SKILLEMALL.ai

AB workorai

Use for WorkorAI talent marketplace requests. Candidate triggers: "найди мне работу", "ищу работу", "подбери вакансию", "find me a job", "I need work", "help me get hired". Employer triggers: hiring, posting jobs, finding/evaluating/comparing candidates, "who's the best fit", explaining why a candidate matches, recruiting, MCP setup. Covers 9 candidate.* tools (search/detail/applications/apply/invites/saved) and 19 employer.* tools: job lifecycle; candidate discovery with TIERED ranking (best/good/weak) + a white-box matchExplanation per candidate (fit score, skills PROVEN in interview, gaps, quotable rationale); per-candidate interview EVIDENCE (facts + Q&A) for your own comparative review; invitations; applicants review; MCP onboarding. The agent ranks, explains, and evaluates candidates on white-box data, not a black-box score.

ClawHub Claude Code author: workor v0.4.5 MIT-0 13 files body ≈ 2 605 tokens Open the sourceclawhub.ai analyzed 2 d ago

Use for WorkorAI talent marketplace requests.

As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerAI and agentsPeople and hiringtype 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
68/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    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: 13. 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 68/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 100Steps. 56 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 2605 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 842: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 56 items
    • +4Reference files are cited in the instructions (8 of 8)
    • +3All 1 scripts are documented
    • +1License stated

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

    External checks

    ClawHub: suspicious
    This WorkorAI skill matches its hiring-marketplace purpose, but it needs review because it can auto-activate broadly while handling reusable account keys and sensitive candidate/employer data.
    LLM: suspicious (high) · VirusTotal: · 9 Jul 2026