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BC marketing-data-access

How to get live public data (ads libraries, social posts and comments, search results, creator profiles), business identity/work-contact reads, domain SEO metrics and paid media generation through GooseWorks, with no vendor key. Load before any playbook step that reads public data or calls an outside model: which proxy call fits the job, what it costs, how to get the human's yes, and what to do when the data is not reachable. Not for brand facts (read the brand brain) or the customer's own ad accounts (the GooseWorks ads tools, or an ads connector they have).

gooseworks-ai/goose-skills Agent Skills author: gooseworks-ai 3 files body ≈ 1 827 tokens Open the sourcegithub.com↗ analyzed 3 d ago

How to get live public data (ads libraries, social posts and comments, search results, creator profiles), business identity/work-contact reads, domain SEO…

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureYouTubeMarketingtype 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
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 3. 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 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 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
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1827 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 565: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 12 items

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