode/skills/uai-artifact-evaluation in your project.
What does Uai Artifact Evaluation need to run?
Going by SKILL.md and its folder, Uai Artifact Evaluation needs the command-line tools its instructions call (git). Our summary lists: Python 3.
Does Uai Artifact Evaluation access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Uai Artifact Evaluation 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 Uai Artifact Evaluation use?
Uai Artifact Evaluation 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 Uai Artifact Evaluation use?
About 1.6k tokens (SKILL.md is roughly 6.4k 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 Uai Artifact Evaluation?
Skills that share tags, products or a category with Uai Artifact Evaluation: Extracting Windows Event Logs Artifacts (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Arize Evaluator (github/awesome-copilot, 40k stars), Artifacts Builder (nexu-io/open-design, 100k stars) and Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Uai Artifact Evaluation?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.