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AC scholar-report

Generate AI-powered academic research reports via the Scholar API (scholar.x49.ai). Creates comprehensive literature review reports with inline citations, paper evidence, and downloadable Markdown. Use when the user wants a research report, literature review, academic survey, state-of-the-art summary, or systematic overview of a topic. Triggers when the user mentions generating a report, reviewing literature, surveying a field, summarizing research trends, or asks complex questions that benefit from a synthesized academic report.

ClawHub Agent Skills author: wahahaaaa123 v1.0.1 MIT-0 2 files body ≈ 2 480 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationData and analyticsResearchtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
94
Quality 40%
83
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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

    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:35
      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

    • note frontmatter-key unknown frontmatter key "effort"

    Process rating: all ten parameters 60/100

    • 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. 11 mutating operations with no state check
    • 40Consistency. Frontmatter name (scholar-report) differs from the folder (scholar-report-x49)
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 40 steps, 2 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 2480 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 535: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 40 items
    • +4Has examples (9 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    This research-report skill does what it says, but it automatically sends queries to a third-party API and includes a shared built-in bearer token.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026