SKILLEMALL.ai
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BB technical-writer

Write, review, and edit developer documentation for SDKs, libraries, and frameworks, including sites such as agent-browser.dev and portless.sh. Use for getting-started guides, tutorials, API references, conceptual explanations, integration guides, migration guides, troubleshooting, and code examples.

The skillemall take

Promises to write and edit documentation for SDKs, libraries, and frameworks—guides, API references, code examples. Four files in the folder, no scripts. Grade B: quality 84%, process 70%. No critical findings, no medium-level ones either. Linter clean.

Runs on all major platforms. Body tokens 3838—standard for documentation work. Safety score maxed out. Install if you need help documenting APIs and writing guides: the skill passed checks without issues, though the process score lags. No reason to wait.

vercel-labs/json-render Agent Skills author: vercel-labs 4 files body ≈ 3 838 tokens Open the sourcegithub.com↗ analyzed 9 d ago

Write, review, and edit developer documentation for SDKs, libraries, and frameworks, including sites such as agent-browser.dev and portless.sh. Use for…

As a process B 70/100 · Nearly there — weak spots: result and completion, progress reporting

AnalyzerWriting and documentsSoftware developmentAI and agentstype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
70/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
50
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: agent-browser

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 70/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 27 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3838 tokens
    • 100Running it twice. Mutating operations check current state
    • low 12 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 301: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 27 items
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.