Claude-skill-registry artifact-guidelines

Guidelines for writing reports, organizing files, and generating code artifacts

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/artifact-guidelines" ~/.claude/skills/majiayu000-claude-skill-registry-artifact-guidelines && rm -rf "$T"
manifest: skills/data/artifact-guidelines/SKILL.md
source content

Report Writing and File Organization

This skill provides best practices for all subagents when generating written artifacts, code files, and figures.

Writing Guidelines

Use GitHub-flavored CommonMark markdown for all text outputs (reports, logs, documentation). Never use plain .txt files.

Write concisely:

  • Short paragraphs with complete sentences
  • Favor insight over exhaustiveness
  • Use lists sparingly, only when they genuinely clarify (e.g., model assumptions, validation criteria)
  • Avoid markdown overuse - minimal headers and bold, no excessive formatting

In reports:

  • Lead with key findings or conclusions
  • Support claims with evidence (plots, statistics, diagnostics)
  • Reference files with clear relative paths: "As shown in
    figures/washout_curves.png
    ..." or "
    washout_curves.png
    " if in same directory
  • Document what you tried, what worked, and what didn't

In logs:

  • Capture decisions and reasoning, not play-by-play execution
  • Record why you chose certain paths or skipped alternatives
  • Note failures and how you addressed them

Use scratchpad:

  • You should create local files to write a first draft, including thinking process
  • Rewrite it to form final output, then delete the temporary local files you created

Code Organization

One logical unit per file:

  • One model per .stan file
  • One analysis per .py script
  • Descriptive names:
    fit_hierarchical_model.py
    not
    model.py
  • Self-contained scripts that run independently

Stan only: All Bayesian models must use Stan via CmdStanPy. Do not use PyMC, NumPyro, Pyro, or other PPLs.

Keep it simple:

  • No deep nesting of directories unless natural grouping exists
  • Clean up exploratory scripts after consolidating insights into reports
  • Every file should have a clear purpose

Figure Organization

Use descriptive filenames:

  • group_washout_curves.png
    not
    fig1.png
  • prior_predictive_check.png
    not
    ppc.png

One figure per concept or question:

  • Avoid packing too many subplots (max 2x2 for comparisons)
  • Save at appropriate resolution (300 DPI for reports, 150 for exploratory)

File Minimalism

Generate fewer, better files:

  • Consolidate related content - one EDA report, not 10 partial analyses
  • Combine related visualizations into multi-panel figures when appropriate
  • Only create files that will be read by users or subsequent agents
  • Remove intermediate artifacts after they've served their purpose

Each file you create should justify its existence. Ask: will this be read? Does it convey unique information?