SKILLEMALL.ai

CC html-mark

Drop coral-gradient pins on any HTML page, write feedback in an inline glass note popup, copy out as Markdown / Plain / JSON — or a For-AI format (unique CSS selector + HTML snapshot per pin) built to paste into Claude Code for one-pass fixes. Pins anchor to their elements and persist in localStorage. Glass-morphism aesthetic, keyboard-friendly, self-contained single file.

ClawHub Hermes author: Maxen v1.2.0 MIT-0 10 files · 2 scripts body ≈ 2 592 tokens Open the sourceclawhub.ai↗ analyzed 8 d ago

Drop coral-gradient pins on any HTML page, write feedback in an inline glass note popup, copy out as Markdown / Plain / JSON — or a For-AI format (unique CSS…

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorGitHubPersonal productivityDesigntype and topics are labelled automatically from the skill text
JSON
Technical rating
C
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 375 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "title"
  • note frontmatter-key unknown frontmatter key "type"
  • note frontmatter-key unknown frontmatter key "invocation"
  • note frontmatter-key unknown frontmatter key "difficulty"
  • note frontmatter-key unknown frontmatter key "claude_version"
  • note frontmatter-key unknown frontmatter key "permissions"
  • note frontmatter-key unknown frontmatter key "examples"
  • note edit-residue the text marks something as outdated (lines 142): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 59/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 44 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2592 tokens
  • 100Running it twice. Mutating operations check current state
  • low The response is described with custom markup (4 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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 375: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (6 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
This annotation skill appears legitimate, but it can be injected into arbitrary live sites and stores or exports captured page content without enough privacy guardrails.
LLM: suspicious (high) · VirusTotal: · 16 Sept 2026