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BF content-machina

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 13 files body ≈ 1 179 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Full-stack content creation persona for OpenClaw agents.

As a process F 43/100 · Will not run — References files that are not bundled: content-machina.png

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
B
89/100
safety, quality, tests
Safety 60%
95
Quality 40%
81
Run on models
none yet
Process rating
F
43/100
Will not run
References files that are not bundled: content-machina.png
Tools and files w 18
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-agent-memory-dump memory/content-machine-novaiok.md
    Agent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokens
    memory/content-machine-novaiok.md

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: content-machina.png

Process rating: all ten parameters 43/100

Will not run. References files that are not bundled: content-machina.png
  • 0Tools and files. 1 referenced file(s) missing: content-machina.png
  • 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
  • 30Running it twice. 2 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 52 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1179 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)
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 600: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 52 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (8 of 9)

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