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AB latex-paper-conversion

This skill should be used when the user asks to convert an academic paper in LaTeX from one format (e.g., Springer, IPOL) to another format (e.g., MDPI, IEEE, Nature). It automates extraction, injection, fixing formatting, and compiling.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 999 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill should be used when the user asks to convert an academic paper in LaTeX from one format (e.g., Springer, IPOL) to another format (e.g., MDPI, IEEE…

As a process B 65/100 · Nearly there — weak spots: result and completion, failures and branches, running it twice

GeneratorLaTeXSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
B
65/100
Nearly there
Failures and branches w 10
0
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: latex-paper-conversion (sickn33/agentic-awesome-skills)

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"

    Process rating: all ten parameters 65/100

    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 999 tokens
    • 100Progress reporting. Reports progress

    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 237: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.