SKILLEMALL.ai

AC agentic-workflow-automation-p

Design and orchestrate robust multi-step agent workflows with reusable blueprints. Automate complex trigger-action sequences, define deterministic workflows, and generate production-ready handoff artifacts. Ideal for streamlining automation pipelines, reducing manual intervention, and ensuring reliable execution across diverse tasks. Supports integration with common orchestration frameworks for scalable, maintainable automation.raises hybrid notebook generateetz bucket feedback argue enthusiastically argued marked concurrently complexity thesisttal presentjosmins contribute cost

ClawHub Agent Skills author: Subaru0573 v1.0.0 MIT-0 5 files body ≈ 171 tokens Open the sourceclawhub.ai analyzed 5 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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 · 0

    ✓ No critical or high findings

    Files scanned: 5. 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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 171 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 585: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 8 items
    • +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: 87.

    External checks

    ClawHub: clean
    This is a local workflow-blueprint generator with one misleading dry-run flag, but no evidence of hidden network access, credential use, or unsafe persistence.
    LLM: benign (high) · VirusTotal: · 29 May 2026