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CC figure-style

Correctness and legibility checklist for publication figures, plus a matplotlib sidecar. Load before plotting anything and call `apply_figure_style()` (role-mapped font ladder, outward ticks, frameless legends, 300-dpi saves, CJK-safe fonts). Covers data fidelity, label budgets, axis/colour/type rules, chart choice by data shape, composition, and a mandatory render-then-inspect QA pass (bbox collisions + per-panel visual crops). Helpers: focal_palette, bar_with_points, strip_with_median, end_of_line_labels, panel_letter, set_frame, panel_crops, save_panel_crops (QA crops go to .cache/, never into the output figures directory). Multi-panel assembly lives in figure-composer; whole-paper figure ordering in paper-narrative.

xuzhougeng/wisp-science Agent Skills author: xuzhougeng AGPL-3.0 2 files · 1 script body ≈ 3 309 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Correctness and legibility checklist for publication figures, plus a matplotlib sidecar.

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

TemplateSoftware developmentData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 3 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 56 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3309 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 729: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 56 items
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.