SKILLEMALL.ai

AC tech-doc-writer

This skill should be used when the user wants to write detailed technical learning documentation, study notes, or knowledge summaries for deep learning, machine learning, or any technical topic. Use when user says "技术文档写作", "写一篇...文档", "整理...笔记", "写一篇关于...的学习资料", "结合文章写一份文档", or requests comprehensive technical documentation with formulas, code examples, and comparisons. Produces structured, detailed markdown documents with mathematical formulas, code examples, comparison tables, and decision guides following a consistent high-quality format.

ClawHub Agent Skills author: hiqishen v1.3.4 MIT-0 4 files body ≈ 2 130 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorSoftware developmentWriting and documentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
52/100
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

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: 4. 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 52/100

    • 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
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 91 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2130 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
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 548: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 91 items
    • +4Has examples (13 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This is a Markdown-only technical documentation writing skill with disclosed research and document-checking steps that fit its purpose.
    LLM: benign (high) · VirusTotal: · 29 May 2026