BC feeding-workflow
AI 团队、RAG、知识库天天吃编造数字？喂料专家用「五步喂料流程 + 三要素纪律」把散料变成真料：收料登记→溯源核实→原创化→入库→回执，凡数值/限值/条款必带 出处+适用条件+归口标准，官方源优先、查不到标'待核实'绝不编造。内置防编造喂料包模板、敏感扫描与分区入库规矩，一次配齐，让下游 AI 产出从此有真料可依。
AI 团队、RAG、知识库天天吃编造数字？喂料专家用「五步喂料流程 + 三要素纪律」把散料变成真料：收料登记→溯源核实→原创化→入库→回执，凡数值/限值/条款必带 出处+适用条件+归口标准，官方源优先、查不到标'待核实'绝不编造。内置防编造喂料包模板、敏感扫描与分区入库规矩，一次配齐，让下游 AI 产出从此有真料可依。
As a process C 53/100 · 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.
- 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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 8, column 17: description_en: Tired of AI teams, RAG and knowledge bases hallucinating figure… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-long-hermesdescription is 160 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 "display_name" - note
frontmatter-keyunknown frontmatter key "description_zh" - note
frontmatter-keyunknown frontmatter key "description_en" - note
frontmatter-keyunknown frontmatter key "agent_created"
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. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 419 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
- +4No input/output examples
- +2Single-language instructions
- +3Description length 160: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 20 items
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 54.