SKILLEMALL.ai

AB jobstead

Helps a job-seeker decide whether a role is actually worth their time, then tells their story for it — beautifully. Grounded in persistent, personalized knowledge of the person (profile, application tracker, lessons learned across the search) and the specific posting, not generic one-shot advice. Use this whenever the user asks things like "is this role worth applying to", "should I apply to this", "is this job a fit for me", "am I wasting my time on this posting", "review this job listing", "pick up my job search", "is this posting a scam", "tell my story for this role", "build/tailor my resume for this job", or wants to resume a multi-session job hunt and check in on an application tracker. Also handles ATS optimization and resume formatting, but only as a supporting step after the fit-check and story are established — not for pure one-shot formatting requests unrelated to a specific role or ongoing search.

ClawHub Agent Skills author: adibas03 v0.1.0 MIT-0 9 files body ≈ 3 396 tokens Open the sourceclawhub.ai analyzed 3 d ago

Helps a job-seeker decide whether a role is actually worth their time, then tells their story for it — beautifully.

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerLearningInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 52 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3396 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • medium 20 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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

    • +3Description length 922: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +4Description says when NOT to use the skill
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 52 items
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: suspicious
    The skill is a coherent job-search assistant, but it asks the agent to search broad persistent memory sources and to quietly add reusable lessons, which needs review before use.
    LLM: suspicious (high) · 7 Sept 2026