SKILLEMALL.ai

BD archmap

架构测绘 Agent。零参调用自动分流(无基线→full 全量初始化,有基线→lite 极简增量),full/lite 显式词强制兜底;项目路径+需求文本做增量影响面分析;+sync 同步变更并重生成全套报告;+diff 产出行级差异影响面与测试选择(diff_impact.json)并留痕变更历史(时间+修改内容,复盘前固定卡点)。实现精准开发、节约tokens。

ClawHub Agent Skills author: xu-jin-cs v0.1.0 MIT-0 27 files body ≈ 3 861 tokens Open the sourceclawhub.ai analyzed 2 d ago

架构测绘 Agent。零参调用自动分流(无基线→full 全量初始化,有基线→lite 极简增量),full/lite 显式词强制兜底;项目路径+需求文本做增量影响面分析;+sync 同步变更并重生成全套报告;+diff…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
D
43/100
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: 26. 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 "aliases"

Process rating: all ten parameters 43/100

  • 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
  • 30Running it twice. 2 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 90 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3861 tokens

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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 184: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 90 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: clean
This skill performs disclosed local architecture and impact analysis, writing reports into the target project's archmap folder without evidence of hidden exfiltration or business-code modification.
LLM: benign (medium) · VirusTotal: · 24 Aug 2026