SKILLEMALL.ai

BD value-alignment

价值对齐:用 有用/无害/诚实 三原则对生成内容做规则化对齐评估,拦截越界与有害输出、 标记过度承诺与无依据断言。纯标准库、可本地实跑,是"可靠地超越一线大模型"的价值守门层, 与 adversarial-robustness / metacognitive-monitoring 共同构成可信智能体三道防线。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 5 files body ≈ 425 tokens Open the sourceclawhub.ai analyzed 2 d ago

价值对齐:用 有用/无害/诚实 三原则对生成内容做规则化对齐评估,拦截越界与有害输出、 标记过度承诺与无依据断言。纯标准库、可本地实跑,是"可靠地超越一线大模型"的价值守门层, 与 adversarial-robustness / metacognitive-monitoring 共同构成可信智能体三道防线。

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

ProcedureInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
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 · 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-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "visibility"

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 (bash) that frontmatter does not declare
  • 100Steps. 12 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 425 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 155: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 12 items
  • +4Has examples (2 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
The skill is a local alignment checker, but its bundled learning system can persist free-form notes and preferences and encourages future rule changes without clear consent, retention, or review controls.
LLM: suspicious (high) · 14 Aug 2026