SKILLEMALL.ai

CB jobnet-search

Make sure to use this skill whenever the user mentions anything related to Danish job searching, job listings, job vacancies, employment opportunities in Denmark, or the Danish government job portal — even if they don't mention jobnet.dk explicitly. Also invoke this skill for questions about specific job titles, occupations, employers, or regions in a Danish employment context. This skill covers the official Danish public job portal operated by STAR (Styrelsen for Arbejdsmarked og Rekruttering). Trigger phrases include: danish jobs, danish job search, jobnet, jobnet.dk, find job denmark, danish employment, job i danmark, job på jobnet, offentlige job, stillinger i det offentlige, public sector jobs denmark, government jobs denmark, STAR jobs, job ledige stillinger, ledig stilling, søg job, job opslag, job vacancy denmark, stillingopslag, jobopslag, sygepleje job, ingeniør job, lærer job, pædagog job, it-job denmark, jobs in copenhagen, jobs in aarhus, jobs in odense, deltidsjob, fuldtidsjob, fastansættelse, tidsbegrænset ansættelse, fleksjob, sygeplejerske job, social worker job denmark, occupation search denmark, esco occupation, job deadline, ansøgningsfrist, søg efter job, full time job denmark, part time job denmark.

MadsLorentzen/ai-job-search Agent Skills author: MadsLorentzen MIT 22 files · 18 scripts body ≈ 1 646 tokens Open the sourcegithub.com↗ analyzed 35 h ago

Make sure to use this skill whenever the user mentions anything related to Danish job searching, job listings, job vacancies, employment opportunities in…

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, failures and branches, progress reporting

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
C
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
58
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
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: 22. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1241 chars, limit 1024
  • note frontmatter-key unknown frontmatter key "enabled"

Process rating: all ten parameters 68/100

  • 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
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 34 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1646 tokens
  • 100Running it twice. No mutating operations
  • 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)
  • +3Description length 1240: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 34 items
  • +3Output format is stated explicitly
  • +4Has examples (11 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.