SKILLEMALL.ai

BF scholar-search

Search academic papers and authors via the Scholar API (scholar.x49.ai). Use for finding papers by topic, keyword, or natural-language query, discovering authors, getting search suggestions, looking up paper metadata, checking journal rankings (JCR/CCF/FQBJCR), or filtering by open access, year range, paper type, or venue. Covers all academic disciplines. Triggers when user mentions academic papers, scholarly articles, research papers, literature search, journal articles, author lookup, citation counts, open access, or wants to search scholarly databases.

ClawHub Agent Skills author: wahahaaaa123 v1.0.1 MIT-0 2 files body ≈ 2 169 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 39/100 · Will not run — References files that are not bundled: landing_page_url

IntegrationResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
94
Quality 40%
77
Run on models
none yet
Process rating
F
39/100
Will not run
References files that are not bundled: landing_page_url
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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. The text references files that are not there: add them or drop the references.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash
  • low Secrets in code secret-high-entropy-token SKILL.md:34
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    2. Built-in free key: `psk_…CO4`
    quoted

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: landing_page_url
  • note frontmatter-key unknown frontmatter key "effort"

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: landing_page_url
  • 0Tools and files. 1 referenced file(s) missing: landing_page_url
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (scholar-search) differs from the folder (scholar-search-x49)
  • 55Failures and branches. 1 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 38 steps
  • 100Execution cost. Instruction body is 2169 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 561: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
This is a coherent academic search skill, but it needs review because its credential handling could expose a generic local secret and it includes a reusable built-in API token.
LLM: suspicious (high) · VirusTotal: · 29 May 2026