AD distill-skill-builder
知识技能型 Skill 创建流水线。将官方文档蒸馏为结构化知识技能的完整方法论——从源分析、爬取/蒸馏、内容结构化、评估打分到 A 级达成。当用户要求创建 skill、蒸馏知识技能、整理 skill、提升 skill 质量、评估 skill 分数时触发。
As a process D 44/100 · 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 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: 13. 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") - note
frontmatter-keyunknown frontmatter key "trigger" - note
frontmatter-keyunknown frontmatter key "hermes"
Process rating: all ten parameters 44/100
- 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, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 4617 tokens
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 27 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (24 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)
- +3Output format is not stated: the model decides each time
- -213 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 127: enough signal without eating the budget
- +4Structure: 83 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (51 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.
External checks
ClawHub: suspicious
The skill is mostly a skill-building guide, but it includes persistent cross-agent deployment steps and advice to modify its evaluator, so users should review it before installing.
LLM: suspicious (high) · VirusTotal: · 29 May 2026