BD medxpert-offline-taskbox
AI 任务太烧钱？低配电脑也能跑大模型——白天派活，晚上回家让本地模型免费跑完，断网也不停。MedXpert 跨机任务箱：WorkBuddy×DSH（DeepSeek Harness）×本地 Ollama 三级协同，批量/重复/文档/敏感任务零云端消耗，复杂推理才上云端。L1-L5 分级路由已代码级实现（add 自动打标 + route 查询）、失败自动重试、同步包双机合并、防僵尸续跑，纯 Python 零依赖即下即用，旧电脑也能当执行节点。模型 API 涨价？跟它无关。省积分、断网续跑、离线任务队列、任务路由与成本控制都找它。
AI 任务太烧钱？低配电脑也能跑大模型——白天派活，晚上回家让本地模型免费跑完，断网也不停。MedXpert 跨机任务箱：WorkBuddy×DSH（DeepSeek Harness）×本地 Ollama 三级协同，批量/重复/文档/敏感任务零云端消耗，复杂推理才上云端。L1-L5 分级路由已代码级实现（add…
As a process D 48/100 · Unfinished process — 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.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 267 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "agent_created" - note
frontmatter-keyunknown frontmatter key "description_en"
Process rating: all ten parameters 48/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 100Steps. 107 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3104 tokens
- low 16 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
- -2localhost URLs: will not work for another user
- +2Single-language instructions
- +3Description length 267: enough signal without eating the budget
- +4Structure: 34 headings
- +3Step-by-step instructions: 107 items
- +4Has examples (3 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.