SKILLEMALL.ai

AC pre-engineering

PRE Engineering — Initialize a PRE (Plan-Review-Execute) multi-agent collaborative project. TRIGGER when: user wants to initialize a multi-agent collaborative project, mentions PRE system, PRE Engineering, wants to set up collaborative agents, wants to initialize project documents for agent collaboration, asks how to make multiple AI roles collaborate on a project, mentions Plan-Review-Execute, wants to use a collaboration log to drive multi-agent work, wants to create Planner/Executor/Reviewer guide documents, wants to start a PRE collaboration workflow, or expresses multi-agent collaboration intent without explicitly mentioning PRE.

ClawHub Agent Skills author: Zhang Zhanluo v1.0.0 MIT-0 8 files body ≈ 3 208 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions

AnalyzerAI 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%
88
Run on models
none yet
Process rating
C
64/100
Has gaps
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: 8. 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
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, git) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 35 steps
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3208 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

    • +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
    • +1No license
    • +2Single-language instructions
    • +3Description length 642: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 35 items
    • +4Has examples (12 code blocks)
    • +4Reference files are cited in the instructions (3 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This is a coherent PRE multi-agent workflow, but it asks users to run persistent agents that can edit code and mutate git history with limited per-action control.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026