SKILLEMALL.ai

BC tldraw-skill

Use when user requests diagrams, flowcharts, architecture charts, or visualizations. Also use proactively when explaining systems with 3+ components, complex data flows, or relationships that benefit from visual representation. Generates .tldr JSON files and exports to PNG/SVG locally using @kitschpatrol/tldraw-cli.

ClawHub Hermes author: Agents365.ai v1.1.0 MIT-0 4 files body ≈ 5 479 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 64/100 · Has gaps — weak spots: consistency, running it twice, progress reporting

IntegrationInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
C
64/100
Has gaps
Consistency w 8
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
  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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 317 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning body-long SKILL.md body ≈ 5479 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 64/100

  • 0Consistency. Frontmatter name (tldraw-skill) differs from the folder (tldraw-pro-skill)
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 9 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, git, python, node) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5479 tokens
  • 85Steps. 54 steps, 2 vague phrases
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 11 branches, has a failure section
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 top-level sections: this looks like several domains in one skill
  • high The skill tells the model to perform an irreversible action with no human approval

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)
  • +2Single-language instructions
  • +3Description length 317: enough signal without eating the budget
  • +4Structure: 29 headings
  • +3Step-by-step instructions: 54 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
This is a coherent diagram skill, but it silently self-updates from Git and may install a global npm tool without asking first.
LLM: suspicious (high) · VirusTotal: · 29 May 2026