AC parallel-responder
并行回复助手 - 让 AI 回复不再等待。支持任务分类、时间预估、并行执行、进度汇报。 简单任务直接回复，中等任务执行 + 汇报，复杂任务启动子 agent 并行处理。 Parallel Responder - Zero-wait AI responses. Task classification, time estimation, parallel execution, progress reporting.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
ReferenceAI and agentsInfrastructureData and analyticstype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 19 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 858 tokens
- 100Running it twice. No mutating operations
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 207: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 19 items
- +4Has examples (11 code blocks)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.
External checks
ClawHub: suspicious
The skill is not malicious, but it encourages broad automated task handling, child-agent workflows, installs, and file saves without clear permission boundaries.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026