SKILLEMALL.ai

BC thesis-reviewer

Use when the user wants to review, evaluate, or provide feedback on a master's or doctoral thesis (硕士/博士学位论文). Triggers on keywords like "论文评审", "学位论文", "thesis review", "审阅论文", "论文修改意见", "硕士论文", "博士论文", "毕业论文", "doctoral thesis", "PhD thesis". Supports all disciplines with discipline-specific review modules (life sciences, medicine, CS/AI, engineering, chemistry, physics, social sciences). Full-spectrum review: academic quality, writing quality, formatting, data analysis, and academic integrity. Adapts review standards based on degree level and discipline.

ClawHub Hermes author: Agents365.ai v3.0.0 MIT-0 14 files body ≈ 1 654 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerResearchInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
95
Quality 40%
81
Run on models
none yet
Process rating
C
56/100
Has gaps
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Write Edit Bash mcp__markitdown__convert_to_markdown

Files scanned: 14. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 564 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 56/100

  • 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
  • 60Consistency. The Hermes dialect needs category and tags
  • 70When it triggers. States when to use, but not when not to
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 95 steps
  • 100Execution cost. Instruction body is 1654 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -215 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 563: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 95 items
  • +4Has examples (5 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

External checks

ClawHub: suspicious
The thesis review workflow is mostly legitimate, but it silently self-updates from Git before each review, which can change installed skill instructions without clear user control.
LLM: suspicious (high) · VirusTotal: · 29 May 2026