SKILLEMALL.ai

AC agent-collaboration-protocol

Structured multi-agent collaboration for backend + frontend builds. Use when an orchestrator needs to coordinate a backend engineer and frontend engineer on the same feature. Triggered by multi-role build requests like "build a dashboard with an API and UI" or "create a full-stack feature" or any task requiring both backend (API, data, infra) and frontend (UI, templates, design) work.

ClawHub Agent Skills author: NightKnight64 v1.5.3 MIT-0 8 files · 1 script body ≈ 3 400 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions

GeneratorSoftware developmenttype 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
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
50
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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 60 steps
    • 100Failures and branches. 6 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3400 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)
    • +3Output format is not stated: the model decides each time
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 387: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 60 items
    • +4Has examples (10 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 1 scripts are documented

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

    External checks

    ClawHub: suspicious
    This skill is a mostly transparent multi-agent build workflow, but it also directs agents through live deployment changes and service restarts without a clear approval gate.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026