SKILLEMALL.ai

BC pincushion

Turn stakeholder feedback into agent work packets. A human drops a visual pin on any live web page; your OpenClaw agent reads it via MCP (selector, screenshot, DOM snippet, thread, acceptance criteria), fixes it, and resolves it.

ClawHub Agent Skills author: Josh v1.0.0 MIT-0 3 files body ≈ 1 365 tokens Open the sourceclawhub.ai analyzed 2 d ago

Turn stakeholder feedback into agent work packets.

As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
62/100
Has gaps
Progress reporting w 2
0
When it triggers w 12
20
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: 3. 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 62/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 10 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1365 tokens

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 229: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
The skill has a legitimate feedback-to-agent purpose, but it asks users to enable cloud synchronization and embed a hosted page-inspection widget without enough deployment and data-flow safeguards.
LLM: suspicious (medium) · 8 Jul 2026