BC knowledge-engineering
工业级RAG切片工具「可落地、可量化、可优化」,将RAG知识库长文档拆解为语义完整、检索就绪的原子化知识切片，内置多层质量门禁（校验→审计→检索可达性评估），确保切片可用性与RAG检索命中率。
工业级RAG切片工具「可落地、可量化、可优化」,将RAG知识库长文档拆解为语义完整、检索就绪的原子化知识切片，内置多层质量门禁（校验→审计→检索可达性评估），确保切片可用性与RAG检索命中率。
As a process C 58/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice
ProcedureAI and agentsCustomer supportWriting and documentstype 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.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 10. 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 ≈ 5090 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "keywords" - note
edit-residuethe text marks something as outdated (lines 47, 90, 192, 278, 327): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 58/100
- 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
- 30Running it twice. 9 mutating operations with no state check
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5090 tokens
- 100Steps. 65 steps
- 100Consistency. Name and required fields are in place
- low 18 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (11 tags): a typed call is more reliable
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)
- +3Description length 96: 120–800 characters recommended
- -225 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +4Structure: 36 headings
- +3Step-by-step instructions: 65 items
- +3Output format is stated explicitly
- +4Has examples (4 code blocks)
- +3All 5 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.
External checks
ClawHub: suspicious
This is a coherent RAG document-slicing skill, but it needs Review because it can automatically install packages and rewrite or overwrite generated knowledge-base files without strong consent or rollback safeguards.
LLM: suspicious (high) · 15 Aug 2026