Wp Performance Review
elvismdev/claude-wordpress-skills
WordPress performance code review and optimization analysis.
A skill your agent uses when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery…
Install Uai Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-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/UAI-Skills/skills/uai-experiments .claude/skills/uai-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 "uai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-experiments into .claude/skills/uai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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/UAI-Skills/skills/uai-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 uai-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-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/UAI-Skills/skills/uai-experiments .agents/skills/uai-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 "uai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-experiments into .agents/skills/uai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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 uai-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-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/UAI-Skills/skills/uai-experiments .cursor/skills/uai-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 "uai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-experiments into .cursor/skills/uai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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 UAI-Skills/skills/uai-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 uai-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills uai-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/UAI-Skills/skills/uai-experiments .gemini/skills/uai-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 "uai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-experiments into .gemini/skills/uai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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 uai-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 uai-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/UAI-Skills/skills/uai-experiments .github/skills/uai-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 "uai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-experiments into .github/skills/uai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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 uai-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 uai-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/UAI-Skills/skills/uai-experiments .opencode/skills/uai-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 "uai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/UAI-Skills/skills/uai-experiments into .opencode/skills/uai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "uai-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
uai-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 669 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 UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery…
- Works in 3 steps: Exact-truth regime: small models where… → Stress regime: the same generators with… → Real-data regime: demonstrates…
- Auditing UAI experiments where inference quality is the endpoint
- SKILL.md covers Metrics follow the…, The synthetic-to-real ladder, Stochasticity is part of the… and Baseline selection that…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Uai Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery metrics for causal and graphical models, seeded repeated runs, baselines from both sampling and optimization families, and mapping each claim to its evidence.
Its SKILL.md is about 1.7k 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 Business, Finance & HR, covering Performance reviews. 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 UAI experiments where inference quality is the endpoint
- Covering calibration and coverage measurement
- Posterior diagnostics
- Structure-recovery metrics for causal and graphical models
Example prompts
- “/uai-experiments”
Requirements
- Python 3
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Exact-truth regime: small models where exact inference or the true SCM is
- Stress regime: the same generators with an assumption deliberately broken
- Real-data regime: demonstrates relevance, evaluated by proxies (held-out NLL,
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 (its code samples are python).
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
Uai Experiments loads about 1.7k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 669 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). 669 words, ~1,685 tokens.
Download SKILL.mdSave it as
.claude/skills/uai-experiments/SKILL.md (or your agent's skills folder).- name
- uai-experiments
- description
- Use when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery metrics for causal and graphical models, seeded repeated runs, baselines from both sampling and optimization families, and mapping each claim to its evidence.
UAI Experiments
Use this while the empirical design is still changeable. At UAI the object under test is usually an inference procedure — a posterior, a graph, an interval, a decision policy — so the experimental question is rarely "is accuracy higher?" and usually "is the uncertainty right, and at what cost?". Reviewers score whether claims are backed up convincingly; design the study so each claim has a designated exhibit.
Metrics follow the probabilistic object
| Claimed object | Primary metrics | Supporting diagnostics |
|---|---|---|
| Posterior approximation | Wasserstein/KL to gold-standard posterior on tractable cases | R-hat, ESS, trace plots; ELBO with restarts |
| Predictive uncertainty | NLL, CRPS, empirical coverage vs nominal | Reliability diagrams; ECE with stated binning |
| Conformal / interval methods | Coverage at each α, interval width | Conditional coverage slices, not just marginal |
| Causal structure | SHD, SID, edge precision/recall vs ground truth | Performance vs sample size; sensitivity to faithfulness violations |
| Treatment effects | Bias/RMSE on ATE/CATE with known ground truth | Overlap diagnostics; propensity calibration |
| Decision policies | Regret, expected utility under the stated prior | Robustness under prior misspecification |
The recurring UAI failure is a proxy mismatch: claiming better uncertainty while measuring only accuracy, or claiming a better posterior while reporting only downstream prediction. Pick the metric that measures the claimed object directly.
The synthetic-to-real ladder
Because ground-truth posteriors and ground-truth graphs exist only where you construct them, strong UAI papers climb a ladder:
- Exact-truth regime: small models where exact inference or the true SCM is available — the only place "closer to the truth" is literally checkable.
- Stress regime: the same generators with an assumption deliberately broken (unfaithful distributions, heavy-tailed noise, hidden confounding) — this is where your limitations section gets its numbers.
- Real-data regime: demonstrates relevance, evaluated by proxies (held-out NLL, stability, downstream decisions) with the proxy status stated plainly.
A paper living only on rung 3 cannot back an inference-quality claim; one living only on rung 1 will be asked why anyone should care. Budget experiments across all three.
Stochasticity is part of the result
- Repeat over seeds and report dispersion (mean ± sd, or paired comparisons when methods share data draws); single-run tables at an uncertainty venue are self-refuting.
- Distinguish the two randomness sources — data generation and algorithm internals — and vary them separately when the claim depends on one of them.
- Fix the comparison protocol before running: same data splits, same compute budget or an explicit cost axis, same tuning effort for baselines as for the proposed method.
# Paired, seeded comparison harness: every method sees identical data draws
import numpy as np
def run_grid(methods: dict, make_data, seeds=range(10)):
rows = []
for s in seeds:
data = make_data(rng=np.random.default_rng(s)) # shared draw per seed
for name, fit in methods.items():
post = fit(data, seed=s)
rows.append({"seed": s, "method": name,
"coverage@90": post.coverage(0.90),
"nll": post.nll(data.test),
"ess_min": post.min_ess()})
return rows # aggregate as mean ± sd; report per-seed table in the appendixShow full SKILL.md (269 more words)Show less
Baseline selection that survives this reviewer pool
- Include the cheap classical baseline (exact inference where feasible, a well-tuned Laplace approximation, the PC algorithm, plain split conformal): UAI reviewers use these as sanity anchors, and their absence reads as fear.
- Include the strongest recent neighbor from UAI/AISTATS/NeurIPS, tuned in good faith with its search grid disclosed.
- When your method has a compute knob (chains, particles, restarts), plot the quality-versus-cost curve rather than a single operating point chosen post hoc.
Compute fairness, concretely
- Match tuning budgets: if the proposed method saw 50 configurations, baselines see 50, drawn from grids their authors would endorse.
- Match convergence criteria: comparing your converged sampler against a baseline's fixed-iteration run measures patience, not methods.
- When budgets cannot match (an exact baseline that only scales to n=100), report it at its feasible scale and mark the cell honestly rather than dropping the method.
- State who tuned what: "baseline hyperparameters from the original paper" and "tuned by us on the same validation split" are different evidentiary claims.
Reporting block, standardized
Give every experiment family the same reporting block in the appendix, so reviewers can audit uniformly and you can spot your own gaps:
EXPERIMENT <id> — backs claim: <paper sentence, quoted>
data: <generator or dataset+version, splits, preprocessing>
methods: <proposed + baselines, tuning grids, selection rule>
randomness: <seeds, what varies per seed: data draw / init / both>
compute: <hardware, wall-clock per method>
metrics: <primary + diagnostics, with definitions or citations>
result: <table/figure reference; dispersion form (sd / CI / paired)>
caveats: <regimes where the result did not hold>The caveats line is not decoration. At this venue an experiment section that admits
where the method loses reads as calibrated; one that never loses reads as curated.
Ablations for mechanisms, not rituals
Design each ablation to isolate the component your theory says matters: remove the coupling, swap the score function, freeze the calibration step. An ablation grid nobody can interpret is appendix filler; a single ablation matching a theorem's prediction is evidence.
Output format
[Claim → exhibit map] <each headline claim with its table/figure/diagnostic>
[Ladder coverage] exact-truth / stress / real — which rungs are missing
[Uncertainty of results] seeds, dispersion, pairing — adequate?
[Baseline audit] classical anchor present? strongest neighbor tuned fairly?
[Proxy mismatches] <claims measured by the wrong metric>© 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-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Uai 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 |
|---|---|---|---|---|---|---|
| Uai Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Wp Performance Reviewelvismdev/claude-wordpress-skills | 235 | 1 repos | ~4.5k | Automated safety check: Pass | MIT | |
| Align Humanagentscope-ai/OpenJudge | 870 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Run Mv Hoi Reconstructionnvidia-isaac/video_to_data | 856 | — | ~1.5k | Automated safety check: Pass | Custom licence | |
| Company Analysiszhu1090093659/dsh-trading | 234 | — | ~4.2k | Automated safety check: Pass | Custom licence | |
| Windbg Diagnostic Methodmicrosoft/win-dev-skills | 465 | — | ~1.9k | Automated safety check: Pass | MIT |
Similar skills
Align Human
agentscope-ai/OpenJudge
A skill your agent uses when the user has a judge/grader and human-labeled data, and wants to measure how well the judge agrees with humans, detect systematic biases, determine whether automatic…
Run Mv Hoi Reconstruction
nvidia-isaac/video_to_data
Run and validate the repository-local multi-view camera calibration and human-object reconstruction pipelines.
Company Analysis
zhu1090093659/dsh-trading
A skill your agent uses when the user wants to analyze a listed company, stock, business, or investment target; challenge or revise an existing company report; compare A/H or primary-listing/ADR…
- Official
Windbg Diagnostic Method
microsoft/win-dev-skills
Use with every WinDbg plugin investigation to apply evidence-first reasoning, confidence calibration, contrarian review, structured reporting, and deterministic validation.
AI Index
mizchi/skills
Method and tooling for measuring how AI-generated a piece of prose reads, in Japanese or English.
More from brycewang-stanford/Awesome-Journal-Skills
All 2,387 skills in this repoAaag Data Analysis
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
Aaag Literature Positioning
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
Aaag Rebuttal
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
Aaag Research Design
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
Aaag Review Process
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
Aaag Submission
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Related
Categories
Questions about Uai Experiments
What does Uai Experiments do?
A skill your agent uses when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery…. Uai Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing UAI experiments where inference quality is the endpoint, covering calibration and coverage measurement, posterior diagnostics, structure-recovery metrics for causal and graphical models, seeded repeated runs, baselines from both sampling and optimization families, and mapping each claim to its evidence.
When should I use Uai Experiments?
Uai Experiments fits situations like: auditing UAI experiments where inference quality is the endpoint; covering calibration and coverage measurement; posterior diagnostics; structure-recovery metrics for causal and graphical models.
How do I install Uai Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-experiments -a claude-code`. Or copy the skill folder (UAI-Skills/skills/uai-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/uai-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Uai Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill uai-experiments -a codex`. Or copy the skill folder (UAI-Skills/skills/uai-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/uai-experiments in your project. Codex loads it when a task matches its description.
Can I use Uai 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 uai-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/uai-experiments, .gemini/skills/uai-experiments, .github/skills/uai-experiments and .opencode/skills/uai-experiments in your project.
What does Uai Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Uai Experiments is instructions for the agent only. Our summary lists: Python 3.
Does Uai 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 Uai 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 Uai Experiments use?
Uai 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 Uai Experiments use?
About 1.7k tokens (SKILL.md is roughly 6.7k 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 Experiments?
Skills that share tags, products or a category with Uai Experiments: Wp Performance Review (elvismdev/claude-wordpress-skills, 235 stars), Align Human (agentscope-ai/OpenJudge, 870 stars), Run Mv Hoi Reconstruction (nvidia-isaac/video_to_data, 856 stars) and Company Analysis (zhu1090093659/dsh-trading, 234 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Uai Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.