SKILLEMALL.ai

AD ops-dashboard

Gather operational signals (disk usage, git status, recent commits, and resources) so you can answer "How is the Clawdy infrastructure doing?" without manually running multiple checks.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 511 tokens Open the sourcegithub.com analyzed 2 d ago

Gather operational signals (disk usage, git status, recent commits, and resources) so you can answer "How is the Clawdy infrastructure doing?" without…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
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-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 49/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 mutating operations with no state check
  • 60Tools and files. Uses tools (git, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 511 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 184: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.