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BC linuxdo-application

Craft high-pass-rate, de-AI'd Chinese applications (小作文) for Linux.do registration. Conducts an adaptive survey to learn the applicant's real background, then generates natural, rule-compliant plain text ready to paste. All user interactions in Chinese.

ClawHub Hermes author: clarezoe v1.0.2 MIT-0 5 files body ≈ 1 277 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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-long-hermes description is 253 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 1 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Consistency. The Hermes dialect needs category and tags
  • 70Failures and branches. 5 branches
  • 85Steps. 41 steps, 1 vague phrases
  • 100Execution cost. Instruction body is 1277 tokens
  • 100Progress reporting. Reports progress

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
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 253: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 41 items
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

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

External checks

ClawHub: suspicious
This is a markdown-only writing skill, but it is built to help users produce a platform registration application that looks self-written and less AI-generated despite the referenced platform rules discouraging AI-generated applications.
LLM: suspicious (high) · 9 Jun 2026