SKILLEMALL.ai

AC dataify-agent-onboarding

Set up and verify a first Dataify workflow, then route the user to MCP, local skills, or REST without losing their original task. Use for first-time setup, installation, authentication, or choosing an integration path. Do not use for an already-configured search or scraping request.

ClawHub Agent Skills author: dataify-server v1.1.1 MIT-0 12 files body ≈ 1 020 tokens Open the sourceclawhub.ai analyzed 2 d ago

Set up and verify a first Dataify workflow, then route the user to MCP, local skills, or REST without losing their original task.

As a process C 64/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting

AnalyzerAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
89
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-password-literal scripts/task_runtime.py:38
      Hard-coded password / key literal (may be an example)
      api_key = api_key[7:].strip()

    Files scanned: 12. 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 64/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 18 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1020 tokens
    • 100Running it twice. No mutating operations
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • -37 of 8 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 283: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    The skill is mostly a Dataify onboarding helper, but it bundles broader scraping/reporting workflows and handles API tokens in ways users should review before installing.
    LLM: suspicious (high) · 7 Sept 2026