BC multi-agent-pro
支持多Agent流水线编排（采集→分析→报告），基于DAG调度实现跨技能状态共享、错误重断点续传、执行报告生成、HTML甘特图可视化、人工审批节点（含超时策略）、历史执行对比、硬件自适应参数和版本更新提醒。v5.3新增官方流水线模板库（4类预置模板+依赖探测）、任务级重试策略（节点级retry块+退避+降级链）、节点类型归组（7→4类认知归组）、错误恢复命令合并（recover统一入口）、条件表达式增强（re_safe+字符串函数）。AI即编排器，脚本提供基础设施。
支持多Agent流水线编排（采集→分析→报告），基于DAG调度实现跨技能状态共享、错误重断点续传、执行报告生成、HTML甘特图可视化、人工审批节点（含超时策略）、历史执行对比、硬件自适应参数和版本更新提醒。v5.3新增官方流水线模板库（4类预置模板+依赖探测）、任务级重试策略（节点级retry块+退避+降级链）、节点…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 32. 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") - warning
body-longSKILL.md body ≈ 5898 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Execution cost. Instruction body is 5898 tokens
- 100Steps. 60 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 11 top-level sections: this looks like several domains in one skill
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
- -237 emoji in the instructions: noise for the model
- -37 of 13 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 235: enough signal without eating the budget
- +4Structure: 50 headings
- +3Step-by-step instructions: 60 items
- +4Has examples (33 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.