SKILLEMALL.ai

AC tesp

Enforce the Task Execution Signal Protocol for non-instant work so execution stays visible, staged, versioned, and auditable. Use when a task will take more than an immediate reply, spans multiple steps, needs async follow-through, involves cross-agent coordination, or requires active progress signaling, blocker escalation, queue hygiene, or result handoff. Trigger on requests about long tasks, execution tracking, task orchestration, status visibility, rollout audit, progress cadence, task boards, or reducing the need for the human to chase updates. 中文简介:用于非即时任务的任务执行信号协议。适用于长任务、多步骤任务、异步执行与多 agent 协作场景,要求快速接收确认、阶段进度广播、阻塞升级、任务看板与结果落位,并通过版本可见、数字进度、活跃板/归档板和低 token 巡检,避免人类反复追问状态。

ClawHub Agent Skills author: Wewehg v1.0.3 MIT-0 4 files body ≈ 511 tokens Open the sourceclawhub.ai analyzed 2 d ago

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

AnalyzerAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
62/100
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: 4. 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
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 21 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 511 tokens

    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 683: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 21 items
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This is a documentation-only task-progress protocol; its main risks are overuse, hardcoded local task-board paths, and provider preferences that users should adapt.
    LLM: benign (high) · VirusTotal: · 29 May 2026