SKILLEMALL.ai

BD zh-knowledge-manager

中文 AI 增强知识管理。PREFIX 确定性分类 + hash/语义去重 + jieba 自动标签 + LLM 对话知识提取。

ClawHub Agent Skills author: RomeoSY v1.0.1 32 files body ≈ 429 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
97
Quality 40%
69
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token package-lock.json:25
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…dNm+w/2fIz…imP+QNz605/XnjF…qBg==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:113
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…Ufq+nJ5J/kzXjkfbr/1WY6…v72/wIx/jn7NoXfm/UPuJ…C6w==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:289
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…LYU+CX9r…iU1/AKbT…lBQ==",
    detector

Files scanned: 32. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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 (node) that frontmatter does not declare
  • 100Steps. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 429 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)
  • +3Description length 64: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This is a coherent knowledge-management skill with local writes and optional AI API use, but users should treat AI extraction and semantic deduplication as sending selected content to configured providers.
LLM: benign (high) · VirusTotal: benign · 28 May 2026