SKILLEMALL.ai

AD link-library

Personal knowledge base that captures web content (articles, tweets/threads, videos, podcasts, images, PDFs) and makes it retrievable for future conversations and writing. Use when: (1) User shares a URL with ANY interest signal — asking to summarize, commenting positively, saying "有意思/不错/interesting/值得看/学到了", or requesting it be saved, (2) User asks to find previously saved content ("我之前存的那篇...", "find that article about..."), (3) User needs reference material for writing or discussion, (4) User shares Twitter/X, WeChat, YouTube, Bilibili, or any web link and engages with it. Interest signals that trigger save: "帮我总结一下", "这篇不错", "有意思", "记一下", "留着以后用", "这个观点很好", "学到了", "值得保存", "放进知识库", sharing link + any commentary or opinion, asking follow-up questions about link content. Do NOT require literal "save"/"bookmark" keywords.

ClawHub Agent Skills author: 不白 v1.0.0 MIT-0 8 files body ≈ 1 489 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceYouTubeMedia and videoWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
D
49/100
Unfinished process
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

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: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 49/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 100Steps. 38 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1489 tokens
    • low 11 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 834: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 38 items
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: clean
    This is a coherent link-saving knowledge-base skill, but users should understand that some shared links may be fetched and saved locally in full text.
    LLM: benign (high) · VirusTotal: · 29 May 2026