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AB make-content

Writes marketing content: articles, single posts, newsletters, scripts and repurposed cuts, each with an edit note and a claims check. Use when a marketer says "write a blog post / case study", "write a LinkedIn or X post / thread / X article", "carousel copy", "draft this week's newsletter", "turn this webinar into posts" or "repurpose this", "write a TikTok or YouTube script", "refresh this old post", or "write the page from this SEO brief". Words only; nothing is invented or published. Not for a dated posting calendar — use run-organic-social; not ad copy (make-static-ads) or promo emails (email-lifecycle). Press releases, landing and product pages, and sales decks are not covered.

gooseworks-ai/goose-skills Agent Skills author: gooseworks-ai 8 files body ≈ 3 776 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Writes marketing content: articles, single posts, newsletters, scripts and repurposed cuts, each with an edit note and a claims check.

As a process B 76/100 · Nearly there — weak spots: running it twice

GeneratorYouTubeWriting and documentsMarketingtype 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
B
76/100
Nearly there
Running it twice w 4
30
When it triggers w 12
50
Failures and branches w 10
50
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: goose-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: 7. 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 76/100

    • 30Running it twice. 11 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 90 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3776 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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

    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 693: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 90 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (4 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.