BD ai-llmops
AI 模型与资产全生命周期管理（LLMOps）实操手册——从"有模型在用"到"资产清清楚楚"：资产全景与台账（模型/Prompt/知识库/评估集/Agent 配置五类资产登记）、模型卡与注册（Model Card 字段规范、注册流程、元数据管理）、版本管理（模型/Prompt/知识库版本化、灰度发布、一键回滚）、上线下线管理（上线审批、退役下线、废弃处置与迁移）、漂移监控（数据漂移/概念漂移/模型漂移、监控指标与告警阈值）、成本治理（Token 成本核算、预算控制、路由分层、蒸馏与缓存降本）、治理制度与流程（角色职责、审批流、审计留痕）。附零依赖本地工具一键出盘点清单、模型卡模板、生命周期检查、漂移监控要点与成本治理清单。面向 AI 平台、工程、运维与财务负责人——与企业 AI 治理（管制度）互补，本技能管资产与工程。
AI 模型与资产全生命周期管理（LLMOps）实操手册——从"有模型在用"到"资产清清楚楚"：资产全景与台账（模型/Prompt/知识库/评估集/Agent 配置五类资产登记）、模型卡与注册（Model Card…
As a process D 46/100 · 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: 18. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 364 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 "display_name" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "title" - note
frontmatter-keyunknown frontmatter key "description_en"
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 549 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
- +2Single-language instructions
- +3Description length 364: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.