SKILLEMALL.ai

BC academic-figures

Publication-ready scientific figures from one command: 21 chart types (bar, scatter, heatmap, forest plot, Kaplan-Meier, ROC, violin, composite, PRISMA 2020 flow, funnel, Bland-Altman, PCA, venn, clustered heatmap), 9 themes incl. colorblind-safe Okabe-Ito and NEJM/Lancet/Science journal palettes, 9 journal submission presets, reviewer-style --annotate arrows, Euler venn, legend control, PDF text-overlap + font-size gates, 600dpi PNG/SVG/PDF/TIFF/EPS. 100% local — data never leaves your machine.

ClawHub Agent Skills author: docsor1212 v2.5.1 MIT-0 52 files body ≈ 11 293 tokens Open the sourceclawhub.ai analyzed 7 h ago

Publication-ready scientific figures from one command: 21 chart types (bar, scatter, heatmap, forest plot, Kaplan-Meier, ROC, violin, composite, PRISMA 2020…

As a process C 57/100 · Has gaps — weak spots: inputs and preconditions, execution cost, running it twice

AnalyzerExcelPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Execution cost w 6
40
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 52. 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")
  • warning body-long SKILL.md body ≈ 11293 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 428): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 57/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 11 mutating operations with no state check
  • 40Execution cost. Instruction body is 11293 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 111 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 28 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 tags): a typed call is more reliable

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)
  • -215 emoji in the instructions: noise for the model
  • -32 of 8 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 500: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 111 items
  • +3Output format is stated explicitly
  • +4Has examples (15 code blocks)
  • +4Reference files are cited in the instructions (5 of 8)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
This skill is a local academic chart generator with ordinary file output and setup helpers; its main risk is that users should notice the local-vs-cloud privacy boundary and dependency setup details.
LLM: benign (high) · VirusTotal: · 13 Sept 2026