SKILLEMALL.ai

AB workstation

Control Varie Workstation sessions (Claude Code multi-session orchestration). Use when: (1) user wants to work on / start / resume a coding project, (2) checking session status, (3) sending commands to a session, (4) listing active sessions, (5) creating new sessions, (6) user replies to a plan approval or question notification, (7) user wants to stop/cancel/interrupt a session, (8) user wants a screenshot of a session or screen. Triggers on: work on, start, resume, sessions, workers, workstation, dispatch, project name, approve, reject, option, pick, yes, no, stop, cancel, interrupt, escape, kill, stuck, screenshot, show me, capture, what does it look like.

ClawHub Agent Skills author: masqueradeljb v1.0.1 MIT-0 2 files body ≈ 4 162 tokens Open the sourceclawhub.ai analyzed 26 h ago

Control Varie Workstation sessions (Claude Code multi-session orchestration).

As a process B 67/100 · Nearly there — weak spots: result and completion, consistency

ProcedureTelegramWhatsAppGitHubAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Consistency w 8
40
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 67/100

    • 0Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (workstation) differs from the folder (coding-agent-orchestrator)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4162 tokens
    • 85Steps. 32 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 3 branches, has a failure section
    • 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
    • low 15 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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 666: enough signal without eating the budget
    • +4Structure: 31 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (16 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    This skill is mostly aligned with controlling coding workstations, but it needs Review because it can act on live sessions and screenshots with broad triggers and inconsistent safety instructions.
    LLM: suspicious (high) · VirusTotal: · 11 Sept 2026