SKILLEMALL.ai
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AB Studying

Plans study schedules and runs study sessions with retrieval practice, spaced review, and exam countdown protocols. Use when a student prepares for an exam, midterm, final, or certification, asks how to study, revise, memorize, or cram, says they studied hard but forgot everything, failed or blanked on a practice test, cannot focus or keeps procrastinating, or is juggling several courses before test week. Covers technique choice, weekly planning, note-taking, problem practice, essay prep, and test-day strategy, and persists what works for this student across sessions. Not for producing flashcard decks or practice tests themselves.

ClawHub Agent Skills author: Iván v1.0.4 MIT-0 15 files body ≈ 3 851 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, running it twice

AnalyzerLearningWriting and documentsInfrastructuretype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
When it triggers w 12
50
the three weakest of ten parameters · all ten

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 · 0

    ✓ No critical or high findings

    Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "changelog"
    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 71/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 65Failures and branches. 3 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 50 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3851 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 14 top-level sections: this looks like several domains in one skill

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 638: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 50 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

    External checks

    ClawHub: clean
    This is a study-coaching skill with disclosed, narrow local preference storage; it does not show hidden, destructive, or unrelated behavior.
    LLM: benign (high) · VirusTotal: · 26 Jul 2026