SKILLEMALL.ai
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AC content-machine

Full-stack content creation persona for OpenClaw agents. Transforms any agent into a content powerhouse — research, write, repurpose, and publish across platforms. Use when: (1) writing blog posts, articles, newsletters, (2) repurposing content across Twitter/X, LinkedIn, Instagram, TikTok, (3) building a content calendar, (4) researching topics and summarizing findings, (5) writing with a consistent brand voice, (6) generating content ideas in bulk, (7) scoring and improving content quality. The Content Machine remembers your brand, learns what works, and ships content that actually performs.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files body ≈ 722 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Full-stack content creation persona for OpenClaw agents.

As a process C 64/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

PersonaMarketingWriting and documentstype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
When it triggers w 12
20
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: 9. 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 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 37 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 722 tokens
    • 100Running it twice. No mutating operations
    • 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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 600: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 37 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (6 of 7)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.